Reproductive Allocation Patterns in Different Density Populations of Spring Wheat

Reproductive Allocation Patterns in different density

populations of Spring Wheat

JING LIU1,3, GEN-XUAN WANG2*, LIANG WEI1 AND CHUN-MING 5 WANG1

(1 Key Laboratory of Arid and Grassland Agroecology at Lanzhou University, Ministry of

Education, Lanzhou 730000, China; 2* College of Life Sciences, Zhejiang University, Hangzhou,

310027, China; 3 Institute of Modern Physics, the Chinese Academy of Sciences, Lanzhou 730000,

10China)

Author for Correspondence Tel: +86-571-88206590 Fax: +86-571-88206590

Email: [email protected], wanggx@ zju.edu.cn 15

1 Abstract: The effects of increased intraspecific competition on size hierarchies (size

inequality) and reproductive allocation were investigated in populations of the annual plant spring

wheat (Triticum aestivum). A series of densities (100, 300, 1000, 3000 and 10000 plants/m2) along

a gradient of competition intensity were designed in this experiment. The results showed that

5average shoot biomass decreased with the increased density. And reproductive allocation was

negatively correlated to Gini coefficient (R2 = 0.927), which suggested that reproductive allocation

is inclined to decrease as size inequality increases. These results suggested that both vegetative

and reproductive structure were significantly affected by intensive competition. On the other hand,

results also indicated that there were different relationships between plant size and reproductive

10allocation pattern in different densities. In the lowest density population lacking competition (100

plants/m2), individual reproductive allocation was size-independent but, in high density

populations (300, 1000, 3000 and 10000 plants/m2) those competition occurred, individual

reproductive allocation were size-dependent: the small proportional larger individuals were

winners in competition and got higher reproductive allocation (lower marginal reproductive

15allocation, lower MRA), and the large proportional smaller individuals were suppressed by larger

ones and got lower reproductive allocation (higher MRA). In conclusion, our results support the

prediction that elevated intraspecific competition would result in higher levels of size inequality

and decreased reproductive allocation (negative relationship between them). However, deeply

analysis indicated that these frequency- and size-dependent reproductive strategies were not

20evolutionarily stable strategies.

Keywords: competition; density; reproductive allocation.

Foundations: Supported by National Natural Science Foundation of China (90102015,

2 30170161) and Cooperation Project of International in China and Greece (2003DFB00034).

In plant populations, increase density can intense plant-plant competition directly (Pianka

1981). And competition among plants is an important factor in affecting size hierarchies (Bonan

1988; Weiner 1985; Pan et al. 2003a, b), growth rate (Ehleringer 1984; Weiner & Thomas 1992),

5survivorship (White, 1981; Tanner, 1997; van Kleunen et al., 2001), and reproductive output

(Ehleringer 1984; Weiner & Thomas 1992; van Kleumen et al. 2001; Soto-Pinto et al., 2000).

Among these components, size hierarchies and reproductive output have been mostly investigated

because they are of tremendous ecological and evolutionary significance.

It has long been recognized that size inequality always increased with increasing density

10(Weiner 1985). The increase of size inequality may affect total reproductive output (Weiner 1985;

Sugiyama & Bazzaz 1997; Pan et al. 2003a, b). Weiner (1988) presented a simple linear model of

size-dependent reproductive output to explain the decrease in reproductive allocation (RA) in

plants grown at high densities. And it has also been reported that, in water deficits and mulching

with clear plastic film conditions in spring wheat (Triticum aestivum) populations, there is a

15negative correlation between RA and Gini coefficient (Pan et al. 2003a, b). It was suspected that

this negative correlation would exist along an elevated intraspecific competition gradient.

Unfortunately, direct evidence along density gradient is still lacked to prove this prediction. So, in

the present experiment, we designed a wide range of densities for further analysis.

The purpose of this study was to explore (1) the relationship between size inequality and RA

20in a wide range of densities from no competition to intensive competition (which enduring self-

thinning) and (2) size-dependent reproductive patterns in different densities.

3 Results

Size inequality

Average shoot biomass per plant decreased with the increased density in 20th June (Figure 1),

which means the increased strength of intraspecific competition suppressed plant growth.

5 RA was negatively correlated to G of the population (R2 = 0.927, Figure 2). The negative

correlation suggests that RA is inclined to decrease as size inequality increases. This result

supports the theory that stand uniformity of field crops is an important aspect of high yield

formation (Glenn & Daynard 1974).

