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Characterizing genotype specific differences in survival, growth, and reproduction for field grown, rapid cycling Brassica rapa
Environmental and Experimental Botany 55 (2006) 61–69
Characterizing genotype specific differences in survival, growth, and reproduction for field grown, rapid cycling Brassica rapa Martin G. Kelly∗ Department of Biology, Buffalo State College (SUNY), 1300 Elmwood Avenue, Buffalo, NY 14222-1095, USA Accepted 30 September 2004
Abstract Rapid cycling Brassica rapa (RCBr) develops rapidly, and has both small adult size and a brief life cycle. Yet, in spite of many investigations using RCBr, extremely few plant ecologists have used this plant in the field. This study is the first to describe the genotype specific variation in traits describing survival, growth, and reproduction for field grown, RCBr. I also identify traits associated with fitness. Five genotypes of RCBr were used: standard, anthocyaninless, yellow-green, anthocyaninless and hairless, and anthocyaninless and yellow-green. Plants were grown outside in a “common garden”. Eight plant traits were measured: life span, height, growth rate, leaf size, number of flowers and fruits, fruit set, and fitness. All traits, except life span, differed significantly among the five plant genotypes. Correlation analysis revealed that fitness increased as each of these of seven plant traits increased. This study demonstrates that RCBr can serve as a model organism in ecological field studies. © 2004 Elsevier B.V. All rights reserved. Keywords: Field ecology; Rapid cycling Brassica rapa; RCBr; Model organism
1. Introduction One species that botanists have broadly employed in all areas of research is Brassica rapa (syn. campestris). This species is endemic throughout Europe eastward to Siberia (Warwick and Francis, 1994, Part V, pp. 11, 19). This plant species is also widely cultivated in cooler climates (Pak Choi, Turnip Rape, Choy Sum, Chinese Cabbage, Tendergreen, Turnip, Sarson, and Broccoli Raab; Williams and Hill, 1986). Moreover, ∗
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B. rapa has established weedy and naturalized populations in North and South America, Australia, and Asia (Warwick and Francis, 1994, Part V, p. 19). Thus, many field studies have been done with it to understand the consequences of field release of genetically modified organisms (Jorgensen and Andersen, 1994; Hauser et al., 1997, 1998a, 1998b; Snow and Palma, 1997; Snow et al., 1999; Pertl et al., 2002; Halfhill et al., 2003; Zhu et al., 2004). Of special importance to this investigation is rapid cycling B. rapa (RCBr). RCBr was derived using classical methods of artificial selection and breeding (Williams and Hill, 1986). Under optimal conditions
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Table 1 Selected ecological studies conducted with Brassica rapa presented in chronological order References
Brassica rapa
Experiments in
Miller and Schemske (1990) Agren and Schemske (1992) Agren and Schemske (1993) Agren and Schemske (1994)
Rapid cycling Rapid cycling Rapid cycling Naturalized
Jorgensen and Andersen (1994) Miller (1995) Nakamura et al. (1995) Schmitt et al. (1995) Davis et al. (1996) Gurevitch et al. (1996) Klaper et al. (1996) Miller (1996) Mitchell-Olds (1996)
Naturalized
Growth chamber Growth chamber Growth chamber Field and greenhouse Field
Pilson (1996) Hauser et al. (1997) Hauser et al. (1998b) Hauser et al. (1998b) Siemens and Mitchell-Olds (1998) Stowe (1998) Kleier et al. (1999) Sleeman and Dudley (2001) Pertl et al. (2002) Siemens et al. (2002) Hauser et al. (2003) Kelly and Terrana (2004)
Naturalized Naturalized Naturalized Naturalized Naturalized
Rapid cycling Naturalized Rapid cycling Rapid cycling Rapid cycling Rapid cycling Naturalized Naturalized
Rapid cycling Rapid cycling Rapid cycling Naturalized Naturalized Cultivar Rapid cycling
Greenhouse Field Greenhouse Growth chamber Greenhouse Greenhouse Greenhouse Greenhouse and growth chamber Field Field Field Field Field and growth chamber Laboratory Growth chamber Greenhouse Field Field Field Field
RCBr has a life cycle of 35–40 days, from parental seed sown to offspring seed harvest (Williams and Hill, 1986). Compared to normal B. rapa, which can produce two generations in a year, under optimal conditions RCBr can produce 10 generations in a year. Thus, the potential applications of RCBr to experimental botany are diverse (Musgrave, 2000). After Williams and Hill (1986) summarized the results of their selection for rapid cycling Brassica species, plant ecologists were among the early adopters. Ecologists have since employed both naturalized and rapid cycling varieties of B. rapa in a wide variety of studies (Table 1). Though Miller and Schemske (1990) were the first to publish ecological research using RCBr, almost all of our knowledge about the growth and development of RCBr is based on plants raised in controlled environments (Table 1). However, Torresen and Lotz (2000) in a direct comparison of B.
