Nathalie Colbach
email: nathalie.colbach@inrae.fr

Nicolas Munier-Jolain

François Dugué

Antoine Gardarin

Florence Strbik

Delphine Moreau

The response of weed and crop species to shading. How to predict their morphology and 1 plasticity from species traits and ecological indexes? 2 3

Keywords: Functional trait, comparative ecology, morphological plasticity, ecophysiology, FLORSYS, 50 photosynthetically active radiation PAR, plant architecture 51 52 53 1

Highlights 21 -33 crop and 25 weed species were studied in various light availability conditions 22 -Potential plant morphology and shading response were measured on individual plants 23 -Ecophysiological parameters were linked to easily-measured species traits 24 -Ecological indicators of habitat preference (Ellenberg) were linked to parameters 25 -Shade response differed for legume vs non-legume, weed vs crop, C3 vs C4 species 26 Abstract 27

To assess the competitive ability of plant species, ecologists describe many species from contrasting 28 habitats with traits that are proxies of ecophysiological functions whereas agronomists describe few 29 species from similar habitats with process-based parameters. Here, we combined both approaches and 30 compared many contrasting crop and weed species of temperate European arable crops in terms of 31 competition for light, to understand weed response to shading by crop canopies and to choose light-32 competitive crop species and varieties. We (1) measured species parameters that drive light-33 competition processes in 26 crop and 35 weed species of temperate European arable cropping systems, 34 *Manuscript (without tracks) Click here to view linked References

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(2) related the parameter values to species features that are easier to measure or available in databases. 35

Early plant-growth parameters (relative growth rate RGR, initial leaf area) were measured in optimal 36 light and nutrient conditions in a greenhouse with automatic non-destructive measurements. Potential 37 plant morphology in unshaded conditions (specific leaf area SLA, leaf biomass ratio LBR, plant height 38 and width per unit biomass HM and WM, vertical leaf distribution) was measured in garden plots in 39 optimal light and nutrient conditions and harvested at 4-5 stages. Shading response was measured by 40 comparing potential morphology to that of plants grown under shading nets. We confirmed well-41 known relationships (lower SLA and LBR in legumes vs non-legumes…), included new species 42 features (base temperature, photosynthetic pathway…), and established relationships for the new 43 shading-response parameters (weeds respond more to shade than crops, by increasing LBR, SLA, HM 44 and WM…). Some correlations reported in ecology (RGR vs SLA…) were not verified on our species 45 pool from arable temperate fields. Shade-response parameters explained species responses to habitat 46 described by Ellenberg indexes, e.g., when shaded, shade-loving species (low Ellenberg-L values) 47 increased SLA and HM to increase light interception. 48 49

embryo [START_REF] Fayaud | Modelling early growth under 868 different sowing conditions: A tool to predict variations in intercrop early stages[END_REF], or that initial leaf area and relative growth rate could increase with seed 143 lipid content as this type of reserve stores more energy [START_REF] Lüttge | Fat -carbohydrate -protein: Storage in plant seeds[END_REF]. 144 145 2.2 Plant material 146 35 weed species and 26 crop species from temperate European arable cropping systems were 147 investigated in the present study (Appendix 1 and Appendix 2). Both crop and weed species were 148 chosen to be frequent but contrasting in terms of species features. Sixteen species were tested in two 149 different years, with several seasons per year for five of these (section B.4 online). For wheat, pea and 150 faba bean, two or three varieties were investigated. Species were chosen to be contrasting in terms of 151 clade, emergence or sowing period, length of life cycle and plant structure. Crop species included both 152 cash crops and cover crops. 153

Weed seeds originated from our in-house seed collection if available, or were bought from Herbiseed 154 (Twyford, UK) at the few occasions where the collection could not provide seeds for the experiment 155 (section A.1 in supplementary material online). Crop seeds of commercial varieties were bought from 156 the local cooperative, and from the in-house variety collection for varieties that were selected by the 157 INRA Dijon genetists' team. Between seed harvest and the experiments, seeds were stored in a cold 158 and dry room. Prior to the experiment, eight samples of 100 randomly chosen seeds were dried for 48 159 hours at 80°C and weighted to determine seed mass for each species or variety. 160 161 2.3 Early growth 162

Experimental conditions 163

The experiment was conducted in an unheated greenhouse at Dijon, Burgundy, France 164 (47°19'2.624"N, 5°4'26.883"E, 257m asl) without artificial light. Several series of experiments with 8 165 to 12 species were carried out, from 2009 to 2012, each lasting for three to four weeks. As far as 166 possible, species were tested during their usual emergence season, i.e. winter species in autumn, spring 167 species in spring and summer species in early summer. Temperature was recorded every 20 minutes 168 with PT100 (ARIA) sensors. Seeds were put onto filter paper inside watered Petri dishes inside growth 169 chambers at optimal temperature and light conditions (details in section A.4 online). Once germinated, 170 seeds were planted 2 cm deep in pots (13 cm x 13 cm x 13 cm) filled with dry potting soil 551 consisting of peat, wood fibers and clay, with 1.2 kg/m³ of 14-16-18 NPK fertilizer and pH 6.5) 172 over clay pebbles, with one plant per plot. For each species or variety, 20 pots were prepared. The 173 greenhouse was equipped with an automatic conveyor belt which moved the pots continuously to 174 provide the most similar thermal and light conditions to all plants. 175

Measurements and statistics 177

The conveyor belt weighted and photographed the pots daily. Water was added daily when needed to 178 keep pots at 2.3 g water/g dry soil. Two pictures were taken from above of each plant twice a day to 179 estimate leaf area. Two control pots without plants were added, each with a 10 cm by 10 cm green 180 cardboard placed horizontally, which was used as a standard to calibrate the images during analysis. 181

Leaf area was estimated from the pictures using Visilog ® (Noésis). 182

Every week after plant emergence, five pots were randomly sampled per species or variety and the 183 plants were taken out to calibrate leaf area values estimated from the images. The height and width of 184 each plant was measured with a ruler, leaf area was measured with a leaf area meter Meter; Li-Cor, Lincoln, NE, USA) and biomass weighted after plants were dried for 48 hours at 80°C. 186 Three weeks after emergence, the remaining 10 plants were similarly measured and weighted. The leaf 187

area measured with the leaf area meter was used to correct the values estimated with image analysis to 188 take account of overlapping leaves that images would not detect (further details in section B.1.1 189 online). 190

For the ten plants monitored throughout the experiment, a linear regression was fitted to the log n -191 transformed leaf area LA p (cm²) vs thermal time TT p (°C days, with species-dependent base 192 temperatures) since plant emergence for each plant p using the lm() function of R (R Core Team, 193 2016). The slope of this regression is the relative growth rate RGR p (cm² cm -2 °C-1 days -1 ) and the 194 constant is the log n -transformed leaf area at emergence LA0 p (cm²) (Storkey, 2004): 195 [1] log n (LA p ) = RGR p • TT p + log n (LA0 p ) 196

Using thermal time rather than the number of days (as did [START_REF] Grime | Relative growth-rate -its range and adaptive significance in a local flora[END_REF] produces an RGR 197 independent of growing conditions and is essential to compare species with different thermal 198 requirements (see for instance [START_REF] Granier | Individual leaf 891 development in Arabidopsis thaliana: a stable thermal-time-based programme[END_REF] for the advantages of thermal time). Measurements 199 taken after the end of the initial exponential growth period were discarded (further details in section 200 B.1.2 online). The parameter values for the species or variety were the average over all those pots for 201 which the R² of the previous linear regression exceeded 0.66 and weighted by the inverse of the 202 relative standard-error of each pot (i.e. se_LA0 p /LA0 p and se_RGR p /RGR p , with se_LA0 p and 203 se_RGR p the standard-errors estimated when fitting equation [1]). 204

Sixteen species were tested in different seasons and years, with 2-5 dates per species (section A.1 205 online). An analysis of variance was run on LA0 p and RGR p , with species and month/year nested 206 within species as factors using lm(), followed by a comparison of means according to Tukey of 207 month/year per species, using lsmeans() and cld() (section B.4 online). 208 209

Potential plant morphology and response to shading 210

The experimental and computational approaches were developed by [START_REF] Munier-Jolain | Investigating and 976 modelling the morphological plasticity of weeds in multi-specific canopies[END_REF] who 211 analysed plant morphology in five contrasting shading conditions over time. Here, we simplified and 212 adapted the method to worked with only two shading conditions (unshaded and highly shaded). 213

Experimental conditions 214

The second series of experiments was carried out in garden plots at INRA Dijon from 2009to 2016. 215 The soil was 0.33 g/g clay, 0.49 g/g silt, and 0.17 g/g sand, with pH=8.3 and 0.31 g/g organic matter. 216

The area was divided into four blocks. The soil was covered with a permeable opaque plastic sheet to 217 avoid emergence of plants other than those sown for the experiment. A 3-m-high metallic cage was 218 erected over half the area of all the blocks, and covered with a shading net to intercept at least 60% of 219 the incident photosynthetically active radiation (PAR). Outside, only the area unshaded by the cage 220 was used for the experiment. Temperature was measured with Testo sensors (175-T1) placed 1 m 221 above ground and protected from the sun, with two sensors inside and two outside the cage. Incident 222 PAR was measured every 10 minutes with quantum sensors (silicium sensors; Solems,Palaiseau,223 France) at 60, 90 and 110 cm above soil surface inside and outside the shading cage. The shading 224 index inside the cage was calculated as 1 -the slope of a linear regression fitted to incident PAR 225 inside vs. outside the cage during the experiment (details in section D.1.5 online). Shading index was 226 0.82 in the 2010 and 2012 experiments, 0.60-0.61 in the other experiments. Section 2.4.3 explains how 227 this index was used to estimate comparable shading response parameters. 228

