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Keywords:

  • ACCase target site mutation;
  • dry matter acquisition/ allocation;
  • evolution of resistance;
  • fitness cost;
  • growth analysis;
  • Lolium rigidum;
  • P450 herbicide metabolism

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • • 
    Costs of resistance are predicted to reduce plant productivity in herbicide-resistant weeds.
  • • 
    Lolium rigidum herbicide-susceptible individuals (S), individuals possessing cytochrome P450-based herbicide metabolism (P450) and multiple resistant individuals possessing a resistant ACCase and enhanced cytochrome P450 metabolism (ACCase/P450) were grown in the absence of mutual plant interaction to estimate plant growth traits.
  • • 
    Both P450 and ACCase/P450 resistant phenotypes produced less above-ground biomass than the S phenotype during the vegetative stage. Reduced biomass production in the resistant phenotypes corresponded to a reduced relative growth rate and a lower net assimilation rate and rate of carbon fixation. There were no significant differences between the two resistant phenotypes, suggesting that costs of resistance are associated with P450 metabolism-based resistance. There were no differences in reproductive output among the three phenotypes, indicating that the cost of P450 resistance during vegetative growth is compensated during the production of reproductive structures.
  • • 
    The P450-based herbicide metabolism is shown to be associated with physiological resistance costs, which may be manipulated by agronomic management to reduce the evolution of herbicide resistance.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The evolution of herbicide resistance in weeds is a worldwide phenomenon in modern cropping systems (reviewed in LeBaron & Gressel, 1982; Powles & Holtum, 1994; Powles & Shaner, 2001). This evolutionary process involves the selection and enrichment of adaptive mutant alleles that endow resistance. If a resistance allele or allelles compromises plant function, then the mutant genotype may suffer a relative disadvantage or reduction in one or more components of plant fitness (termed physiological or direct cost of resistance) compared with the wild type in the absence of herbicide selection (Fisher, 1928; Strauss et al., 2002). A number of explanations are considered to account for the existence of physiological herbicide resistance costs (Strauss et al., 2002): (1) a mutation associated with target site resistance may in some way compromise normal functioning of the target site enzyme, resulting in pleiotropic changes in cellular metabolism (Gronwald, 1994; Purrington & Bergelson, 1999; Berticat et al., 2002); (2) where the resistance mechanism is nontarget site based then the resistance may be associated with diversion of resources to increased or novel production of detoxification enzymes (Coley et al., 1985; Herms & Mattson, 1992); or (3) resistance loci may be tightly linked and cosegregate with other loci affecting plant growth attributes (Bergelson & Purrington, 1996; Purrington & Bergelson, 1997).

The existence, magnitude and nature of any resistance cost depends on the mechanism(s) and genetic basis of resistance (Coustau et al., 2000; Roux et al., 2004). For target site-based herbicide resistance a physiological cost is well documented for triazine herbicide resistance. In this case, a specific point mutation in the psbA gene that encodes for a Ser (264) Gly amino acid substitution in the D1 protein confers triazine resistance in many weed species and this mutation incurs a resistance cost by reducing photosynthetsis (see explanation 1) and hence compromising vegetative and reproductive plant growth (reviewed by Gronwald, 1994). However, for other target site-based mechanisms endowing herbicide resistance the results on the expression of resistance costs are less clear. For example, there is a diversity of target site gene mutations endowing resistance to the acetyl CoA carboxylase (ACCase) and acetolactate synthase (ALS) inhibiting herbicides, each having the potential to endow different resistance costs, meaning that studies on costs of herbicide resistance must be assessed for each specific mutation.

Resistance costs that could be associated with nontarget site based herbicide resistance have been little studied. Evolved herbicide resistant populations of Lolium rigidum and Alopercus myosuroides can have a nontarget site based resistance mechanism of enhanced rates of herbicide metabolism owing to elevated activity of cytochrome P450 enzymes (Hall et al., 1995; Preston et al., 1996). It is not known whether there is a physiological resistance cost associated with this nontarget site-based P450-catalysed herbicide metabolism mechanism. Increased or novel production of detoxifying enzymes (nontarget site resistance) may incur resistance costs if there is an increased allocation of resources to the production of these enzymes (explanation 2) (Herms & Mattson, 1992). In this case, the expression and magnitude of these costs will depend on whether enzyme production is constitutive or induced by herbicide application (Karban et al., 1997; Strauss et al., 2002).