rapa grown in growth chambers and the field concluded that it was not possible to use the phenotypic outcomes for plants grown in growth chambers as predictors for plants grown in the field. Though several ecological investigations have used RCBr, ecologists—except Kelly and Terrana (2004)—have not employed RCBr in the field (Table 1). Before RCBr can be applied as a model organism to more substantial ecological questions two points must be established. First, that ecologically relevant phenotypic variation exists in field grown plants. Second, that rapid cycling forms of this species grown in a natural setting can be used as model for native or naturalized B. rapa. Here, I characterize the growth of RCBr in a field setting. I identify some individual traits associated with plant survival and reproduction in a field grown population of RCBr. I see this fundamental goal only as a first step. Ultimately, the second goal must be explicitly met before ecologists adopt RCBr as a model organism for native or naturalized B. rapa in experiment based, field research.
2. Materials and methods 2.1. Plant material RCBr was derived from a global collection of B. rapa (L.) varieties (Williams and Hill, 1986). Plants were selected for the following six qualities: reduced size at maturity, minimum time from germination to flowering, uniformity of age at first flowering, high flower production, rapid maturation of seeds, and lack of seed dormancy (Tomkins and Williams, 1990). Individuals that flowered fastest were used as the base population. These individuals were out-crossed to generate seeds. In the next generation, the 10% of the offspring population that flowered first were selected as parents. These 288, or more, plants were mass pollinated to produce the next generation of seeds; artificial selection continued until the response to selection was stabilized (Williams and Hill, 1986). Under optimal laboratory conditions, RCBr flowers within 16 days of seed germination (Williams and Hill, 1986). It is important to recognize that RCBr retains considerable isozymic variation (Williams and Hill, 1986). In addition, when inbreeding is forced, fitness is significantly reduced (Evans, 1991). Both outcomes suggest
M.G. Kelly / Environmental and Experimental Botany 55 (2006) 61–69
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Table 2 Five genetically defined varieties of RCBr known to differ in phenotype Variety
Genotype
Phenotype
Standard
Rbr/Rbr
Normal chlorophyll production, anthocyanin expression, and epidermal hair density
Anthocyaninless
anl/anl
Normal chlorophyll production and epidermal hair density, lacks anthocyanin
Yellow-green
ygr/ygr
Deficient in chlorophyll production, normal anthocyanin expression and epidermal hair density
Anthocyaninless and hairless
anl/anl and Hir (0–1)
Normal chlorophyll production, lacks anthocyanin and has a very reduced number of epidermal hairs
Anthocyaninless and yellow-green
anl/anl and ygr/ygr
Deficient in chlorophyll production and lacks anthocyanin, has normal epidermal hair density
that B. rapa did not pass through a genetic bottleneck in order to establish the rapid cycling lines. RCBr are available in a wide variety of known genotypes with distinct phenotypes. For this experiment, I compared five self-incompatible genotypes known to differ in plant pigmentation and trichome production (Table 2). I chose phenotypic traits that alone, or in combination, might affect plant survival or reproduction. I used 48 seeds per genotype, for a total of 240 seeds. I used seeds purchased from Carolina Biological Supply Company (USA). RCBr seed stocks for these same five self-incompatible genotypes may also be purchased from Blades Biological Ltd. (UK). 2.2. Field study design Plants were grown outside in a “common garden” experiment and experienced the same general growing conditions. Over the course of the experiment mean daily high and low air temperatures were 24.1 and 14.6 ◦ C, and mean daily precipitation was 0.2 cm. Weather data were recorded at a station located 2.3 km from the field site. The total experimental area was watered and fertilized three times per week with 30 L of water containing all-purpose fertilizer (water soluble, 20–20–20 with micronutrients) at the concentration recommended for outdoor use (4 cc/L). Six plots were laid out in the most compact arrangement possible (2 × 3). Plots were laid out on August 29, 2002. The total experimental area was 1.6 m wide and 1.1 m tall. Five plots were used for this experiment; the sixth plot contained a different set of seeds for another project. To reduce the chance effect of local environ-