For each species or variety, seeds were sown into 4 x 4 x 4 cm peat clods (Jiffy pastilles, Puteaux SA) 229 inside plastic seedling trays, preparing 100 clods with 2-3 seeds per clod. The clods were watered and 230 put into lightened growth chambers at 4°C for those species that needed to be vernalized, or directly 231 into an unheated greenhouse without artificial light. Plant stage was monitored on the BBCH scale, i.e. 232 a generic scale applying to both mono and dicotyledonous weed species to identify their growth stages 233 [START_REF] Hess | Use of the 902 extended BBCH scale-general for the descriptions of the growth stages of mono; and 903 dicotyledonous weed species[END_REF]. Once seedlings had reached the 2-leaf stage (stage 2 on the BBCH scale), 234 superfluous plants were eliminated to keep only one plant per clod, and clods were transplanted into 235 the garden plots. Half of the plants (at least 16, if possible 32) were placed inside the shaded cage, and 236 the remaining outside, in the unshaded area. Plants were placed inside holes in the plastic sheet, with 237 at least 50 cm between plants to avoid shading and root interference. In each experimental series, up to 238 10 species or varieties were tested simultaneously, with at least one plant in each block of each light 239 treatment. In case of climbing or twining species, a circular meshed trellis was set up for each plant. 240

The plots were regularly hand-weeded, and watered if necessary. To avoid N stress, 50 kg N/ha were 241 added at the end of winter during the years the experiments were conducted. 242 243

Measurements

244

For each species or variety, four to eight plants were sampled before transplanting, and then for each 245 light treatment at five sampling dates, i.e. 2 leaves, 4 leaves, 8 leaves for dicots or tillering for 246 monocots, flowering onset and flowering end. Sampling dates in unshaded and shaded conditions 247 could differ, because of lower temperature and light conditions inside the shading cage. 248 A lateral picture of each sampled plant was taken with a Canon EOS 450D and analysed with Matlab 249 scripts to determine the dsitribution of leaf area vs relative plant height. Then, plant height and width, 250 leaf area and biomass were measured. For the latter two, leaves (including petioles), stems and 251 reproductive parts were discriminated. 252 253

The parameters of plant morphology and shading response 254

The parameters for characterizing plant morphology and response to shading (Table 1.B and C) were 255 derived from Munier-Jolain et al ( 2014) and were calculated for each sampling date of the garden-plot 256 experiment as well as for one measurement of the initial-growth experiment (~BBCH = 0). As the 257 latter worked with unshaded conditions, shading response was not assessed. 258

Four parameters assess the species efficiency in producing leaf area, leaf biomass, plant height and 259 plant width in unshaded conditions, i.e. specific leaf area (SLA0), leaf biomass ratio (LBR0), specific 260 plant height (HM0) and specific plant width (WM0). Two other parameters (b_HM and b_WM) 261 where v0 was the potential plant morphology in unshaded conditions and v_mu the shading response. 276

The shading index was 0 in unshaded conditions and corresponded to the ratio of the PAR measured 277 inside to that outside the shaded cage (usually approximately 0.60). In the example of the specific leaf 278 area SLA, a positive SLA_mu value indicates that plants increase their specific leaf area when shaded 279 by reducing leaf thickness. The v0 values can also be calculated directly as the average over the four 280 (or more) plants sampled in unshaded conditions, which makes their estimation less dependent on 281 shading conditions but reduces the number of plant samples. 282

The equation for determining the parameters related to plant height and width was somewhat more 283 complicated. Specific plant height HM depends on the plant height H, the total above-ground biomass 284 BM and the shape parameter b_HM: 285 eq. 2.

HM = H / BM b_HM 286

To calculate all three parameters, HM0, b_HM and HM_mu, eq. 1 was modified as follows : 287 eq. 3. 288 This equation was fitted to plant height H vs shading index SI and plant biomass BM, using data of 289 both shaded and unshaded plants. To make HM0 less dependent on shading conditions, it was 290 recalculated as the average of HM over all unshaded plants, using the b_HM value estimated with eq. 291 3. The same principle was used for b_WM, WM_mu and WM0. 292

The last two variables, median leaf area height RLH and leaf distribution b_RLH were not measured 293 directly on individual plants, but estimated by fitting an S-shaped non-linear regression to the relative 294 cumulated leaf area RCLA (cm² cm -²) vs relative plant height rh (cm cm -1 ) (Munier-Jolain et al., 295 2014): 296 eq. 4. 297 RLH is the relative plant height (cm cm -1 ) below which half of the plant's leaf area is located, and 298 b_RLH (dimensionless) is a shape parameter. Values close to 1 indicate a uniform leaf area 299 distribution, and larger values correspond to a leaf area concentrated around RLH. The RLH0 and 300 b_RLH corresponding to leaf area distribution in unshaded conditions were calculated as the averages 301 over RLH and b_RLH estimated with eq. 4 over all unshaded plants. The shading response RLH_mu 302 was estimated by fitting eq. 1 to RLH from all plants vs shading intensity SI. 303 It was not possible to carry out measurements at exactly the same stages for all species because of 304 experimental constraints. Moreover, not all samplings could be carried out when plants were missing 305 because of predation or insufficient emergence. To make species comparable, parameters were 306 interpolated over plant stages, using the BBCH scale. Parameter values were then estimated for 11 307 stages (from BBHC 0 to 10) for each species using local non-parametric regressions (details in section 308 D.3 online). This method has the advantage of not assuming any general shape of the relationship 309 between parameter and time. 310

Here, linear smoothing was used if there were less than six sampling dates, quadratic local polynomial 311 otherwise. Constraints were added, based on biological knowledge: shading response at plant 312 emergence (BBCH=0) was nil (mu = 0), monocotyledonous plants consisted of only leaves at 313 emergence (LBR0 = 1), leaves of totally mature plants (BBCH=10) were dry (SLA0 = 0, LBR0 = 0) 314 and did not respond to shading (mu_SLA and mu_LBR=0). Additional restrictions ensured that 315 parameter values were logical from a biological point of view. For instance, specific leaf area SLA 316 must be > 0, leaf biomass ratio LBR must be in [0, 1] etc. Predictions were also capped by minimum 317 and maximum measured values to avoid extremely small or large values in case of extrapolation for 318 late stages when only a few early stages were measured. 319 eq. 1 and eq. 3 were log-transformed before fitting with PROC REG of SAS. eq. 4 was fitted with 320 PROC NLIN. Non-parametric interpolation was carried out with PROC LOESS. 321 322

Effects of species features on plant morphology and shading

response parameters 324

The data from these two series of experiments as well as data from a field experiment estimating 325 morphology and plasticity parameters [START_REF] Munier-Jolain | Investigating and 976 modelling the morphological plasticity of weeds in multi-specific canopies[END_REF] were pooled in order to establish 326 functional relationships between parameters and species features (taxonomy, quantitative and 327 qualitative, traits, habitat indicators) that are easy to measure or can be found in literature and trait 328 databases (Appendix 3). The initial growth parameters (initial leaf area, relative growth rate) were 329 analysed as a function of 11 species features: seed mass and lipid content, clade (monocot or dicot), 330 emergence type (epigeal or hypogeal), legume vs. non-legume, C3 vs C4, crop vs. weed species, plant 331 lifespan and ecological habitat preferences. Lifespan data for weeds were taken from a database in the 332 decision support system DECID'Herb (Munier-Jolain et al., 2005a); for crops, lifespan was estimated 333 from simulations with the crop model STICS (Brisson et al., 1998) or based on expert opinion. For 334 annuals, we considered minimum and maximum plant lifespan durations. In addition, we 335 discriminated perennials from annuals with a short (strict spring and summer annuals), a long (strict 336 winter annuals) or an indeterminate lifespan (species that emerge in both autumn and spring). These 337 categories were useful for including interactions with quantitative features in the analyses. For habitat 338 preferences, we used base water potential and temperature for germination as indicators of 339 hydrothermal requirements, and three Ellenberg indicators (N, L, R) for nitrogen, light and pH habitat 340 preferences [START_REF] Ellenberg | Zeigerwerte der Gefässpflanzen Mitteleuropas Göttingen[END_REF][START_REF] Ellenberg | Zeigewerte von 863 Pflanzen in Mitteleuropa[END_REF]. If the latter were missing, they were estimated 341 from other ecological indicators (details in section A.3 online). Interactions between clade and 342 emergence type on one hand, seed weight on the other hand were also included. 343

For the analysis of the potential morphology and shading response parameters, further features (plant 344 growth form, distinguishing prostrate, erect, rosette and climbing or twining, section A.2.1 online), 345 parameters (e.g. potential HM when analysing shading response HM_mu) as well as plant stage (in 346 BBCH scale, and distinguishing early, mid and late life) were added (Table 3). Interactions between 347 stage and plant growth form were also included. Features were chosen for their biological relevance to 348 the studied parameters (see introduction). When features supply similar information, precise 349 quantitative features were preferred (e.g. species base temperature was preferred to Ellenberg 350 preference index for temperature). 351 First, correlations among parameters were investigated with a Principal Component Analysis (PCA), 352 followed by a Ward ascendant hierarchy classification to cluster crop and weed species into functional 353 groups, using the PCA() and hclust() functions of the FactoMineR package of R. To identify which 354 species features were linked to parameters, the species features were projected onto the PCA axes. 355 Moreover, two-by-two correlations were analysed among parameters and features as well as between 356 parameters and features, using both Pearson correlation coefficients (cor() function of R) and linear 357 regressions (lm function of R). 358

Then, the effect of species features on parameters was analysed with linear models using PROC 359 GLMSELECT of SAS (version 9.4) which was developed to select from a very large number of 360 effects [START_REF] Cohen | Introducing the GLMSELECT PROCEDURE for Model Selection[END_REF] and has been successfully used in various disciplines (e.g., Van der Borght et 361 al., 2011). Features were removed sequentially (backward selection), by removing effects that at each 362 step produce the smallest value of the Schwarz Bayesian information criterion (SBC) statistic and 363 stopping when removing any effect increased the SBC statistic again. The final model was chosen 364 among the successive models as the one that yielded the lowest predicted residual sum of square with 365 cross validation. For potential plasticity and shading response, forward selection was used as 366 backward selection tended to produce over-fitted models. We moreover eliminated any feature whose 367 effect was not significant at p=0.05. Using a method including cross-validation leads to more robust 368 relationships and avoids fitting regressions that are based on a single extreme species behaviour. The 369 detailed results on all parameters can be found in supplementary material online (section E.1). Here, 370 only a few examples and a schematic summary were presented. 371

Results

372

First, we looked how the analysed parameters varied among species (section 3.1), whether they were 373 correlated (section 3.2) and how they differed between crop and weed species (section 3.3). Next, we 374 analysed which species features influenced parameters of initial growth (section 3.4), potential 375 morphology and shading response (section 3.5). 376

Which parameters varied most among species?