Individual L. rigidum plants can display multiple resistance to ACCase and ALS inhibiting herbicides owing to both target site- and nontarget site-based resistance mechanisms (Tardif et al., 1993; Tardif & Powles, 1994; Preston et al., 1996; Preston & Mallory-Smith, 2001). This phenomenon of multiple resistance provides an excellent opportunity to compare resistance costs associated with different herbicide resistance mechanisms in individuals within a single resistant population (similar genetic background). In a well characterized multiple resistant L. rigidum population (SLR31), the majority of individuals are resistant to aryloxyphenoxypropionate (APP) and sulfonylurea (SU) herbicides because of enhanced herbicide metabolism mediated by cytochrome P450 monooxygenase enzymes (Christopher et al., 1991; Christopher et al., 1994; Tardif & Powles, 1994; Preston & Powles, 1998). In addition, 15% of individuals possess a resistant ACCase enzyme (target-site mechanism) (Tardif & Powles, 1994) conferred by an Ile to Leu substitution at amino acid 1781 of the ACCase gene (X. Q. Zhang & S. B. Powles, unpublished). This point mutation endows resistance to APP and cyclohexanedione (CHD) herbicides (Tardif & Powles, 1994). Individuals with the resistant ACCase also have enhanced P450-mediated herbicide metabolism and are therefore multiple resistant individuals (Vila-Aiub et al., 2005). A small proportion (6%) of the population is susceptible to APP, CHD, and SU herbicides. A plant cloning technique (Vila-Aiub et al., 2005) has permitted the identification and isolation of these three distinct phenotypes from within the SLR31 population, herein termed herbicide susceptible individuals (S), enhanced P450 metabolism-based resistant individuals (P450) and multiple herbicide resistant individuals (ACCase/P450). Research presented here aims to determine direct resistance costs by assessing plant performance (i.e. resource acquisition and allocation patterns at the vegetative and reproductive stage) of these herbicide resistance phenotypes (S, P450 and ACCase/P450).

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Plant material

A multiple herbicide-resistant Lolium rigidum Gaudin population (SLR31), originating from Bordertown, South Australia (140°46′ E, 36°18′ S) was used in all experiments. The P450 and ACCase/P450 phenotypes comprising SLR31 L. rigidum population exhibit differential responses to the ACCase inhibiting APP and CHD herbicides. The insensitive ACCase confers resistance to certain CHD herbicides (e.g. sethoxydim), while those individuals in the population possessing only enhanced herbicide metabolism (P450) are susceptible to these CHD herbicides, but resistant to diclofop-methyl (Tardif & Powles, 1994). Based on this difference, susceptible (S), and P450 and ACCase/P450-based resistant individuals could be identified within the SLR31 population (Vila-Aiub et al., 2005). A large number of individuals were asexually cloned, and the parent plants and their respective clones were numbered to ensure subsequent identification. All individuals were sprayed with diclofop-methyl and dead individuals were classified as the herbicide susceptible (S) phenotype within the SLR31 population. Individuals resistant to diclofop-methyl were sprayed with sethoxydim. Diclofop-methyl resistant plants that were susceptible to sethoxydim were classified as the P450 phenotype. The remaining fraction of the SLR31 population which survived exposure to diclofop-methyl and then to sethoxydim, was classified as the ACCase/P450 phenotype (Vila-Aiub et al., 2005). Seeds of each phenotype from plants grown under identical conditions were harvested in December 2001 and stored at constant 20°C (Vila-Aiub et al., 2005) before use in experiments.