Size-dependent reproductive allocation

10 To investigate the relationships between RA and plant size and between spike size and plant

size, data in 20th June was analyzed when spring wheat grown in grain filling stage and no

individuals died because of ripening. For all five populations, spike size increased linearly with

plant size (above-ground biomass) (Figure 3, Table 2). Meanwhile, RA increased inversely with

plant size in the four competitive densities (300, 1000, 3000 and 10000/m2) (Figure 4, Table 3).

15Apparently, individual spike size and RA in competitive populations in spring wheat were size-

dependent. However, in density 100/m2, RA kept constant in different plant sizes (Figure 4, Table

3). So, when the population lacks competition, individual RA is size-independent.

. Discussion

20 Four competition levels were investigated in this study: (1) no competition (populations

sowing in 100/m2); (2) slight competition (populations sowing in 300/m2); (3) intermediate

4 competition (population sowing in 1000/m2); and (4) intensive competition (populations sowing in

3000/m2 and 10000/m2 which endured self-thinning in the growth period). The results indicated

that the relationship between plant size and RA of these populations developed in different ways.

Reproductive allocation and size structure

5 Weiner (1988) presented a simple linear model (WR  aWP  b ) between weight of plant

reproductive structure (spike biomass,WR ) and plant size (shoot biomass,WP ) to explain the

decrease in RA in plants grown in high densities. He suggested that plants must reach a certain

size before they can devote energy to reproductive biomass. Thus, the slope and the x-intercept of

the linear model should be positive. The linear relationship was confirmed in all of the five

10densities in the present study, except that the x-intercept in 100/m2 was negative (Fig. 5, Table 3).

Pan et al. (2003a) developed Weiner’s theory and transformed the formula as below:

RA  WR WP  a  b WP

Where a is defined as innate RA, which represented the maximum RA (RAmax) of the

species. The slope of the inverse curve is the additional RA from each additional unit of the Wp,

15which is defined as marginal reproductive allocation (MRA, dRA/ dWp  b / x 2 ). And MRA is

an indicator of reproductive potentiality. The declined in MRA as plant size increases is called

diminishing marginal reproductive allocation (Pan et al. 2003a). It is believed that there is a trade-

off between plant size and MRA, and a population must consist of a large proportion of small

5 individuals with high MRA and a small proportion of large individuals with low MRA. So these

frequency- and size- dependent reproductive strategies in plant populations are thought of a good

case for evolutionarily stable strategy (ESS). However, there are several deficiencies in this

contention.

5 In competitive populations, large individuals are strong competitors and they have chances to

utilize all their potential reproductive ability. So the diminished MRA means that large individuals

have higher individual fitness, and can generate offspring more successfully. Small individuals are

weak competitors with high MRA. If other individuals give up competition, they will improve

their RA. However, in dense populations, small individuals risked density-dependent mortality

10more than large individuals (Weiner 1985). So compared with large ones, small individuals are

easy to give up the competition. Moreover, spring wheat is an annual semelparous species, and

this determined that small individuals with high MRA have little chance to improve their RA. So,

in this experiment, the diminished MRA is not bad for large individuals and high MRA is not good

for small individuals. That is, trade-off between plant size and MRA is invalid.

15 A strategy or strategy mixture that cannot be invaded by novel strategies is called an

evolutionarily stable strategy (ESS or ESS mixture. Falster and Westoby, 2003). So we can test a

population for ESS by whether it has the capacity to exclude alternate strategies from invading.

Potential invading strategies can be thought of as rare mutants within the existing population, or as

initially rare species colonizing from elsewhere. It has been widely recognized that modern seed

20crops, which are seeking maximum population yield, are of evolutionarily unstable, and require

ongoing artificial selection to maintain the evolutionarily unstable strategy (Zhang et al. 1999;

Falster and Westoby 2003). In this study, the frequency- and size- dependent reproductive

6 strategies in dense spring wheat populations are of evolutionarily unstable too, because neither

large plant size with high reproductive capacity nor small plant size with low reproductive

capacity is a successful strategy. Furthermore, in self-thinning populations, the later strategy will

be eliminated from the serious competitive populations, and the previous strategy will fail when

5faces a more competitive strategy such as the mutant that developed large plant size and low RA.

So these strategies are not stable against invasion by rare mutant or deviant strategies. In fact,

without artificial management, most of crop populations will be replaced by other wild species, or

will be replaced by more competitive mutants.

Size-dependent reproductive allocation patterns in different densities

10 In this study, different size-dependent reproductive patterns are shown along the different

densities. In population without competition (density treatment in 100 plants/m2), all members

(large ones and small ones) can develop their maximum potential reproductive ability. So MRA

equal to zero constantly (Figure 4, Table 3) and their reproductive pattern are size-independent.