377 Initial leaf area LA0 varied more than a 100-fold, from 0.01 cm² for Matricaria perforata to 3.98 cm² 378 for Pisum sativum cv. Enduro (Table 1.A). It varied more among species than relative growth rate 379 RGR which varied from 0.0093 (Pisum sativum cv. Enduro) to 0.0592 cm²/cm²/°Cdays (Zea mays). 380 Plant width per unit biomass WM0 and, to a lesser degree, height per unit biomass HM0 were the 381 potential-morphology parameters for which species differed most over all stages (largest coefficient of 12 Shading response varied the most among species for specific leaf area (i.e. SLA_mu) and height per 385 unit biomass (HM_mu) and the least for leaf biomass ratio (LBR_mu) (Table 1.C). Shaded plants 386 produced larger (and usually thinner) leaves (i.e. SLA_mu > 0), and increased both their height and 387 width per unit biomass (HM_mu and WM_mu > 0). Some species decreased their leaf biomass ratio 388 when shaded (e.g. Brassica napus, LBR_mu= -0.51 in average over all stages), others invested more 389 biomass into leaves (e.g. Digitaria sanguinalis, average RLH_mu = 0.28). Shading effect on leaf area 390 distribution also varied with the species: some shifted their leaves topwards (e.g. Galium aparine, 391 RLH_mu = 0.62 averaged over all stages), other moved them downwards (e.g. Abutilon theophrasti, 392 average RLH_mu = -0.54). 393

Parameters describing potential morphology and shading response also varied with plant age (Figure 394 1). In unshaded conditions, leaf biomass ratio LBR was the parameter that changed the most during 395 plant life (Figure 1.C), decreasing from 1 (i.e. plants consisting of only leaves) in young plants to 396 approximately 0.20 (i.e. only 20% of biomass attributed to leaves) in average in fully mature plants, 397 but with a huge variability ranging from 0 (leaf-less plants) to more than 0.75 (75% of biomass 398 attributed to leaves at that stage. In addition, specific leaf area SLA decreased (i.e. leaves became 399 smaller, Figure 1.A) and median leaf area height RLH increased (i.e. plants became top-heavier, 400 biomass ratio when shaded, and their leaves became smaller (also line [8] in Table 5). Other 420 correlations were only visible in linear regressions including species features (Table 6). The taller a 421 species was per unit biomass and the top-heavier it was in unshaded conditions, the less it was able to 422 grow taller and top-heavier when shaded (HM_mu and RLH_mu). The expected trade-off between 423 relative growth rate RGR and specific leaf area SLA0 could not be observed on the principal 424 component analysis (Figure 2), and only slight correlations could be identified for four stages using 425 linear regressions (see example in Figure 3, details in section B.3.1 online). 426 427

Did crop and weed species differ?

428 Weed species differed from crop species in several parameters (Table 1): their leaf area at emergence 429 was smaller but they presented a larger specific leaf area in unshaded conditions (SLA0), they were 430 wider per unit biomass (higher WM0), and both plant height and width depended more on plant 431 biomass (higher b_HM and b_WM). Weeds responded much more to shade than crops, further 432 increasing their SLA (higher SLA_mu), their leaf biomass ratio (higher LBR_mu), their plant height 433 and width per unit biomass (higher HM_mu and WM_mu). 434 When other species features were included in the analysis as in sections 3.4 and 3.5, the crop vs weed 449 status was rarely significant. The difference between crops and weeds only remained significant for 450 LA0 (Table 4), b_WM, RLH0 and LBR_mu (Table 6). Indeed, crop and weed species notably differed 451 in several features (Table 3). For instance, there were no legumes among weeds and their leaf nitrogen 452 content was much lower than in crops. Crop plants were potentially taller and narrower than weeds, 453 they were more often winter annuals and perennials, their seeds were heavier but lipid-poorer, they 454 required less warmth and moisture to grow but more light, and there were fewer C4 species and fewer 455 species with epigeal pre-emergent growth among them. 14 None of the analysed species parameters could be easily related to one or a small number of species 457 traits and other features, using Principal Component Analysis (Figure 2.A and C). Consequently, 458 linear regressions were used in the next sections to relate parameters to species features. 459 460

Which features influenced initial growth?

461 Among the 16 species or varieties that were run in different months or years (details in section B.4 462 online), only one species presented a significantly different initial leaf area LA0, i.e. Zea mays leaf 463 area was approximately four times larger in July 2012 than in March 2010. This magnitude is though 464 small compared to the interspecies variation in LA0 which varied more than 400 times among species 465 (Table 1). Once data was aggregated over all seasons for each species, standard-error was 466 approximately half the average leaf area (se = 0.565 ⸱ LA0 1.01 , section B.2 online).The relative 467 growth rate RGR varied for two species with month/year (Solanum nigrum, Z. mays) but the variation 468 was small (2 and 1.3 times), particularly compared to the inter-species variation (13 times). 469

Initial leaf area increased with increasing base temperature and seed weight (Table 4). It was also 470 higher for epigeal vs hypogeal species, and for crop vs weed species. RGR was higher for non-legume 471 vs legume species. It also increased with increasing seed weight and base temperature but decreased 472 with increasing initial leaf area, particularly for hypogeal species. The effect of the other features was 473 not significant. Even when all other features were disregarded, RGR and Ellenberg N were not 474 correlated at all in non-legume species (p= 0.4996, section B.3.1 online). 475 476

Which features influenced potential morphology and shading

477 response? 478

Leaf biomass ratio as a case study

479 Plant stage and growth form. In young plants, leaf biomass ratio in unshaded conditions (LBR0) was 480 the highest for rosette-shaped and erect plant species (regressor values of 1.81 and 1.07 in lines [3] and 481

[4] in Table 5) and the lowest for prostrate and climbing or twining plant species (values of 0.12 and 0 482 5). However, there was a lot variability in shading 493 response with many species increasing their leaf biomass ratio (approximately 50% of values above 494 zero at stages 8-10 in Figure 1.

D). 495

Other plant morphology features. In unshaded conditions, species with potentially wide plants (i.e. 496 with a large maximum plant width) attributed less biomass to leaves than narrower species (-0.00651 497 in line [7] of Table 5). When shaded, potentially tall plants (i.e. with a large maximum plant height) 498 increased leaf biomass ratio less (-0.000663 in line [6]). The same applied to species with a large leaf 499 biomass ratio in unshaded conditions (-0.519 in line [8]). 500

Plant lifespan. In unshaded plants, leaf biomass ratio was highest for perennials and indeterminate 501 annuals and lowest for summer and winter annuals (1.078 and 0.722 in lines [9] and [11] are larger 502 than 0 and -0.002 in lines [10] and [12] of Table 5). When shaded, the same ranking persisted, i.e. 503 perennials and indeterminate species attributed even more biomass to leaves (0.376 and 0.174 in lines 504

[9] and [11]) than the other two types (0 and -0.024 in lines [10] and [12]). 505

Taxonomy, dinitrogen fixation and photosynthetic pathway. In unshaded conditions, dicots 506 generally attributed more biomass to leaves than monocots (0.0877 in line [13] of Table 5). But when 507 shaded, they attributed less biomass to leaves than monocots (-0.145 in line [13]). In unshaded 508 conditions, C4 species presented a lower proportion of leaf biomass than non-legume C3 species (-509 0.622 in line [14]). There was no significant difference in shading response between C3 non-legumes 510 and C4 species (blank cell in line [14]). Legumes also attributed less biomass to leaves than C3 non-511 legumes (-1.603 in line [15]) but legumes and non-legumes did not differ in terms of shading response 512 (blank cell in line [15]). 513 Ecological habitat preferences. The behaviour of non-legumes also depended on their nitrogen 514 requirement (Ellenberg N): species that preferred nitrogen-rich habitats (i.e. high Ellenberg-N values) 515 had a lower leaf biomass ratio in unshaded conditions (-0.146 in line [16] of Table 5) and reduced it 516 even more when shaded (-0.0131 in line [16]), than species preferring nitrogen-poor habitats. The 517 other habitat preferences only influenced shading response. Heliophile species which prefer sunny 518 open habitats (i.e. high Ellenberg-L values) increased leaf biomass ratio more (0.0741 in line [17]) 519 than species preferring shaded habitats (i.e. low Ellenberg-L values). Hygrophilic (i.e., "moisture-520 loving") species (i.e. high base water potential) attributed less biomass to leaves (-0.0539 in line [18] 521 when shaded that species that were adapted to drier habitats. 522

Seed and leaf traits. In unshaded conditions, heavy-seeded species attributed less biomass to leaves 523 than light-seeded ones (-0.236 in line [19] of Table 5). But, when shaded, they attributed more 524 biomass to leaves (0.0632 in line [19]). Leaf traits only influenced shading response. Species with 525 denser leaves (i.e. higher dry matter content) attributed less biomass to leaves when shaded than 526 species with less dense leaves (-0.000726 in line [20]). And species with nitorgen-rich leaves (i.e. high 527 leaf nitrogen content) increased leaf biomass ratio more than species with nitrogen-poor leaves 528 (0.00223 in line [21]). 529

The other parameters 530

The same kind of linear regressions were carried out for all species parameters as a function of species 531 features (section E.1 online). These were summarized into profiles linking species traits and other 532 features to contrasting morphologies and shading responses (Table 6). For instance, in unshaded 533 conditions, a large specific leaf area (i.e. larger usually thinner leaves) was found in young plants, non-534 perennial species with a prostrate growth form, potentially tall and narrow plants; they tended to be 535 dicots, C3 non-legumes that preferred N-poor, acid and/or warm habitats (Table 6.A). Conversely, a 536 low specific leaf area (i.e. small and usually thick leaves) was more frequent in old plants, perennial 537 plants with a climbing or twining growth form, potentially short and wide plants; they tended to be 538 monocots, legumes or C4 species, and they preferred basic and/or cool habitats. 539