Growth analysis and biomass allocation during the vegetative phase

In 2002, seeds of the individual S, P450 and ACCase/P450 phenotypes were germinated on 0.7% (w : v) agar (12 h light at 25°C, 12 h dark at 15°C). After 4 d, individual seedlings (2 cm high) were transplanted into pots (9 cm diameter, 13 cm high) filled with an equal weight of washed white sand and supplied with the following nutrient solution: 90 mg kg−1 KH2PO4, 140 mg kg−1 K2SO4, 150 mg kg−1 CaCl2, 40 mg kg−1 MgSO4, 10 mg kg−1 MnSO4, 9 mg kg−1 ZnSO4, 2 mg kg−1 CuSO4, 0.7 mg kg−1 H3BO3, 0.4 mg kg−1 CoSO4, 0.2 mg kg−1 Na2MoO4. The nutrient solution was supplied at half strength at the time of seeding and 1 wk after seeding. Nitrogen (as NH4NO3) was applied (50 mg kg−1) at weekly intervals over the course of the experiment. The field capacity of the sand substrate (12%) was measured before commencement of experiments and pots were watered to field capacity on a daily basis. Plants were grown in a controlled environment facility (phytotron) under natural solar radiation at 20°C/15°C (day/night) and arranged in a completely randomized design. Pots were regularly rearranged to randomize any environmental differences within the phytotron. There were 18–24 replicates per treatment (three phenotypes × two harvest times (29 d and 57 d after seeding)). The experiment was repeated using 25–28 replicates per treatment in 2003.

For both experiments, shoots and roots were harvested 29 d and 57 d after sowing (DAS) (shoots were divided into leaf material and stems including the leaf sheath). Leaf area per plant was determined with a digital leaf area meter (LI-3100; LiCor, Lincoln, Nebraska, USA). Above-ground material and roots were oven dried at 80°C for 72 h, and dry biomass recorded. Relative growth rate (RGR) and its components (net assimilation rate, NAR; specific leaf area, SLA; leaf weight fraction, LWR) were calculated for each treatment combination (phenotype × harvest). A software program developed by Hunt et al. (2002) was used to calculate growth parameters, which are derived according to classical growth analysis:

  • image(Eqn 1 )

(t is time; W is total dry weight per plant; LA is total leaf area per plant; WL is total leaf dry weight per plant). Plant growth traits and derived variables (Hunt, 1982; Poorter & Nagel, 2000) estimated after each harvest, are described in Table 1. At the final harvest (57 DAS), the number of vegetative tillers per plant was recorded.

Table 1.  Measured and estimated vegetative plant growth variables for the S, P450 and ACCase/P450 phenotypes of Lolium rigidum
Attribute (dimension)Symbol or formula
Number of vegetative tillersTV
Root biomass (g)WR
Stem biomass (g)WST
Leaf biomass (g)WL
Leaf area (cm2)LA
Total biomass (g)W = WS + WR + WL
Relative growth rate (RGR) (mg mg−1 d−1)(ln W2 − ln W1)/(t2 − t1)
Specific leaf area (SLA) (cm2 mg−1)LA/WL
Net assimilation rate (NAR) (mg cm−2 d−1)((W2 − W1)/(T2 − T1))((ln2W − ln1W)/(2LA − 1LA))
Leaf weight ratio (LWR) (mg mg−1)WL/W
Root weight ratio (RWR) (%)WR/W
Stem weight ratio (SWR) (%)WST/W

Statistical analysis

The unbiased formula proposed by Hoffmann and Poorter (2002) was used to calculate RGR. The variance (σ2 or V) associated with RGR was estimated with Causton and Venus's formula (1981):

  • image(Eqn 2 )

(ln W2 is the mean of the ln-transformed plant weight at time 2; ln W1 is the mean of the ln-transformed plant weight at time 1). The degrees of freedom associated with RGR and its components is n − 2, where n is the total number of plants used in two harvests.

One-way analyses of variance (anova) were performed to compare RGR and its components for herbicide susceptible and resistant L. rigidum phenotypes. Tukey's honestly significant difference (HSD) test was used to compare mean values (α = 5%).

Multivariate one-way analysis of variance (manova) was conducted to test for the effect of phenotype on correlated dependent growth traits measured on single plants (number of vegetative tillers, leaf area, total biomass, and root, leaf and stem weight ratios) harvested 57 DAS (2002 and 2003 experiments). If multivariate tests were significant at P < 0.05, one-way anovas were performed individually on each growth trait. To comply with the assumptions of normal distribution and homoscedasticity, data for number of tillers, leaf area and total biomass were log-transformed (y = log10 x) , and root, leaf and stem weight ratios were angular-transformed (y = arcsin √x) (Sokal & Rohlf, 1969) before manova and anova. Where appropriate, means were separated using Tukey's HSD test (α = 5%).