mental differences, seeds were randomly assigned to one of the five plots and to a grid position in the plot. Seed sowing positions were marked as a grid with a #804 SoilMasterTM dibble board (6 × 8 pegs spaced 4.4 cm× 6.4 cm apart). Each 6 × 8 plot of 48 seeds was 53.5 cm wide and 27 cm tall. All seeds were sown on August 29, 2002. Experimenter sown seeds were used for two reasons: (1) the randomization of individuals across the environment minimizes the correlation between genotype and environment, or the correlation of the phenotype with the environment because of past environmental effects (Mitchell-Olds and Shaw, 1987) and (2) Wade and Kalisz (1990) convincingly argued that the application of quantitative methods to measure natural selection was not sufficient to determine why selection operated in the manner observed; they recommended the use of experimental manipulation to identify the agents of viability and fecundity selection as a complement to quantitative analysis. Six students in a Plant Ecology course collected data. This semester long course met one evening per week (Fall 2002). The site was surveyed every week to monitor plant presence (September 5–October 17). Each seed’s place was marked with an individually numbered plastic stake. Death was recorded when a plant could not be located at the base of its marker, or the plant had browned and turned brittle. Individual plants were identified every census; the plant’s marker was not removed until the end of the experiment (October 17th). The experiment was terminated and all plants harvested three weeks after the last fruits were initiated.
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2.3. Individual traits measured Eight traits were measured for each plant. Life span was the total days from emergence to death. As plants were measured across time, some data were treated as repeated measures for analysis. For example, plant height was measured at 14, 21, 28, 35, and 42 days; these five measures of height across time were used as repeated measures of height per plant (height, mm). Similarly, knowing plant height at 14, 21, 28, 35, and 42 days permitted me to calculate the growth rate for each plant over time. Growth rates over 14, 21, 28, 35, and 42 days were treated as repeat measures of growth rate per plant (growth rate, mm/day). At 14 days, each plant’s largest cotyledon was measured for length and width. Based on the cotyledon’s heart shape (Tomkins and Williams, 1990), linear size was used to calculate the area of the cotyledon based on the formula for a cardioid (Harris and Stocker, 1998, p. 323). Similarly at 21 days, each plant’s largest leaf was measured for length and width. Based on the leaf’s oval shape (Tomkins and Williams, 1990), leaf length and width were used to calculate the area of the leaf based on the formula for an ellipse (Harris and Stocker, 1998, p. 93). As these two measures of area (cotyledon and leaf) were correlated (r = 0.734, P < 0.0001, N = 111) they were used as repeat measures of leaf size (mm2 ). If a plant flowered, the number of flowers present on a given survey date were counted. Flowers were counted for each flowering plant on September 19th and 26th, and October 3rd and 10th. These four measures of flower production per plant were used as repeat measures of flowering per plant (flowers). If a plant fruited, the number of fruits present on a given survey date were counted. Fruits were counted for each flowering plant on September 26th, and October 3rd and 10th. These three measures of fruit production per plant were treated as repeat measures of fruiting per plant (fruits). The ability of an individual plant to convert its flowers into fruits was measured by its average, proportional fruit set (fruit set). The number of seeds per mature fruit differed across genotypes (F1,3 = 44.5, P = 0.006). Seed set per fruiting plant was calculated by multiplying the number of fruits per plant by the average number of seeds per fruit on a genotype specific basis. The average numbers of seeds per fruit for each genotype were: anl/anl (3.8, S.E. = 0.58, N = 21 fruits), anl/anl and Hir 0-1 (4.9, S.E. = 0.47, N = 29 fruits),
Rbr/Rbr (6.7, S.E. = 0.58, N = 42 fruits), and ygr/ygr (7.0, S.E. = 0.0, N = 2 fruits). If a plant grew but did not fruit, its fitness (seeds per plant) was set to zero. 2.4. Statistical methods The mean and its standard error, and sample size (N) for each measured trait was calculated. Repeated measures analysis of variance (ANOVA) compared the five genotypes to determine if they differed in the average value for a trait measured across time (StatView Reference, 1999, p. 83). The null hypothesis was that there were no differences in RCBr traits associated genotype. Pearson correlation analysis of life history variables with Fitness was performed. The statistic (r) measures the proportionate linear increase or decrease of two variables. The null hypothesis was that there was no correlation between the two variables being compared (r = 0). To determine if a correlation coefficient differed significantly from zero, the correlation statistic was transformed into a variable (z) with a standardized normal distribution; this gives the probability that r = 0 (StatView Reference, 1999, p. 44). For fruit set (a proportion) the Kruskal–Wallis test was used as nonparametric equivalent to the one-way ANOVA comparing three or more groups (StatView Reference, 1999, p. 121). The null hypothesis was that the distribution of fruit set was equivalent for the five genotypic groups of RCBr. Both H and P as reported were corrected for the effect of ties. Similarly, Spearman correlation analysis of fruit set with fitness was performed (StatView Reference, 1999, p. 121). This nonparametric test measures the linear increase or decrease of ranks for two variables. The null hypothesis was that there was no correlation between fruit set and fitness (ρ = 0). Both ρ and P as reported were corrected for the effect of tied ranks.