The species traits and other features found in these contrasting morphologies and shading responses 540 varied considerably with the analysed parameters. Some tendencies could though be identified. For 541 instance, short-living species (i.e. summer annuals) tended to improve efficiency of biomass to 542 increase leaf area and to occupy space in both shaded and unshaded conditions, i.e. they presented a 543 larger specific leaf area, their plant height depended more on plant biomass and when shaded, they 544 became taller per unit biomass, they invested more biomass in stems but their leaf area was distributed 545 more uniformly along plant height. A few of these correlations could also be seen on the Principal 546 Component Analysis, mainly the higher shading response of summer annuals (Figure 2.A). There was 547 no common tendency in terms of resource deficiency, i.e. different parameter values were found in 548 species adapted to N-poor, cool or dry habitats. 549 550

The importance of interactions 551

Overall, the variability (R²) explained by the species features in the multiple regressions varied from 552 0.09 to 0.85 (mean 0.39), depending on the analysed parameter (Table 7). If regressions were carried 553 out separately for crops and weeds, R² was higher for the former (average 0.60) vs the latter (0.44). 554

The R² was also higher for monocots (average 0.60) vs dicots (0.45) in case of separate regressions. 555

The explained variability could be increased further by adding interactions, e.g. between plant stage 556 and species traits, thus pinpointing correlations that were only visible at either early or late stages. This 557 would though have increased the number of regressors even more, with a high risk of overfitting the 558 model and thus decreasing its genericity. 559

In the complete model, the R² reflected the precision of the various measurements. It was highest for 560 leaf biomass ratio based on only weight measurements, and lowest for specific height HM and width 561 WM which were based on plant height and width (Table 7). Measuring the latter two is notoriously 562 difficult, particularly in climbing and twining species. 563

Discussion

564

The present experiments measured parameters for initial growth, plant morphology in unshaded 565 conditions and plant response to shading in more than 50 annual crops and weeds from temperate 566 arable cropping systems and belonging to 17 different botanical families. In terms of plant 567 morphology, the study showed that species widely differed in terms of plant volume, with specific 568 plant heights and widths HM0 and WM0 greatly varying among species, but they were similar in 569 terms of leaf biomass ratio and leaf area distributions. Similarly, some shading response strategies 570

were common to all species (e.g. specific leaf area SLA increased in shaded species) whereas 571 opposing responses were observed for other morphology variables (e.g. either attribute more biomass 572 to stems or to leaves). 573

In terms of functional relationships linking parameters to easily accessible species features, the study 574

(1) confirmed a few well-known relationships (e.g. lower specific leaf area SLA and leaf biomass ratio 575 LBR in legumes vs non-legumes, increase of initial leaf area with seed weight) (Table 2), (2) included 576 new species features into these relationships (e.g. relative growth rate RGR increased with species 577 base temperature, C4 had a lower LBR than C3 species), and (3) demonstrated a series of original 578 relationships for the newly proposed shading-response parameters (e.g. weeds respond more to shade 579 than crops and do this by increasing LBR and SLA and by producing taller and wider plants for a 580 given plant biomass; prostrate and rosette-shaped plants etiolate more than erect and climbing or 581 twining species). 582 583 4.1 Are our results consistent with previous studies? 584 Many of our results linking species parameters to species features were consistent with previous 585 reports and/or hypotheses (Table 2) and we often confirmed relationships that were first demonstrated 586 on a small number of species (e.g., [START_REF] Fayaud | Modelling early growth under 868 different sowing conditions: A tool to predict variations in intercrop early stages[END_REF] or on other species (e.g., den Dubbelden and 587 Verburg, 1996). Recent studies also demonstrated that changes in light quality modify plant 588 morphology even if the amount of photosynthetically active radiation remains unaltered (McKenzie-589 Gopsill et al., 2019;[START_REF] Schambow | Shade avoidance cues reduce Beta vulgaris growth[END_REF]. Such results support the pertinence of our parameters 590 which discriminate the effect of light on biomass accumulation from that of shading on morphology. 591

Here, we will focus on understanding discrepancies between our results and previous literature reports. 592 Some are only slight. For instance, height per unit biomass was reported to be larger for climbing vs 593 self-supporting legume species [START_REF] Den Dubbelden | Inherent allocation patterns and potential growth rates of 856 herbaceous climbing plants[END_REF]. This was true here only in older 594 plants whereas the opposite ranking was observed for young plants. 595 As we worked with original parameters, it was often difficult to find literature studies to compare to 596 our results. This was particularly true for shading response. The rare studies that investigated 597 morphological plasticity in similar species did not measure shading intensity and calculated specific 598 height differently (with a constant b_HM parameter, [START_REF] Pakeman | A trait-982 based approach to crop-weed interactions[END_REF]. It is thus impossible to 599 compare results. 600

The best documented variable is specific leaf area SLA. The LEDA trait data base (Kleyer et al., 601 2008) reported larger values for the species used here (275 71 cm²/g over sources and species in the 602 data base compared to 179 70 cm²/g over stages and species in our study), without any correlation 603 between the two types of data (p=0.6468 for Spearman correlation, section F.1.1 online). Indeed, we 604 measured SLA in unshaded conditions using all plant leaves and including petioles, whereas some 605 previous studies often only considered the top leaf limbs and did not specify shading conditions. But 606 SLA has been shown to vary along plant height, because of self-shading [START_REF] Ishida | Interactive effects of leaf age and 907 self-shading on leaf structure, photosynthetic capacity and chlorophyll fluorescence in the rain 908 forest tree, Dryobalanops aromatica[END_REF], and we 607 similarly showed that SLA usually increased with shading. When comparing our results to Storkey's 608 (2004) who also worked in unshaded conditions and calculated SLA over all plant leaves, our data 609 were correlated (Spearman r = 0.63, section F.1.2 online). Our SLA values are still lower than 610 Storkey's, probably because he excluded petioles from his measurements (J. Storkey, pers. comm. 611 2018). 612 These methodological differences probably also explain why we did not observe the frequently 613 reported correlation between relative growth rate RGR and specific leaf area SLA (Poorter and 614 Remkes, 1990;[START_REF] Reich | From tropics to tundra: Global convergence in 999 plant functioning[END_REF][START_REF] Poorter | Is inherent varaiation in RGR determind by LAR at low 990 irradiance and by NAR at high irradiance? A review of herbaceous species[END_REF][START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF], except very 615 slightly at vegetative stage. Another explanation could be that our RGR (cm²/cm²/°Cday) was based 616 on plant leaf area growth and not on plant biomass growth as the literature RGR (g/g/days). The two 617 are only equivalent if temperature, leaf biomass ratio and specific leaf area are constant over time, 618 which is not the case (see section 3.5.1). But [START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF] who used the same approach as we did 619 (but also included radiation effects) found the same magnitude in terms of relative growth rate RGR 620 and initial leaf area LA0, and the species were ranked similarly (Pearson correlation coefficient = 0.31 621 and 0.82 for RGR and LA0, respectively). Storkey's RGR and LA0 were in average respectively 40% 622 and 50% larger than ours, because they included stem area in their calculation of RGR, and they 623 started leaf area measurements only a few days after emergence. 624

Most probably, we could not find a trade-off between RGR and SLA in the present study because we 625 focused on crops and weeds (which cohabit in the same type of habitat) whereas ecological studies 626 cover a larger range of habitats and species types (or 100-400 cm²/g in [START_REF] Poorter | Leaf-area ratio and net assimilation rate of 24 wild-species differing in 988 relative growth-rate[END_REF]e.g. 627 SLA measured here at stage 5 ranged from approximately 75-300 cm²/g compared to 60-600 cm² in 628 [START_REF] Reich | From tropics to tundra: Global convergence in 999 plant functioning[END_REF]. [START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF] who worked with species similar to ours could only observe the 629 RGR-SLA correlation when discriminating monocots vs dicots and spring vs autumn growth seasons. 630

Other studies focusing on crops were also unable to identify an RGR-SLA trade-off and explained this 631 by their limited species pool as well as a domestication effect which could have distorted the 632 relationship [START_REF] Tribouillois | A Functional Characterisation of a Wide Range of Cover Crop Species: Growth and Nitrogen Acquisition Rates, Leaf Traits and Ecological Strategies[END_REF]. 633

These differences in methods and species pools probably also explain other discrepancies between our 634 results and previous reports from literature. For instance, we did not observe the expected negative 635 correlation between the leaf dry matter content LDMC and the specific leaf area SLA (Wilson et al., 636 1999;[START_REF] Roche | Congruency analysis of species ranking based on leaf traits: which traits are the more reliable?[END_REF][START_REF] Mcintyre | The role of plant leaf attributes in linking land use to ecosystem function in 952 temperate grassy vegetation[END_REF][START_REF] Tribouillois | A Functional Characterisation of a Wide Range of Cover Crop Species: Growth and Nitrogen Acquisition Rates, Leaf Traits and Ecological Strategies[END_REF] or the positive correlation between 637 RGR or SLA and Ellenberg N [START_REF] Poorter | Leaf-area ratio and net assimilation rate of 24 wild-species differing in 988 relative growth-rate[END_REF]. Indeed, the latter was observed with 638 biomass-based RGR and single-leaf SLA on a species pool whose Ellenberg N indices varied from 1 639 to 8. Our species ranged from 5 to 9 and in that range Poorter & Remkes' data did not show any 640 notable correlation either (section F.2 online). The same was true for many other correlations with 641

Ellenberg indicator values reported in literature (Table 2). 642

In conclusion, the identification of functional relationships depends on measurement conditions, 643 methods as well as on the investigated species pool. Our results clearly show the limits of transposing 644 results from comparative ecology, which focuses on a wide range of habitats, to agricultural fields. 645

The trade-offs among plant traits identified on large sets of wild species are not necessarily valid when 646 analysing plant strategies of species from a narrower range of habitats. 647 648

Disentangling species differences from environmental effects 649

The present study combined the method developed by Munier-Jolain et al ( 2014) to characterize plant 650 morphology in heterogeneous crop:weed canopies with the method developed by [START_REF] Gardarin | Effects of seed depth and soil structure on the emergence of 878 weeds with contrasted seed traits[END_REF]651 2011) to link difficult-to-measure species parameters to easily accessible species features. Munier-652 Jolain's parameters were essential to separate the effect of radiation on biomass accumulation from 653 that of shading response. Indeed, shaded plants are usually smaller and lighter than unshaded plants, 654 but the shading-response parameters used here check whether plants change their rules for allocating 655 biomass and determining morphology. 656