Photosynthesis and gas exchange

Leaf CO2 assimilation rates (µmol m−2 s−1) were determined using a LiCor 6400 gas exchange apparatus. Photosynthetic responses were measured at photon flux densities between 0 and 1500 µmol m−2 s−1 (6400-02B red-blue LED light source). Seven plants per phenotype were randomly selected before the final harvest of the 2003 experiment for these measurements. Photosynthetic gas exchange was measured on one fully expanded leaf at three 60-s intervals. The gas analyser was calibrated to provide constant conditions of 20°C, 360 µmol CO2 mol−1 (6400-01 CO2 injector system) and 500 µmol s−1 air flow rate. The response of CO2 assimilation rate to photon flux was described using a rectangular hyperbola model (Long & Hällgren, 1993):

  • image(Eqn 3 )

(A is the leaf CO2 exchange rate at x light intensity; Asat is the light-saturated rate of CO2 uptake (i.e. maximum photosynthesis); Kq is the light intensity of photosynthetically active radiation (PAR) at which photosynthesis is half of the light saturated maximum (Asat)). The nonlinear least squares regression curves were generated with sigmaplot version 6.0 (SPSS Science, Chicago, IL, USA) after transforming data (y = √x + 6). Comparison of model parameters between the phenotypes was performed by anova and means were separated by Tukey's HSD test (α = 5%) (graphpad prism version 3.0; GraphPad Software, Inc., San Diego, CA, USA).

Biomass allocation during the reproductive phase

Glasshouse experiment  Seeds of the S, P450 and ACCase/P450 phenotypes were germinated on 0.7% agar, as previously described, and after 3 d seedlings (2-cm high) transplanted into pots (17-cm diameter, 14-cm high) containing washed white sand (after 3 d). Mineral nutrients and N were supplied as described for the 2002 vegetative growth experiment. Plants were grown in controlled glasshouse conditions (mean temperatures were between 17 and 24°C) with natural solar radiation. Plants (n = 24–30) of each phenotype were arranged in a completely randomized design. Daily watering to field capacity was as described for the previous experiment.

Before flowering, cross-pollination between phenotypes was prevented by grouping plants of the same phenotype within pollen-proof enclosures. At maturity, seed heads were harvested from individual plants and the number of reproductive tillers was counted. Total above-ground biomass was determined after drying plants at 80°C for 72 h. Seed heads (spikes) were then threshed and total seed mass was measured. The number of seeds produced per plant (Sn) was estimated as:

  • image(Eqn 4 )

(TSw is the total seed weight produced per plant; Sw is the mean weight of 50 seeds per plant; n = 3). Individual seed weight was determined from the average weight of 50 seeds. Harvest index (HI) (%) per plant was calculated as the ratio of seed mass to total above-ground biomass (seed mass + vegetative biomass).

Field experiment  Individual seedlings (2 cm high) of the three phenotypes (S, P450, ACCase/P450) were transplanted into large pots (25 cm diameter, 23 cm high) containing a standard potting mix (50% peat moss and 50% river sand). Plants of each phenotype (20 replicates) were grown outdoors during the normal growing season for L. rigidum (July–November) in a completely randomized design. Plants were fertilized with a slow-release fertilizer (Macrocote: Blue Plus; Langley Chemicals, Welshpool, WA, Australia) at a rate of 12 g per pot (w : w nutrient concentrations: N 16.3% (NH2 8.4%, NH4 6.4%, NO3 1.5%); phosphorus (P) 4%; potassium (K) 10%; sulphur (S) 5%; magnesium (Mg) 0.63%; iron (Fe) 0.20%; copper (Cu) 0.03%; zinc (Zn) 0.03% and manganese (Mn) 0.08%) and with three applications of liquid fertilizer (N 19% (NH2 15%, NH4 1.9%, NO3 2.1%); P 8%; K 16%, Mg 1.2%; S 3.8%; Fe 0.4; Mn 0.2 g l−1; Zn 0.2 g l−1; Cu 0.1 g l−1; B 0.1 g l−1 and Mo 0.01 g l−1). Plants were watered regularly. Pollen proof enclosures were erected before flowering, as described previously. Plants were harvested as described for the glasshouse experiment.