This implies that plant RA is decided by its intrinsic property when lacking competition. While in

15the higher density populations (density treatments in 300, 1000, 3000, and 10000 plants/m 2) with

competition, just as found by Weiner (1988) and Pan et al. (2003a, b), there are significant

correlations between plant size and reproductive structure (Figure 3, Table 2) and between plant

size and RA (Figure 4, Table 3). Along the higher density treatments, the increased size inequality

suggested the proportion of small ones increased and the proportion of large ones decreased. So, it

20is the small ones that mainly contributed to the great growth redundancy, for the small ones had

relative lower RA in the higher density treatments. Furthermore, the relative lower RA in large

proportional small ones (even some barren shoots existed self-thinning populations) incurred

7 average RA decreased along the density. So it can be concluded that there were negative

correlations between size inequality (as measured by Gini coefficient) and RA (Figure 2) within

these competitive populations.

In conclusion, this study supported the prediction (Pan et al. 2003a) that elevated intraspecific

5competition would result in higher levels of size inequality and decreased RA. And there were

inverse correlations between plant size and RA in competitive populations. These facts imply that

reproductive strategies in competitive populations are frequency- and size- dependent. But we are

not sure that they are evolutionarily stable so far, because there was no evidence to prove that they

can exclude other competitive strategies, especially in artificial managed populations.

10Material and Methods

Field experiment was conducted in the year of 2003 at Yuzhong experimental station of

Lanzhou University(36º03´N, 103º53´E and 1517m in altitude). The main climatic conditions

are presented in Table 1. The soil is a loess-like loam, with a bulk density of 1.37g/cm3, and a field

water holding capacity (FWHC, maximum capillary held water) of 25% (gravimetrically). High

15organic fertilizer inputs were imposed to the experimental plot before sowing.

Seeds of spring wheat (Triticum aestivum L., cv. Longchun No.15) were sprinkled in some

plots 50×50 cm2 at five densities (in each density three replicates were used in this study): 100,

300, 1000, 3000 and 10000 seeds/m2, respectively. The total irrigation was 50×5 mm. Rainfall

during the period from sowing to the last harvest was 229 mm (Table 1). Sowing occurred on 18-

2020 March 2003, and harvest was taken on 20 June. The plots were surrounded by supports of wire-

netting to prevent the plants from lodging (especially in high densities) and 25 cm guard rows to

8 avoid marginal effect. Plants were sprayed with chlorpyriphos (Dow Agrosciences, USA) against

pests attack and streptomycin solution against bacteria. High fertilizer inputs (irrigated with 500ml

solution which content 1% urea and 0.2% potassium dihydrogen phosphate per plot every two

weeks) were imposed during the growth stage.

5 Plants grown in the central plot were harvested at ground level. 100-150 lived individuals

were randomly collected from the plants, which come from three plots. The individuals were put

in paper bags separately, dried (65℃, 48h), and weighed. Data collected for each plot included:

(dry) shoot biomass per plant (total sample size is 100-150 in each treatment for calculating Gini

coefficient, but in density 100 plants/m2, the total sample size is 40-70). And in the samples, shoot

10biomass and spike biomass (for calculating RA which is equal to spike biomass/shoot biomass) of

each plant were weighed separately.

Hierarchies were defined in terms of the degree of inequality in the frequency distribution as

measured by the Gini coefficient (Weiner & Solbrig 1984). Gini coefficients range from 0

(absolute equality in the frequency distribution) to 1 (absolute inequality) (Bonan 1988). The Gini

15coefficient was calculated using the formula:

n n 2 G   xi  x j 2n x i1 j1

Where xi and xj represent the masses of all possible pairs of individuals, and n is the sample

size. Calculated G values were multiplied by n n 1 to give unbiased values G. Confidence

intervals for Gini coefficients were determined using a ‘bootstrapping’ technique (Dixon et al.

201987, Weiner 1985). In this study, G was calculated for each of 200 artificial samples of the same

9 size as the original sample, which are taken from the original sample with replacement.

Confidence intervals for G values are determined from the distribution of G values for these

bootstrapped samples using a bias-corrected percentile method.

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11 Figure legends:

Figure 1. Mean plant biomass (above-ground) in different density treatments in 20th June. Figure 2. Relationships between Gini coefficients of spring wheat populations and reproductive

allocation (RA) (data in 20th June). The regression line is y = -1.464x + 0.5221, R2 = 0.927.

5Figure 3. Linear relationship between spike size and plant size (above-ground biomass) in

different spring wheat populations in 20th June.

 indicate the individuals with spikes which used in fitting the line;  indicate the barren

individuals which was excluded in fitting the line. See Table 2 for regression coefficients.