These parameters are very expensive to measure in terms of time, space and labour. But their nature 657 makes their values largely independent of the experimental conditions (see e.g. section 2.3.2 658 explaining this for relative growth rate RGR) and thus makes it possible to compare species tested in 659 different years or outside growth chambers, albeit some methodological precautions. To minimize the 660 risk of confusing species and year effects, the experimental setup in the garden plots aimed to ensure 661 optimal water and nitrogen conditions, and the initial growth phase was studied in greenhouse where 662 plants were protected from frost damage. Working in garden plots has the disadvantage that the 663 amount of available light varies with the years (see section E.6 online), but it has the major advantage 664 over climate chambers of having natural light (in terms of magnitude, quality and daily variation), and 665 allowing us to space plants sufficiently to avoid shading from neighbours. By sampling plants at key 666 stages (instead of fixed dates), we moreover accounted for differences in temperature not only between 667 shaded and unshaded conditions, but also among experimental years and seasons. Moreover, we 668 recently started to test our species in quite different conditions (University of Rostock, North-Eastern 669 20 Germany) and the first results show that the parameter values estimated there on the same French 670 populations are very close to those measured in France [START_REF] Bürger | Basistemperatur der Keimung und relative Wachstumsrate von 13 833 Unkrautarten im Vergleich der Samen aus zwei mitteleuropäischen Herkünften (Germination 834 base temperature and relative growth rate of 13 weed species -comparing populations from 835 two geographical origins)[END_REF]. 671

In contrast to most ecological studies (e.g. the trade-off between RGR and SLA, Table 2), we analysed 672 most of the parameters implicated in competition for light, and this for a large number of species. We 673 moreover analysed these parameters in multiple regressions instead of two-by-two analyses and did 674 this using a large number of contrasting species. This was essential as previous studies established 675 many simultaneous two-by-two correlations, without being able to conclude which was actually 676 relevant as many explanatory species features were themselves correlated, particularly when working 677 with small species pools (see review by [START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF]. We had the same problem 678 here: the difference in parameters between crop vs weed species was often due to differences in other 679 species properties, e.g. legumes could only be crops and C4 species were more frequently weeds. But, 680 even if it had been our objective, it would have been next to impossible to apply a complete species 681 sampling plan decorrelating the crop vs weed status from other species because of the effect of crop 682 breeding. 683

Our multiple regressions made it to possible to identify minor correlations that are impossible to see in 684 2-by-2 analyses, similar to [START_REF] Tribouillois | A Functional Characterisation of a Wide Range of Cover Crop Species: Growth and Nitrogen Acquisition Rates, Leaf Traits and Ecological Strategies[END_REF], or to identify the traits and processes that explain 685 differences between species types. For instance, we observed large differences in parameter values 686 between the analysed crop and weed species. Even though our species choice did not aim at testing the 687 crop vs. weed status of species, the observed differences were consistent with hypotheses on 688 domestication, i.e. crops were selected to ensure a fast establishment, homogenous populations and a 689 large seed production to the detriment of other abilities. Conversely, weeds responded much more to 690 shade, by increasing leaf area and plant width per unit biomass. This is logical insofar as weeds 691 usually grow below crop canopies and must thus be able to tolerate shade. But most of this difference 692 was actually related to other feature differences, as shading response depended very little on the crop 693 vs weed character of the species, once other features were included in the analysis. 694

Our approach made it possible to explain species responses to habitat that are characterized in ecology 695 by integrative indicators such as Ellenberg indicators. For instance, heliophilic species (i.e. high 696

Ellenberg-L values) had a high light requirement because they were potentially smaller and narrower 697 per unit biomass, and when shaded, they were bad at outgrowing neighbours (i.e. increasing height 698 and width per biomass) and had to increase leaf biomass ratio to compensate for their smaller leaves. 699

In addition to habitat preferences, we were able to demonstrate other novel relationships, e.g. the lower 700 leaf biomass ratio of C4 vs. C3 species, or the correlation with seed and emergence traits whose effect 701 was not solely limited to initial growth but persisted throughout plant life (e.g. correlation between 702 seed lipid content or leaf nitrogen content and specific leaf area). Many of these correlations between 703 parameters and species features are easy to understand and predict (e.g., the larger photosynthetic 704 efficiency of C4 allows them to reduce their leaf biomass ratio), but there have been, to our 705 knowledge, no experimental demonstrations presented to date as summarized in Table 2. For others, 706 we have no biological assumption yet, far less a demonstration of a biological cause. For instance, 707 basidophile species here presented a lower leaf area and plant width per unit biomass and responded 708 less to shading in terms of plant height and width than acidophile species, even though the experiment 709 was carried out on an alkaline soil. 710 711

Practical conclusions for experiments and parameter estimation

712 As already mentioned above, the parameters studied here are difficult to measure. The present study 713 attempted to propose a few solutions to this bottleneck. For instance, additional analyses (section E.3 714 online) showed that experiments could be limited for the first three stages and further behaviour could 715 be predicted from these earlier measurements and species traits. The necessary species features are 716 either easy to measure (e.g. seed mass) or can be found in trait databases (e.g. the TRY database, 717 http://www.try-db.org). 718 Parameters could also be solely estimated from species features to add new species to models such as 719 FLORSYS weed dynamics simulation model (whose parameters were the conceptual basis of the 720 present study, [START_REF] Munier-Jolain | A 3D model for light interception in 973 heterogeneous crop:weed canopies. Model structure and evaluation[END_REF][START_REF] Munier-Jolain | Investigating and 976 modelling the morphological plasticity of weeds in multi-specific canopies[END_REF]. This approach was validated by [START_REF] Gardarin | Modélisation des effets des systèmes de culture sur la levée des adventices à partir 875 de relations fonctionnelles utilisant les traits des espèces[END_REF] in greenhouse experiments who compared the predicted emergence of weeds to 722 observations, using either measured or estimated parameters, as well as by [START_REF] Colbach | Uncertainty analysis and 850 evaluation of a complex, multi-specific weed dynamics model with diverse and incomplete 851 data sets[END_REF] who 723 compared observed weed densities from multiannual and multisite field trials to simulations with the 724 FLORSYS model including parameter-feature functions for pre-emergent parameters. Incidentally, the 725 latter evaluation study also validated the relevance of the present morphology and shading-response 726 parameters for predicting crop yield and multiannual weed dynamics as the FLORSYS simulations were 727 run with many species and parameters measured in the experiments presented here. 728

As the R² of the statistical models estimating parameters from species features were sometimes low, 729 using separate models for crops vs weeds, or dicots vs monocots, would improve the level of 730 explained parameter variability (section 3.5.3). This is tantamount to including more interactions, with 731 a risk of overfitting the model. The use of the GLMSELECT function which uses cross-validation to 732 identify the best model is thus essential to avoid making effects depend on a single data point and to 733 reduce the risk of confusing effects. The latter was essential as our data set was imbalanced, e.g. there 734

were no legume weeds and few C3 crop species, and probably thanks to domestication, our crop 735 species were generally taller with heavier seeds. Cross-validation was even more crucial as we 736 identified here several novel correlations for which we have as yet no demonstration of a biological 737 link (e.g. decrease in specific leaf area SLA with increasing Ellenberg-R which reflects species 738 preference for basic soils). 739

Predicting parameters from a few detailed measurements combined with accessible species features, or 740 with e.g. crop-only functions would be helpful for parameters that could not be precisely predicted 741 here with the complete models such as those of Table 5. This would be particularly the case for the 742 parameters that are essential to simulate crop production and weed impacts with models such as 743 FLORSYS, e.g. potential plant width per unit biomass and its shading response [START_REF] Colbach | The response of weed and crop species to shading: 853 which parameters explain weed impacts on crop production?[END_REF]. 744 745

Practical implications for crop and weed management 746

The effect of domestication on crops was visible both in the analysed light-competition parameters as 747 well as in the species features that were linked to these parameters, with crops tending to be faster, 748 larger and more homogeneous (i.e. lower standard-errors on parameter values, lower sensitivity of 749 plant width to plant biomass b_WM) aiming at homogenous canopies with a large biomass production. 750

Conversely, weeds were more plastic, reflecting their adaptation to survive and grow inside earlier-751 emerging crop canopies. But the present study demonstrated these species types to overlap, with a 752 large variability. 753 Table 4 and Table 6 could be used to choose (cash or cover) crop species or varieties based on their 754 light-competition abilities according to the targeted objectives and the production situation. For 755 instance, epigeal heavy-seeded summer crops could ensure a faster crop establishment whereas 756 oligotrophic, non-legume C3 dicots would maximise light interception. 757 However, neither biomass production nor crop-weed competition can be inferred from a single 758 parameter, and it is impossible to conclude on the performance of parameter combinations from these 759 tables. This is only possible after integrating the parameters into a simulation model such as FLORSYS 760 as we did in the companion paper [START_REF] Colbach | The response of weed and crop species to shading: 853 which parameters explain weed impacts on crop production?[END_REF]. There, we were able to determine ideal crop-761 parameter combinations in terms of weed control, showing for instance that the same parameter values 762 promote crop and weed species in mixed canopies and that successful species present a larger specific 763 leaf area and are taller and wider per unit biomass, particularly when shaded. Integrating the 764 parameters into a model also allows checking their consistencies indirectly, by comparing model 765 simulations to independent field observations. This evaluation demonstrated that the model based on 766 the present parameters produces predictions consistent with field observations [START_REF] Colbach | Uncertainty analysis and 850 evaluation of a complex, multi-specific weed dynamics model with diverse and incomplete 851 data sets[END_REF]. 767 768

Conclusion

769 By combining ecological and agronomical approaches, the present study was able to produce new 770 insights on crop:weed competition for light. From agronomy, we borrowed the idea of using detailed 771 parameters linked to ecophysiological processes. We could thus explain species responses to habitat -772 which are characterized in ecology by integrative indices -via differences in plant morphology and, 773 particularly, the ability to respond to shade by, e.g., increasing leaf area or plant height for a given 774 biomass. From comparative ecology, we borrowed the notion of species traits and trade-offs among 775 traits, showing, e.g., that plants with a lower specific leaf area compensate with a higher leaf biomass 776 ratio. By combining both approaches, we were able to establish functional relationships that link 777 process-close but difficult-to-measure species parameters to easy-to-measure integrative species traits. 778 This, combined with the use of novel traits that have not yet been used in comparative ecology, 779 identified new insights on which plant traits drive shading response. As we focused on species that 780 cohabit in the same type of habitat (i.e. crops and weeds in temperate arable fields), the investigated 781 species were more similar and the range of explored species traits much smaller than in ecological 782 studies. This, together with our process-close parameters (e.g. leaf-area based RGR, discriminating 783 shade response from biomass production), explains why classic correlations (e.g., relative growth rate 784 RGR vs specific leaf area SLA) reported in ecology were not observed. Relationships established in 785 ecology on a large range of wild species from very contrasting habitats do thus not necessarily apply 786 to domesticated species or species evolving in a single type of habitat. 787 Table 1. Species parameters for characterizing initial growth, potential plant morphology and response to shading. Median, minimum and maximum values over all species as well as means per crop and weed species (values of a row followed by the same letter are not significantly different at p=0 & standard-deviation/mean, except for shading response where standard-deviation because of negative values of mean close to zero $ Biomass is always dry (leaf or plant) mass.