Statistical analysis

The effect of phenotype on reproductive traits was assessed by multivariate one-way analysis of variance (manova). To comply with assumptions of normal distribution and homoscedasticity, harvest index values were angular transformed (y = arcsin √x) and values for other experimental variables were log-transformed (y = log10 x or y = log10 x + 1) (Sokal & Rohlf, 1969). Means were compared using Tukey's HSD test where appropriate (α = 5%).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Growth analysis, biomass allocation and photosynthesis during the vegetative phase

A classical growth analysis was performed on plants of the S, P450 and ACCase/P450 phenotypes grown under nutrient and temperature controlled conditions. The experiment was conducted in 2002 and repeated in 2003. A manova showed a significant (P < 0.001) effect of phenotype on dependent growth variables estimated 57 DAS for both experiments, accounting for 58% (1 – Wilk's lambda) and 46% of the total variance in 2002 and 2003, respectively.

Herbicide susceptible (S) vs metabolic (P450) resistant phenotype

In the 2002 experiment, the resistant P450 phenotype produced significantly less total biomass (at 57 DAS) than plants of the S phenotype (Table 2). Leaf area and number of vegetative tillers produced per plant did not differ between the two phenotypes (Table 2). The P450-based resistant plants had a significantly lower mean RGR than S plants during the 29–57 DAS interval (Table 3). This differential response was correlated with a lower NAR in the P450 phenotype (Table 3). Differences in resource allocation were observed: P450 resistant plants allocated 5% less resources to roots (RWR) (Fig. 1a) and 5% more to leaves (Fig. 1b). There were no differences in stem weight ratio and SLA between the two phenotypes (Fig. 1c).

Table 2.  Mean total biomass, leaf area and number of vegetative tillers at 57 d after sowing (DAS) for the S, P450 and ACCase/P450 phenotypes of Lolium rigidum growing under temperature- and nitrogen-controlled conditions (2002 and 2003)
Growth traits2002 Experiment (n = 18–24)2003 Experiment (n = 25–28)
SP450ACCase/P450PSP450ACCase/P450P
  1. P-values from one-way anova on individual traits. Different superscript letters indicate significant differences between mean values within rows for each experiment according to Tukey's HSD (α= 0.05). Values in parentheses denote SE of the mean.

Total biomass (mg)9044a (546)6792b (479)6859b (326)< 0.0019426a (430)8113b (321)8252b (358)0.02
Leaf area (cm2)1019 (36) 974 (57)1013 (61)   0.59 946 (43) 993 (38) 908 (49)0.32
Tillers 111 (5.5) 111 (5.8) 115 (5.6)   0.83 134 (8.8) 126 (6.4) 114 (4.7)0.28
Table 3.  Mean estimates of relative growth rate (RGR) and its components for the S, P450 and ACCase/P450 phenotypes of Lolium rigidum growing under temperature and nitrogen controlled conditions (2002 and 2003)
Growth rates and related components2002 Experiment (n = 18–24)2003 Experiment (n = 25–28)
SP450ACCase/P450SP450ACCase/P450
  1. NAR, Net assimilation rate; SLA, specific leaf area; LWR leaf weight ratio. Parameters were estimated between the 29 and 57 d after sowing (DAS) harvest interval. Different superscript letters indicate significant differences within rows for each experiment according to Tukey's HSD (α= 0.05).

29−57RGR (mg mg−1 d−1)0.146a0.130b0.138ab0.150a0.140b0.144ab
NAR (mg cm−2 d−1)1.319a0.981b1.021b1.580a1.282b1.401b
SLA (cm2 mg−1)0.270a0.289a0.273a0.221a0.245a0.224a
LWR (mg mg−1)0.443a0.479b0.504b0.464a0.477a0.474a
image

Figure 1. Biomass allocation (%) to roots (root–weight ratio, RWR) (a and d), leaves (leaf–weight ratio, LWR) (b and e) and stems (stem–weight ratio, SWR) (c and f) for S, P450 and ACCase/P450 phenotypes of Lolium rigidum estimated 57 d after sowing (DAS) (2002 and 2003). Vertical bars denote SE of the mean. Different letters indicate significant differences between mean values according to Tukey's HSD (α = 0.05).