3. Results The average level of germination in the field for RCBr was 66.7%. Germination ranged from 50.0% for the anthocyaninless mutant to 79.2% for the yellowgreen mutant. Standard, RCBr seeds germinated at a level of 62.5%. There were no genotype specific differences in the level of seed germination (G = 3.86, 2 χcritical = 9.49). On germination, a field grown RCBr
22.2 ± 2.1 (N = 18) 72.0 ± 9.2 (N = 59v 69.8 ± 3.2 (N = 126) 2.6 ± 0.1 (N = 126) 1.6 ± 0.3 (N = 92) 0.6 ± 0.3 (N = 32) 13.4 ± 0.05 (N = 32) 1.3 ± 0.6 N = 38 20.0 ± 2.3 (N = 14) 75.0 ± 8.9 (N = 50) 58.7 ± 2.7 (N = 104) 2.4 ± 0.1 (N = 104) 0.9 ± 0.2 (N = 76) 0.5 ± 0.2 (N = 17) 13.5 ± 0.07 (N = 17) 0.6 ± 0.4 N = 32 25.2 ± 2.8 (N = 10) 96.8 ± 8.9 (N = 66) 93.0 ± 4.1 (N = 141) 3.6 ± 0.1 (N = 141) 3.2 ± 0.4 (N = 105) 2.0 ± 0.2 (N = 57) 49.6 ± 0.05 (N = 57) 6.2 ± 1.2 N = 37 Life span (days) 21.6 ± 3.3 (N = 12) 26.0 ± 5.2 (N = 7) Leaf size† (mm2 ) 172.0 ± 30.0 (N = 45) 112.5 ± 16.5 (N = 39) 112.5 ± 5.7 (N = 101) 102.4 ± 5.8 (N = 93) Height† (mm) Growth rate† (mm/day) 4.3 ± 0.2 (N = 101) 3.8 ± 0.2 (N = 93) Flowers† (number/flowering plant) 4.3 ± 0.6 (N = 77) 4.9 ± 0.5 (N = 71) Fruits† (number/flowering plant) 2.9 ± 0.5 (N = 43) 3.2 ± 0.5 (N = 41) 53.0 ± 0.13 (N = 43) 48.5 ± 0.06 (N = 41) Fruit Set† (%/flowering plant) Fitness† (seeds/plant) 10.6 ± 3.2 (N = 30) 7.5 ± 1.8 (N = 24) Values are mean ± S.E. (N = the number of measurements made). † The compacted variable analyzed by repeated measures ANOVA.