Table 2. Summary of relationships between species features and parameters reported in literature, the underlying hypotheses and possible adaptations for our study (SLA: specific leaf area, RGR: relative growth rate, 062 LBR and RBR: respectively, leaf and root biomass ratio, LAR: leaf area ratio; HM specific plant height; for further details see Table 1). Green and red cells show relationships respectively consistent and inconsistent 063 with our own observations, yellow cells show cases where we did not find any relationship in contrast to literature, blank cells show correlations that we did not study [START_REF] Wilson | Specific leaf area and leaf dry matter content as alternative predictors of plant strategies[END_REF][START_REF] Roche | Congruency analysis of species ranking based on leaf traits: which traits are the more reliable?[END_REF][START_REF] Wright | The worldwide leaf economics spectrum[END_REF][START_REF] Mcintyre | The role of plant leaf attributes in linking land use to ecosystem function in 952 temperate grassy vegetation[END_REF][START_REF] Tribouillois | A Functional Characterisation of a Wide Range of Cover Crop Species: Growth and Nitrogen Acquisition Rates, Leaf Traits and Ecological Strategies[END_REF] Analyse all parameters

Resource capture -LDMC is a marker of a conservation strategy (low efficiency in resource capture) [START_REF] Lavorel | Predicting changes in community composition and ecosystem 942 functioning from plant traits: revisiting the Holy Grail[END_REF] Analyse shading response Leaf nitrogen content LNC RGR + LNC is a marker of resource acquisitive species. [START_REF] Lavorel | Predicting changes in community composition and ecosystem 942 functioning from plant traits: revisiting the Holy Grail[END_REF][START_REF] Tribouillois | A Functional Characterisation of a Wide Range of Cover Crop Species: Growth and Nitrogen Acquisition Rates, Leaf Traits and Ecological Strategies[END_REF] Analyse all parameters Epigeal vs hypogeal pre-emergent growth

Initial plant leaf biomass +

The emerging cotyledons of epigeal species contribute to leaf mass and area immediately after emergence [START_REF] Fayaud | Modelling early growth under 868 different sowing conditions: A tool to predict variations in intercrop early stages[END_REF] Analyse initial plant leaf area Seed mass Initial plant leaf biomass + Heavier seeds include more reserves and/or a larger embryo [START_REF] Seibert | Growth Analysis of Weed and Crop Species with Reference to Seed Weight[END_REF][START_REF] Fayaud | Modelling early growth under 868 different sowing conditions: A tool to predict variations in intercrop early stages[END_REF] Analyse initial plant leaf area SLA -Small-seeded species devote more biomass to leaves but have denser leaves Insect growth rate + Higher growth rate compensates for higher temperature requirement [START_REF] Angus | Phasic development in 807 field crops. I. Thermal response in the seedling phase[END_REF][START_REF] Trudgill | Thermal time -concepts and utility[END_REF][START_REF] Gardarin | Prediction of germination rates of weed species: 880 relationships between germination parameters and species traits[END_REF] Analyse all parameters, use base temperature and water potential instead of Ellenberg T and M Germination rate + Higher temperature requirements allow annuals to detect gaps in existing vegetation [START_REF] Washitani | Gap-detecting mechanism in the seed germination ofMallotus japonicus (Thunb.) Muell. Arg., a common pioneer tree of secondary succession in temperate Japan[END_REF] SLA + Frost resistant species have smaller (and usually thicker) leaves [START_REF] Palta | Frost-Hardiness in Relation to Leaf Anatomy and Natural Distribution of 984 Several Solanum Species1[END_REF]) Hygrophily RGR, SLA, germination speed + Drought-resistant species invest more into roots, higher growth rates compensates for higher moisture requirements [START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF] Heliophily RGR, SLA -High SLA compensates for low light availability in shaded habitats [START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF] Analyse all parameters as a function of Ellenberg N, L and R Nitrophily SLA + In nutrient-rich habitats, species mainly compete for light, which selected for high SLA and RGR to the detriment of below-ground processes [START_REF] Poorter | Leaf-area ratio and net assimilation rate of 24 wild-species differing in 988 relative growth-rate[END_REF][START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF]) RGR + RBR -Nitrophilic species invest less biomass into roots [START_REF] Fichtner | The effect of nitrogen nutrition on growth and biomass partitioning 871 of annual plants originating from habitats of different nitrogen availability[END_REF][START_REF] Moreau | The ecophysiological determinants of 957 nitrophily in annual weed species[END_REF]) LBR -Preferences for soil pH RGR, SLA + Calciphile species could prefer nitrate over ammonium and higher temperature requirements, calcifuge species could be better adapted to acidic habits with their low nutrient availability and higher toxicity [START_REF] Bartelheimer | The response of grassland species to nitrate versus 809 ammonium coincides with their pH optima[END_REF][START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF] Morphology SLA RGR + [START_REF] Poorter | Leaf-area ratio and net assimilation rate of 24 wild-species differing in 988 relative growth-rate[END_REF][START_REF] Reich | From tropics to tundra: Global convergence in 999 plant functioning[END_REF][START_REF] Poorter | Is inherent varaiation in RGR determind by LAR at low 990 irradiance and by NAR at high irradiance? A review of herbaceous species[END_REF][START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF]) LA0 RGR - [START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF]) $ 0 = no effect, + = positive correlation or increase, -= negative correlation or decrease 065 Table 3. Differences in species traits between crop and weed species. Comparison of lsmeans after analysis of variance of trait as a function of crop vs weed character. R² cells were coloured from white (0) to green (highest partial R²). For the list of references referring to the traits, see section

• • • • • • • • • • • • • • • • • • 10
• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • -0

(

  evaluate how far plant height and width depend on plant biomass, ranging from 0 (height and width 262 are constant) to 1 (height and width increase linearly with plant biomass). Two further parameters 263 assess leaf area distribution along plant height, with high RLH0 values indicating top-heavy plants and 264 high b_RLH values indicate that leaves are grouped together instead of distributed along the whole 265 plant height. Five other parameters evaluate the species response to shading, positive values indicating 266 that shaded plants increase their specific leaf area, leaf biomass ratio, plant height and width per unit 267 biomass (SLA_mu, LBR_mu, HM_mu, WM_mu, respectively) and shift their leaves topwards 268 , species (or variety) and morphological variable, a non-linear equation based on 272 Munier-Jolain et al (2014) was fitted to each variable v (e.g. specific leaf area SLA) measured on all 273 shaded and unshaded plants vs the shading index SI (MJ/MJ): 274 eq. 1. 275

Figure 1 .

 1 Figure 1. I) with plant age. The variability among species made it more difficult to identify general 401 tendencies for the other parameters (Figure 1.E and G). 402 Shading response generally increased with plant stage, i.e. parameter values became increasingly 403 positive or negative (Figure 1.B, D, F, H, I). Shading response of specific leaf area SLA was the 404 shading response that changed most during plant life, with plants progressively increased their SLA 405 more when shaded (Figure 1.B). The same applied to specific plant height and width (Figure 1.D and 406 F), i.e. older plants increased their plant heights and widths more when shaded. As written above, the 407 change in shading response with plant age depended very much on the species for the two remaining 408 parameters. Some species increasingly attributed more biomass to leaves (Figure 1.D) and/or shifted 409 their leaves upwards when shaded (Figure 1.H); the opposite applied to other species. 410 411

  However, when clustering species based on parameters of initial growth, plant morphology and 435 shading response for the different plant stages (Figure 2.B and D), crop and weed species belonged to 436 the same clusters. The only exception was cluster C consisting of the earliest stages of five weed 437 species only, i.e. Abutilon theophrasti (ABUTH), Avena fatua (AVEFA), Chenopodium album 438 (CHEAL), Digitaria sanguinalis (DIGSA) and Polygonum persicaria (POLPE). The species and plant 439 stages of this cluster were characterized by taller and wider plants per unit biomass in unshaded 440 conditions (HM0 and WM0 in upper right quadrant of Figure 2.B), with a strong impact of plant 441 biomass on plant height and width (b_HM, b_WM in the same upper right quadrant). All the other 442 clusters comprised both crops and weeds, and usually species changed clusters when growing older. 443 Ambrosia artemesiifolia (AMBEL), Panicum miliaceum (PANMI), oilseed rape (BRSNN), sunflower 444 (HELAN), two pea varieties (China and Enduro) and maize (ZEAMX) were the only species 445 remaining in the same cluster throughout their plant life, albeit in different ones. Wheat varieties 446 always belonged to the same clusters, whereas pea and field bean varieties were spread over different 447 clusters, pointing to a larger intra-species variability in the studied parameters. 448

  in lines [2] and [5]). LBR0 decreased with plant stage, i.e. young plants consisted mostly of leaf 483 biomass and old plants mostly of stem biomass (Figure 1.C). The decrease was the fastest for rosette-484 shaped and erect plants (regressor values of -0.953 and -0.843 for stage in lines [3] and [4] in Table 5) 485 and the slowest for prostrate and climbing or twining species (values of -0.752 and -0.558 in lines [2] 486 and [5]). So, in old plants (stage = 10), rosette-shaped plants presented the lowest leaf biomass ratio 487 (1.81 -0.953 • 10 = -7.72) and climbing or twining species the highest leaf biomass ratio (0 -0.558 • 488 10 = -5.58). 489 Shading response generally increased with plant stage, i.e. parameter values became increasingly 490 positive or negative (Figure 1.D). Generally, older plants tended to attribute less biomass to leaves 491 when shaded, particularly climbing or twining species (-0.0477 in line [5] is more negative than the 492 three regressor values of lines [2] to [4] in Table

  years, this project was supported by INRA, the French project CoSAC (ANR-15-CE18-790 0007), the European projects AMIGA (FP7-KBBE-2011-5-CP-CSA), the Casdar RAID project funded 791 by the French Ministry in charge of Agriculture and Food (Ministère de l'Agriculture et de 792 l'Alimentation, avec la contribution financière du compte d'affectation spéciale 'Développement 8 Tables

(

  (base values or Ellenberg indicator values as proxies) Thermophily

Figure 2 .