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In the 2003 experiment, the resistant P450 phenotype (57 DAS) again produced significantly less total dry biomass than the S phenotype, and once again there were no differences in leaf area and number of vegetative tillers between phenotypes (Table 2). In common with the 2002 experiment, the P450 phenotype exhibited a significantly lower RGR than the S phenotype (Table 3) which was correlated with a lower NAR (Table 3). Photosynthetic rates (leaf CO2 assimilation rates) at saturating light intensities were consistently lower (P < 0.01) for the P450 phenotype (Fig. 2), as indicated by the value of parameter Asat (Table 4). Light intensities at which photosynthesis rates were 50% of maximum (parameter Kq) were not significantly different between the two phenotypes (Table 4, Fig. 2). Individuals of the S and P450 phenotypes allocated proportionally the same biomass to roots, leaves and stems (Fig. 1d–f, Table 3). The results from the 2002 and 2003 experiments confirm that the P450 phenotype produces less vegetative biomass than the S phenotype and that this difference is the result of a lower RGR, NAR and CO2 assimilation rate.

image

Figure 2. Leaf CO2 assimilation rate plotted against photosynthetic photon flux density (PPFD) for the S (open circles), P450 (open squares) and ACCase/P450 (closed squares) phenotypes of Lolium rigidum. Photosynthetic rates (n = 7) were measured for single fully expanded leaves in an atmosphere enriched with 360 mmol CO2 mol−1 at constant 20°C and 500 µmol s−1 air flow rate. Vertical bars denote SE of the mean.

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Table 4.  Parameter estimates and regression coefficients for the hyperbolic model (y = Asatx/(Kq + x)) describing leaf CO2 assimilation rate (rate of photosynthesis) in response to light intensity for the S, P450 and ACCase/P450 phenotypes of Lolium rigidum
PhenotypeParameter  
AsatKqR2
  1. Data were square root transformed and parameter estimates and their 95% confidence limits (values in parentheses) were back-transformed. Different superscript letters denote significant differences between the phenotypes by Tukey's HSD test (P ≤ 0.05).

S36.1a (33.5–38.8)81.3a (64–99)0.79
P45030.8b (29.3–32.4)71.1a (60–82)0.88
ACCase/P45032.6b (30.1–35.1)79.9a (62–98)0.78

Herbicide susceptible (S) vs multiple resistant (ACCase/P450) phenotype

At 57 DAS (2002 experiment), ACCase/P450 plants accumulated significantly less total biomass than S plants (Table 2). No differences in the number of vegetative tillers or leaf area were observed between the phenotypes (Table 2). A lower RGR was recorded for the ACCase/P450 phenotype, although this difference was not statistically significant (Table 3). As observed for the P450 phenotype, ACCase/P450 individuals showed significantly lower NAR than the S individuals (Table 3). The ACCase/P450 phenotype exhibited a higher LWR (Fig. 1b) and partitioned proportionally less resources to roots and stems than the S phenotype (Fig. 1a,c).

In the 2003 experiment, as observed in 2002, the ACCase/P450 phenotype accumulated less total dry matter than the S phenotype, and leaf area and number of vegetative tillers produced were not significantly different (Table 2). The ACCase/P450 phenotype had a significantly lower NAR (Table 3) and a marginally reduced CO2 assimilation rate in relation to the S phenotype (P = 0.05) (Table 4, Fig. 2). Accordingly, the ACCase/P450 phenotype exhibited a reduced RGR, though the difference between phenotypes was not statistically significant (Table 3). Individuals of the resistant phenotype allocated proportionately less resources to stems (Fig. 1f).

Metabolic (P450) vs multiple resistant (ACCase/P450) phenotype

In both experiments, there were no significant differences in total dry matter acquisition or allocation to different organs between the two resistant phenotypes (Table 2, Fig. 1). Growth analysis revealed that individuals of the P450 and ACCase/P450 phenotypes had similar RGR, which correlated well with similar net assimilation (Table 3) and photosynthetic rates (Table 4, Fig. 2).

Biomass allocation during the reproductive stage

Two independent experiments were conducted to assess reproductive growth traits for plants of the S, P450 and ACCase/P450 phenotypes grown under glasshouse (2002) and field conditions (2003). For the glasshouse experiment, manova analysis identified a significant (P = 0.0015) overall effect of phenotype on reproductive growth attributes. However, when analysed individually by anova, the only significantly different trait was the number of reproductive tillers, with the S phenotype producing significantly more tillers than either resistant phenotype (Table 5). There were no significant differences between phenotypes in total seed mass, the number of seeds produced or individual seed weight (Table 5).