Figure 4. Inverse relationship between RA and plant size (above-ground biomass) in different

10spring wheat populations in 20th June.

individuals which was excluded in fitting the line. See Table 3 for regression coefficients.

12 Table 1. The mean climatic conditions of the experimental site in China (Data come from

Lanzhou meteorological administration)

Year Annual Annual mean Precipitation Annual Evaporation Relative mean precipitation during mean during humidity temperature (mm) growing evaporation growing (%) (℃) season (mm) (mm) season (mm) Mean 9.1 328 158 1365 938 59 annual 2003 10.8 376 229 1113 703 58

Table 2. Parameters of linear regression* between spike size (y) and plant size (above-ground

5biomass, x) for individuals of spring wheat in different densities in 20th June.

2 Density in Density in Slope (a1) y-intercept x-intercept R Sample th sowing 20 June (b1) (-b1/a1) size (plants/m2) 100 101±2 0.271±0.009 0.206±0.103 -0.760 0.958 46 300 279±6 0.312±0.005 -0.021±0.033 0.067 0.973 100 1000 952±24 0.321±0.004 -0.005±0.007 0.016 0.977 148 3000 2288±164 0.308±0.005 -0.030±0.004 0.097 0.960 151(2**) 10000 5424±620 0.290±0.005 -0.011±0.002 0.038 0.970 150(39**)

* The model is y  a1x  b1

** Number of barren individuals in the sample. To fit the model, the barren individuals were

excluded.

13 Table 3. Parameters (±S.E.) of inverse regression* between RA (y) and plant size (above-ground

biomass, x) for individuals of spring wheat in different densities in 20th June.

2 Density in sowing Density in Slope b2 y-intercept a2 R Sample size (plants/m2) 20th June 100 101±2 0.032±0.058 0.290±0.010 0.007 46 300 279±6 -0.044±0.010 0.319±0.004 0.153 100 1000 952±24 -0.008±0.001 0.323±0.002 0.218 148 3000 2288±164 -0.009±0.001 0.264±0.004 0.376 151(2**) 10000 5424±620 -0.005±0.001 0.264±0.006 0.225 150(39**) 1 * The model is y  b  a 2 x 2

** Number of barren individuals in the sample. To fit the model, the barren individuals were

5excluded.

s 10 s a m o

) 8 i t

o 6 p o / h g s ( 4 n a e m 10 2 0 100 300 1000 3000 10000 density

15Figure 1. Mean plant biomass (above-ground) in different density treatments in 20th June.

15 10000/m2 3000/m2 1000/m2 300/m2 100/m2 0.4

R 0.25

0.1 0 0.1 0.2 0.3 Gini coefficient Figure 2. Relationships between Gini coefficients of spring wheat populations and reproductive allocation (RA) (data in 20th June). The regression line is y = -1.464x + 0.5221, R2 = 0.927.

16 100 plants per m2 7

) 6 g (

z 4 i s

S 1 0 0 10 20 30 P lant size (g)

300 plants per m2 7

z 4 i s

k 2 i p

S 1 0 0 10 20 15 P lant size (g)

1000 plants per m2 2 ) g

20 e k

i 0.5 p S 0 0 2 4 6 P lant size (g)

3000 plants per m2 25 0.7 )

g 0.6 (

z 0.4 i s

k 0.2 i p

S 0.1 0 30 0 1 2 3 P lant size (g)

10000 plants per m2 0.5 )

g 0.4 (

z 0.3 i

0.2 e k i

p 0.1 S 0 0 0.5 1 1.5 P lant size (g) 40Figure 3. Linear relationship between spike size and plant size (above-ground biomass) in

different spring wheat populations in 20th June.

17 individuals which was excluded in fitting the line. See Table 2 for regression coefficients.

18 100 plants per m2 0.4

0 0 10 20 30 P lant size (g)

300 plants per m2 0.4 10 0.35 0.3 0.25

A 0.2 R 0.15 0.1 0.05 0 15 0 10 20 P lant size (g)

1000 plants per m2 0.4

A 0.2 20 R 0.1

0 0 2 4 6 P lant size (g)

3000 plants per m2 25 0.4

A 0.2 R

0.1 30 0 0 1 2 3 P lant size (g)

10000 plants per m2 0.4

35 R 0.2

0 0 0.5 1 1.5 P lant size (g) 40Figure 4. Inverse relationship between RA and plant size (above-ground biomass) in different

spring wheat populations in 20th June.

19 individuals which was excluded in fitting the line. See Table 3 for regression coefficients.