 2 Figure 2. Typology of crop and weed species based on a Principal Component Analysis on the morphology and shading-response parameters. Correlation circles with 105parameters in blue (see Table1for meaning), with the five most important species features projected in red for the first two (A) and the last two axes (C). Individuals are 106 species x stage combinations clustered into groups, following a Ward ascendant hierarchy classification for the first two (B) and the last two axes (D). Species names are 107 EPPO codes, with weeds highlighted in yellow and crops in green, and the main cluster of each species in bold (Nathalie Colbach © 2019) 108

Figure 3 .

 3 Figure 3. Variation in relative growth rate (RGR) with specific leaf area estimated for BBCH stage 4 from experiments ( = monocots, = dicots, = hypogeal, = epigeal). Line shows fitted linear regression (y = 0.0090 + 7.55 10 -5 x, R² = 0.19, p= 0.0013)

  Figure 1 Click here to download Figure: Figure 1 boxplot.pdf

Figure 2

 2 Figure 2 Click here to download Figure: Figure 2 cluster.pptx

Table 5 .

 5 Effect of species traits on potential leaf biomass ratio and its response to shading. Linear regressors estimated with GLMSELECT using forward selection N=672. Blank cells show effects that are not significantly different from zero at p=0.05. Continuous variables are in italics.

	A.2 online

Table 6 .

 6 Summary of effects of species features on morphology and plasticity parameters based on linear regression as 084 in Table5(with details in section E.1 online).

	085
	086	A. Unshaded conditions
		Parameters	Contrasted	Species types
			morphologies	Crop or weed,	Plant growth	Taxonomy, N2	Habitat	Seed and
				plant stage, and	form & ,	fixation and	preference $	leaf traits §
				life-cycle	maximum plant	photosynthetic
				duration	dimensions,	pathway
					morphology
		Specific leaf		Young plant,	Prostrate, tall	Dicots, non-	N-poor ' , acid,
		area (SLA0)		annuals,	and narrow	legume, C3	warm
				Old plant,	Climbing or	Monocots,	N-rich # ,
				perennial,	twining, short	legume, C4	Basic, cool
					and wide
		Leaf biomass ratio (LBR0)		perennial Young plans,	twining, narrow Climbing or	legume, C3 Dicots, non-	N-poor	Small seeds
				Old plans,	Prostrate, wide Monocots,	N-rich	Heavy seeds
				summer or		legume, C4
				winter annual
		Specific plant		Young plant	Erect or rosette,		N-poor for	Lipid-poor
		height (HM0)			tall, stemmy		non-legumes,	seeds
							shaded, warm
				Old plant	Prostrate,		N-rich for	Lipid-rich
					climbing or		non-legumes,	seeds
					twining, short,		sunny, cool
					leafy
		Impact of		Young plant,	Rosette or erect,	Dicots	Sunny, basic Lipid-poor
		plant biomass		summer annual	tall per unit		seeds
		on plant			biomass
		height (b_HM)		Old plant, indeterminate	Climbing or twining, short	Monocots	Shaded, acid Lipid-rich seeds
				annual	per unit biomass
		Specific plant			Tall	Non-legume, C4 Shaded, acid N-rich
		width (WM0)					leaves
					Short	Legume, C3	Sunny, basic N-poor
							leaves
		Impact of		Weed	Wide, wide per
		plant biomass			unit biomass
		on plant width		Crop	Narrow, narrow
		(b_WM)			per unit biomass
		Median leaf		Weed, old plant,			N-poor,	Small seeds,
		area height		perennial			basic, cool,	hypogeal
		(RLH0)t					moist	growth, non-
							dense leaves
				Crop, young			N-rich, acid,	Heavy
				plant, summer			warm, dry	seeds,
				annuals			epigeal
							growth,
							dense leaves
	087
	088

  Table 1 for meaning), with the five most important species features projected in red for the first two (A) and the last two axes (C). Individuals are 106 species x stage combinations clustered into groups, following a Ward ascendant hierarchy classification for the first two (B) and the last two axes (D). Species names are 107 EPPO codes, with weeds highlighted in yellow and crops in green, and the main cluster of each species in bold (Nathalie Colbach © 2019) 108

  List of crop species tested in the present experiments (further trait values can be found in section A.2 online)

	Appendix 2. Family	Species		Cultivar	EPPO	Seed mass	Used in experiments in
					code	(mg) §	Greenhouse Garden plots
	Poaceae 10 Appendix	Avena strigosa Festuca rubra		Pratex Greenlight FESRU AVESG	18.11 0.86	2015 2015
	Sorghum bicolor Appendix 1. List of weed species tested in the present experiments (further trait values can be found in SORVU 23.0 Triticum aestivum Caphorn TRZAX 42.1 section A.2 online) Triticum aestivum Cézanne TRZAX 45.5	2010 2009 2009	2009 2009
	Family Poaceae Asteraceae Boraginaceae Brassicaceae Amaranthaceae Fabaceae Apiaceae Asteraceae Brassicaceae Caryophyllaceae Cucurbitaceae Euphorbiaceae Geraniaceae § Dry mass per seed; $ Year the experiments were carried out; & (Munier-Jolain et al., 2014) Species EPPO code Seed mass (mg) § Triticum aestivum Orvantis TRZAX 42.1 Used in analysis of xTriticosecale Matinal TTLSS 43.6 Greenhouse Garden plots 2009 2009 Alopecurus myosuroides ALOMY 2.3 & Zea mays ZEAMX 252 2010, 2012 2012 2009 2009, 2015 2009 Avena fatua AVEFA 18.5 Guizotia abyssinica Azofix GUIAB 2.51 2010 2015 2009, 2010 2010 Digitaria sanguinalis DIGSA 0.63 2012 Helianthus annuus HELAN 41.1 2010 2012 2012 Echinochloa crus-galli ECHCG 2.24 Phacelia tanacetifolia Angelia PHCTA 1.98 2010 2015 2010, 2011 2011 Panicum miliaceum PANMI 4.3 2012 Brassica napus BRSNN & 4.4 2012 Poa annua POAAN 0.3 Raphanus sativus Cardinal RAPSR 12.1 2010 2015 2010, 2012 2012 Setaria viridis SETVI 1.4 Sinapis alba SINAL 6.5 2010 2012 Amaranthus retroflexus AMARE 0.38 Glycine max GLXMA 185 2010 2010, 2011 2011 Chenopodium album CHEAL 0.56 Lathyrus sativus N-fix LTHSA 162 2016 2010, 2011 2011 Aethusa cynapium AETCY 1.4 Lens culinaris Anicia LENCU 31.0 2016 2011 Ambrosia artemisiifolia AMBEL 4.59 Lens nigricans Lentifix LENNI 17.1 2015 2010, 2011 2011 Ammi majus AMIMA 0.50 Lotus corniculatus Leo LOTCO 1.13 2016 2010 Lapsana communalis LAPCO 0.90 Medicago lupulina Virgo MEDLU 1.71 2016 2010 Matricaria perforata MATIN 0.27 2012 Medicago sativa Galaxy MEDSA 2.00 2016 2012 Matricaria recutita MATCH 0.3 Pisum sativum China PIBSX 153 2010 2010 2010, 2011 Senecio vulgaris SENVU 0.26 2010 Pisum sativum Enduro PIBSX 187 2010 2010 2010 Sonchus asper SONAS 0.3 Pisum sativum 886/1 PIBSX 131 2010 2010 2010, 2011 2011 Capsella bursa-pastoris CAPBP 0.14 2009 Trifolium alexandrinum Tabor TRFAL 3.64 2010 2016 2009 Raphanus raphanistrum RAPRA 6.35 Trifolium pratense Trevviso TRFPR 2.20 2016 2012 Sinapis arvensis SINAR 1.97 Trifolium repens Aberdai TRFRE 0.66 2015 2009 Stellaria media STEME 0.4 Trigonella foenum-graecum Fenusol TRKFG 16.9 2015 2009, 2010 2009 Sicyos angulatus SIYAN 102.2 Vicia faba Diana VICFX 270 2016 2010 Euphorbia helioscopia EPHHE 2.5 Vicia faba Gladice VICFX 426 2010 2010 2010 Mercurialis annua MERAN 1.87 Vicia sativa Nacre VICSA 50.4 2016 2011 Geranium dissectum GERDI 2.12 2009 2009
	Malvaceae	Abutilon theophrasti	ABUTH	8.12	2012 $	2012
	Plantaginaceae	Veronica hederifolia	VERHE	3.52		2009
		Veronica persica	VERPE	0.67	2009, 2010 2009
	Polygonaceae	Fallopia convolvulus	POLCO	6.52	2010	2011
		Polygonum aviculare	POLAV	1.52	2010, 2011 2011
		Polygonum persicaria	POLPE	1.9	2010, 2011 2011
	Rubiaceae	Galium aparine	GALAP	7.37	2010	&
	Solanaceae	Datura stramonium	DATST	7.2	2010	2012
		Solanum nigrum	SOLNI	0.8	2010, 2011 2011
	Violaceae	Viola arvensis	VIOAR	0.57	2009, 2010
	§ Dry mass per seed
	$ Year the experiments were carried out
	& (Munier-Jolain et al., 2014)

  (nitrogen), L (light), R (pH), base temperature and water potential § Seed mass and lipid-content, leaf dry matter content, leaf nitrogen content, epigeal or hypogeal growth & Erect, prostrate, rosette, climbing or twining # Only for non-legumes