Table 5.  Mean values for reproductive traits of the S, P450 and ACCase/P450 phenotypes of Lolium rigidum under glasshouse and field conditions
Reproductive growth traitsGlasshouse experimentField experiment
SP450ACCase/P450PSP450ACCase/P450P
  1. P-values from one-way anova on individual traits. Different superscript letters indicate significant differences between phenotypes within rows according to the Kruskal–Wallis test (α= 0.05). Values in parentheses denote SE of the mean.

Number of reproductive tillers 169a (8.8) 142a (8.3) 116b (4.7)< 0.001 133 (7.4) 120 (8.9) 117 (7.2)0.25
Total seed mass (g)   9.3 (0.7)  10.3 (0.6)   9.3 (0.8)   0.40  20.3 (1.2)  18.6 (1.1)  20.9 (1.2)0.44
Harvest index (%)  16.6 (1.1)  18.6 (0.9)  17.0 (1.1)   0.35  29.7 (1.5)  28.3 (0.9)  30.4 (1.1)0.52
Seed number5310 (292)5643 (351)5213 (456)   0.389847 (835)8488(516)9600 (689)0.28
Individual seed weight (mg)   1.79 (0.07)   1.86 (0.07)   1.83 (0.06)   0.83   2.35 (0.4)   2.23 (0.1)   2.07 (0.1)0.82

In the field experiment, manova did not reveal any significant (P = 0.60) effect of phenotype on reproductive traits of individuals of the S, P450 and ACCase/P450 phenotypes. Reproductive output for all phenotypes was higher than observed in the glasshouse experiment (except for number of reproductive tillers) but was not significantly different between phenotypes (Table 5).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This study conducted with a single multiple-resistant L. rigidum population (SLR31) has identified a consistent reduction in vegetative biomass (resource acquisition) in the herbicide-resistant P450 and ACCase/P450 phenotypes compared with the susceptible phenotype (S > P450 = ACCase/P450) after 57 d growth (Table 2). This indicates a physiological or direct resistance cost associated with P450-endowed herbicide resistance expressed at the vegetative growth stage. This is the first report that a resistance mechanism of enhanced herbicide metabolism results in a physiological cost.

These differences in biomass production during the vegetative stage, did not result in differences in reproductive output (seed mass and seed number) (S = P450 = ACCase/P450) (Table 5). This lack of correspondence between vegetative and reproductive performance has also been noted in some previous studies of herbicide-resistant and -susceptible biotypes (Stowe & Holt, 1988; Reboud & Till-Bottraud, 1991; Purba et al., 1996). Reboud and Till-Bottraud (1991) have suggested that the advantage of herbicide susceptible over resistant individuals may be most apparent during early vegetative growth and that compensation of resistance costs over time may occur in the absence of stressful conditions, leading to similar reproductive outputs. Also, the expression of pleiotropic costs associated with herbicide resistance mechanisms may vary between plant organs (stems, leaves or flowers) and growth stages (Purrington & Bergelson, 1999), making it necessary to evaluate the basis of these costs (which may be related to P450 expression levels) in vegetative and reproductive plant stages.

Variations in RGR necessarily correspond to linear variations in its physiological (NAR) and morphological (SLA and LWR) components. According to previous studies, inherent variations in RGR in terrestrial plants may be alternatively explained by variations in either NAR (Garnier, 1992) or SLA (Poorter & Remkes, 1990; Hunt & Cornelissen, 1997), although other studies have found no clear explanatory correlation between RGR and any of its components (Meziane & Shipley, 1999). In this study, results from two experiments, have shown that significant differences in the RGR of S and P450 phenotypes are well correlated with differences in net assimilation rate (NAR) (Table 3).

In order to limit differences in RGR which resulted from the lower NAR and photosynthetic rate of resistant phenotypes, compensatory changes in LWR were observed. Both resistant phenotypes allocated more resources to leaves (LWR) than the S phenotype, in order to maximize total carbon fixation. This morphological adjustment was not sufficient to compensate for their intrinsically lower NAR, which still resulted in lower total dry matter production (Table 2).

Herbicide resistance costs

The magnitude (%) of resistance costs (1 − (resistant biomass/susceptible biomass) × 100) associated with enhanced P450 herbicide metabolism at the vegetative stage were 25% and 13% for the 2002 and 2003 experiments, respectively. Of course, it is not valid to widely extrapolate from this result as it remains to be established whether the physiological cost accompanying the nontarget site P450-based enhanced herbicide metabolism resistance mechanism observed in this SLR31 L rigidum population is associated with resistance in other L. rigidum populations and other resistant species. With insecticide-resistant insect species, P450-enhanced metabolism has been shown to incur significant resistance costs (Boivin et al., 2003).

Our study suggests that the mutation Ile (1781) Leu in the ACCase gene incurred no measurable physiological cost, as responses of multiple resistant individuals (ACCase/P450) were not significantly different from those of individuals possessing only enhanced P450 metabolism during vegetative and reproductive growth. This Ile (1781) Leu mutation has now been observed in several species (Zhang & Devine, 2000; Brown et al., 2002; Christoffers et al., 2002; Délye et al., 2002a, b) and studies on resistance costs should be conducted with these resistant biotypes to establish cost of this mutation in other resistant populations/species. This result with the ACCase gene Ile (1781) Leu mutation of no resistance cost cannot of course be extrapolated to other mutations of the ACCase gene that endow herbicide resistance. Resistance cost studies need to be conducted with the other known resistance endowing ACCase gene mutations (Délye et al., 2003, 2005). It is acknowledged that in the present study a direct comparison of the fitness of ACCase mutants without the presence of altered P450 metabolism was not possible and is required to unequivocally assert the absence of a resistance cost for this ACCase mutation. Two studies found no direct costs of resistance to ACCase inhibiting herbicides (APP-CHD) in Digitaria sanguinalis (Wiederholt & Stoltenberg, 1996a) and Setaria faberi (Wiederholt & Stoltenberg, 1996b). Although is probable that these resistant biotypes had a target site mutation, the lack of knowledge of the molecular basis of resistance and the comparison of resistant and susceptible individuals with dissimilar genetic backgrounds precludes a clear comparison with the results of our study.

Despite this apparent lack of a resistance cost associated with the Ile (1781) Leu mutation in this L. rigidum population, other studies have demonstrated significant differences in germination requirements and seedling emergence characteristics for the ACCase phenotype (Vila-Aiub et al., 2005).

Ecological significance of herbicide resistance costs

When they occur, resistance costs associated with alleles endowing herbicide resistance may potentially limit the frequency and alter the evolutionary dynamics of these alleles in weed populations. The extent and consequences of these costs will depend on the intensity of competitive interactions that will ultimately modify plant fitness. These competitive interactions will interplay with environmental conditions and life history traits to modify the intensity of plant competition and ecological fitness relations between resistant and susceptible components of weed populations (Whittaker et al., 1973; Harper, 1977; Purba et al., 1996).

The extent and magnitude of any physiological cost associated with herbicide resistance mechanisms is an important parameter influencing the preselection frequency of resistance in populations and the subsequent dynamics of resistance during and after herbicide selection. Knowledge of resistance costs is invaluable in predictive models of herbicide resistance dynamics under various management scenarios (Gressel & Segel, 1990; Maxwell et al., 1990; Diggle & Neve, 2001; Neve et al., 2003). The results of this study suggest that attempts to predict the rate of evolution of herbicide resistance should consider that the potential expression of resistance costs may vary depending on the mechanism of resistance and the plant life stage involved. Based on predictions from asymmetric competition (Shipley & Keddy, 1994), in which large individuals obtain more resources than small plants and size variability becomes magnified over time (i.e. large plants suppress small plants) (Weiner, 1986), individuals possessing an enhanced P450 metabolism might potentially be at a disadvantage when competing with herbicide-susceptible individuals. Agronomic practices such as the inclusion of competitive crops and pasture phases that exploit the reduced growth of individuals with P450-enhanced herbicide metabolism may help to select against herbicide-resistant individuals and moderate rates of resistance evolution in the field.

In conclusion, the data presented in this study provide the first evidence for a resistance cost associated with a P450-based nontarget site herbicide detoxification mechanism and the absence of a resistance cost for a target site ACCase gene mutation (Ile (1781) Leu). This information is needed for use in predictive modeling and in designing resistance management strategies.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

WAHRI is funded by the Australian Grains Research and Development Corporation (GRDC). An International Postgraduate Research Scholarship supported M. M. V. A. during this research at The University of Western Australia. The authors thank Dr K. Steadman and Dr M. Walsh for critical reading of the manuscript.

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  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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