	Table 6a
	Parameters	Contrasting	Species types
		changes	Crops or weeds,	Plant growth	Taxonomy, N2	Habitat	Seed and
			plant stage, and	form & ,	fixation and	preference $	leaf traits §
			life-cycle	maximum plant	photosynthetic
			duration	height and	pathway
				width,
				morphology
	Change in		Old plants,			N-rich,	Lipid-rich
	specific leaf		summer annuals			shaded	seeds, N-
	area when						rich leaves
	shaded (mu_SLA)		Young plants, indeterminate			N-poor, sunny	Lipid-poor seeds, N-
			annuals				poor leaves
	Change in Leaf biomass ratio when shaded (mu_LBR)		Weeds, young plants, perennials	Tall, stemmy	Monocots	N-poor, sunny, dry	Heavy seeds, non-dense or N-rich leaves
			Crops, old	Short, leafy	Dicots	N-rich,	Light seeds,
			plants, summer			shaded,	dense or N-
			or winter			moist	poor leaves
			annuals
	Change in		Old plants,	Rosette or	Legume, C3	Shaded,	Light seeds,
	Specific plant		summer annuals	prostrate, tall		acid	hypogeal,
	height when			and narrow,			dense leaves
	shaded			short per unit
	(mu_HM)			biomass
			Young plants,	Erect, climbing	Non-legume, C4 Sunny,	Heavy
			perennials	or twining, short		basic	seeds,
				and wide, tall			epigeal,
				per unit biomass			non-dense
							leaves
	Change in		Old plants	Narrow		Acid
	Specific plant
	width when shaded		Young plants	Wide		Basic
	(mu_WM)
	Change in		Old plants,	Rosette, bottom-	Dicots	Shaded	Lipid-poor
	Median leaf		perennials or	heavy			or light
	area height		winter annuals				seeds, non-
	when shaded						dense leaves
	(mu_RLH)		Young plants,	Prostrate, top-	Monocots	Sunny	Lipid-rich
			summer or	heavy			or heavy
			indeterminate				seeds, dense
			annuals				leaves
	$ Ellenberg N

Table 6b Parameters Model type All species

 6b

			Weeds only	Crops	Monocoty-ledonous species only	Dicoty-ledonous species only
	Number of species	61	25	36	15	46
	Potential morphology (in unshaded conditions)
	Specific Leaf Area SLA0	0.42	0.30 0.55	0.49	0.55
	Leaf biomass ratio LBR0	0.86	0.83 0.88	0.89	0.86
	Specific plant height HM0	0.26	0.25 0.68	0.3	0.32
	Sensitivity of plant height to biomass b_HM	0.59	0.63 0.85	0.79	0.59
	Specific plant width WM0	0.22	0.26 0.45	0.24	0.40
	Sensitivity of plant width to biomass b_WM	0.29	0.32 0.48	0.96	0.20
	Median relative leaf area height RLH0	0.35	0.37 0.65	0.67	0.28
	Shape of leaf area distribution b_RLH	0.62	0.45 0.71	0.93	0.68
	Shading response of
	Specific Leaf Area SLA_mu	0.45	0.48 0.75	0.44	0.53
	Leaf biomass ratio LBR_mu	0.41	0.51 0.54	0.54	0.44
	Specific plant height HM_mu	0.42	0.53 0.58	0.61	0.41
	Specific plant width WM_mu	0.09	0.21 0.23	0.46	0.1
	Median relative leaf area height RLH_mu	0.39	0.56 0.58	0.48	0.41

Table 7

 7

variation in Table 1.B). Conversely, species were more similar in terms of leaf biomass ratio (LBR0)

and leaf area distribution (RLH0).

GLMSELECT of SAS on 45 annual crop and weed species. Blank [START_REF] Larsen | Self-incompatibility in Beta vulgaris L. I. Four gametophytic, complementary S-loci 940 in sugar beet[END_REF]Bruckler, 1983b;1983a;[START_REF] Hur | Temperature Effects on Germination of Birdsfoot Trefoil and 905 Seombadi1[END_REF][START_REF] Gummerson | The effect of constant temperatures and osmotic potentials on the 900 germination of sugar beet[END_REF][START_REF] Bouaziz | Modeling wheat seedling growth and emergence. I. Seedling growth 818 affected by soil water potential[END_REF][START_REF] Fournier | Les quatres flores de France[END_REF][START_REF] Benvenuti | Calculation of threshold temperature for the development of 816 various weeds[END_REF][START_REF] Van Der Weide | Population dynamics and population control of Galium aparine L[END_REF][START_REF] Chauvel | Variabilité de la production de semences chez le vulpin (Alopecurus myosuroides 838 Huds.) en fonction de la culture[END_REF][START_REF] Marshall | Non-linearity in rate-temperature relations of germination in oilseed 945 rape[END_REF]Brisson et al., 1998;[START_REF] Grundy | Modelling the germination of Stellaria 896 media using the concept of hydrothermal time[END_REF]Colbach et al., 2002a;Colbach et al., 2002b;[START_REF] Granier | Individual leaf 891 development in Arabidopsis thaliana: a stable thermal-time-based programme[END_REF][START_REF] Batlla | A quantitative analysis of seed responses to cycle-813 doses of fluctuating temperatures in relation to dormancy: developent of a thermal time model 814 for Polygonum aviculare L. seeds[END_REF]Munier-Jolain et al., 2005b;[START_REF] Mcgiffen | Emergence 950 prediction of common groundsel (Senecio vulgaris)[END_REF]Sartorato and Pignata, 2008;[START_REF] Alghamdi | Chemical Composition of Faba Bean ( Vicia faba L.) Genotypes under Various 805 Water Regimes[END_REF][START_REF] Masin | Temperature and 947 Water Potential as Parameters for Modeling Weed Emergence in Central-Northern Italy[END_REF][START_REF] Gardarin | Prediction of germination rates of weed species: 880 relationships between germination parameters and species traits[END_REF]Kattge et al., 2011;[START_REF] Nasab | Effects of water potential on germination and seedling growth of two varieties 979 of lentil (Lens culinaris Meidck)[END_REF][START_REF] Fayaud | Early growth in intercrops: an 865 experimental and simulation approach for a range of species under different sowing 866 conditions[END_REF][START_REF] Guillemin | Determination of base 898 temperatures and base water potentials for germination of weeds[END_REF][START_REF] Dürr | Ranges of critical temperature and water 859 potential values for the germination of species worldwide: Contribution to a seed trait 860 database[END_REF][START_REF] Gardarin | How much of seed dormancy in weeds can be explained by seed 883 traits?[END_REF][START_REF] Rolletschek | A novel noninvasive procedure for high-throughput screening of major seed traits[END_REF][START_REF] Bretagnolle | Determination of fatty acids 820 content, global antioxidant activity and energy value of weed seeds from agricultural fields in 821 France[END_REF][START_REF] Gardarin | How do seed and seedling traits influence 885 germination and emergence parameters in crop species? A comparative analysis[END_REF][START_REF] Tribouillois | Determination of germination response to temperature and water potential for a wide range of cover crop species and related functional groups[END_REF][START_REF] Scherner | Germination of winter annual grass weeds under a range of temperatures and water potentials[END_REF] Insect growth rate + Higher growth rate compensates for higher temperature requirement [START_REF] Angus | Phasic development in 807 field crops. I. Thermal response in the seedling phase[END_REF][START_REF] Trudgill | Thermal time -concepts and utility[END_REF][START_REF] Gardarin | Prediction of germination rates of weed species: 880 relationships between germination parameters and species traits[END_REF] Analyse all parameters, use base temperature and water potential instead of Ellenberg T and M Germination rate + Higher temperature requirements allow annuals to detect gaps in existing vegetation [START_REF] Washitani | Gap-detecting mechanism in the seed germination ofMallotus japonicus (Thunb.) Muell. Arg., a common pioneer tree of secondary succession in temperate Japan[END_REF] SLA + Frost resistant species have smaller (and usually thicker) leaves [START_REF] Palta | Frost-Hardiness in Relation to Leaf Anatomy and Natural Distribution of 984 Several Solanum Species1[END_REF]) Hygrophily RGR, SLA, germination speed + Drought-resistant species invest more into roots, higher growth rates compensates for higher moisture requirements [START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF] Heliophily RGR, SLA -High SLA compensates for low light availability in shaded habitats [START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF] Analyse all parameters as a function of Ellenberg N, L and R Nitrophily SLA + In nutrient-rich habitats, species mainly compete for light, which selected for high SLA and RGR to the detriment of below-ground processes [START_REF] Poorter | Leaf-area ratio and net assimilation rate of 24 wild-species differing in 988 relative growth-rate[END_REF][START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF]) RGR + RBR -Nitrophilic species invest less biomass into roots [START_REF] Fichtner | The effect of nitrogen nutrition on growth and biomass partitioning 871 of annual plants originating from habitats of different nitrogen availability[END_REF][START_REF] Moreau | The ecophysiological determinants of 957 nitrophily in annual weed species[END_REF]) LBR -Preferences for soil pH RGR, SLA + Calciphile species could prefer nitrate over ammonium and higher temperature requirements, calcifuge species could be better adapted to acidic habits with their low nutrient availability and higher toxicity [START_REF] Bartelheimer | The response of grassland species to nitrate versus 809 ammonium coincides with their pH optima[END_REF][START_REF] Bartelheimer | Functional characterizations of Ellenberg indicator values -a 811 review on ecophysiological determinants[END_REF] Morphology SLA RGR + [START_REF] Poorter | Leaf-area ratio and net assimilation rate of 24 wild-species differing in 988 relative growth-rate[END_REF][START_REF] Reich | From tropics to tundra: Global convergence in 999 plant functioning[END_REF][START_REF] Poorter | Is inherent varaiation in RGR determind by LAR at low 990 irradiance and by NAR at high irradiance? A review of herbaceous species[END_REF][START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF]) LA0 RGR - [START_REF] Storkey | Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits[END_REF]) $ 0 = no effect, + = positive correlation or increase, -= negative correlation or decrease Ellenberg L (light) 0.0741 [18] Base water potential (MPa) -0.0539 Seed and leaf traits [19] Seed mass log10(mg) -0.236 0.0632 [20] Leaf dry matter content (g/g) -0.000726 [21] Leaf nitrogen content (g/g) 0.00223 § LBR was transformed to 10 LBR before analysis