anl/anl and ygr/ygr anl/anl and Hir (0–1) anl/anl Rbr/Rbr Trait
Table 3 Genotype specific means in survival, growth, and reproduction for field-grown RCBr
plant lived an average of 22.5 days (Table 3). Though average life span ranged from 20.0 days (anthocyaninless and yellow-green mutants) to 26.0 days (anthocyaninless mutants), life span did not vary significantly with genotype (F1,4 = 0.642, P = 0.6349). At 14 days, the area of one cotyledon averaged 67.8 mm2 in size. The average size for one cotyledon ranged from 51.2 mm2 (yellow-green mutants) to 99.3 mm2 (standard RCBr). Similarly at 21 days, the area of the largest leaf averaged 147.8 mm2 . Average leaf size ranged from 72.0 mm2 (yellow-green mutants) to 172.0 mm2 (standard RCBr). Differences in average leaf size (mm2 ) were found to be significantly associated with genotype (F1,4 = 4.763, P = 0.0014). Average plant height for field grown, RCBr plants was 86.6 mm. Average plant height ranged from 58.7 mm (anthocyaninless and yellow-green mutants) to 112.5 mm (standard RCBr, Table 3). Genotype specific differences in average height were found (F1,4 = 9.523, P < 0.0001). Similarly, growth rate (mm/day) for these B. rapa plants was 3.3 mm/day. Average growth rate ranged from 2.4 mm/day (anthocyaninless and yellow-green mutants) to 4.3 mm/day (standard RCBr, Table 3). Growth rate was also found to differ on average for these five genotypes of RCBr (F1,4 = 8.825, P < 0.0001). The average number of flowers per flowering plant for field grown, RCBr plants was 2.9 flowers. Average flower number per flowering plant ranged from 0.9 flowers (anthocyaninless and yellow-green mutants) to 4.9 flowers (anthocyaninless mutants, Table 3). Average flower production (per flowering plant) was significantly affected by plant genotype (F1,4 = 8.128, P < 0.0001). The average number of fruits per flowering plant for field grown, RCBr plants was 2.1 fruits. Average fruit number per flowering plant ranged from 0.5 fruits (anthocyaninless and yellow-green mutants) to 3.2 fruits (anthocyaninless mutants, Table 3). A significant genotype specific affect on the number of fruits per flowering plant was observed (F1,4 = 3.493, P = 0.0159). The average level of fruit set in the field was 40.8%. Fruit set ranged from 13.4% (yellowgreen mutant) to 53.0% (standard RCBr, Table 3). The Kruskal–Wallis test showed that fruit set (per flowering plant) differed significantly among genotypes (4 d.f., H = 19.426, P = 0.0006). Average fitness for field grown RCBr was 4.9 seeds per plant. Average fitness ranged from 0.6 seeds per plant (anthocyaninless and
ygr/ygr
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yellow-green mutants) to 10.6 seeds per plant (standard B. rapa, Table 3). Average fitness differed significantly across genotypes (F1,4 = 6.682, P < 0.0001). The correlation of measured traits with fitness revealed that, on average, each of these traits was individually and positively correlated with fitness. As plant life span increased, fitness increased (r = 0.454, N = 61, P = 0.0002). As leaf size increased, fitness increased (r = 0.597, N = 259, P < 0.0001). As plant height increased, fitness increased (r = 0.557, N = 565, P < 0.0001). As plant growth rate increased, fitness increased (r = 0.575, N = 565, P < 0.0001). For flowering plants, as flowers (r = 0.507, N = 421, P < 0.0001) and fruits (r = 0.789, N = 190, P < 0.0001) increased, fitness increased. Lastly, as the proportion of flowers converted into fruits increased, fitness increased (ρ = 0.697, N = 190, P < 0.0001).
4. Discussion For field studies that require known genotypes and phenotypes, there are two taxonomically related plant model systems commonly used. One of these is Arabidopsis thaliana, a small mustard whose entire genome has been sequenced (Arabidopsis Genome Initiative, 2000). For research that requires Brassica species, both cultivated and wild lines of B. oleracea, B. rapa, and B. napus are available. The relatedness of Brassica species to A. thaliana has permitted the identification of consensus genetic markers (Brunel et al., 1999). In addition gene for gene alignment has been observed between B. oleracea and A. thaliana (Li et al., 2003). Thus, the close relation of Brassica species to A. thaliana makes the molecular genetic tools derived for A. thaliana transferable to RCBr. If these model systems exist and are commonly used in field studies, does RCBr have anything to offer plant ecologists? Like A. thaliana, RCBr is characterized by its rapid development, brief life cycle, small adult size, and the wide availability of defined genotypic lines. Because RCBr has such a brief life span (22.5 days), it is readily possible to study the evolving genetic structure of RCBr populations in the field in a reduced amount of time. More importantly, can RCBr provide any advantages over A. thaliana in ease of use? Many experimental manipulations in plant ecology rely on breeding designs to generate seeds. Weinig (2002)
measured flower traits for both A. thaliana and RCBr grown in the same controlled environments. From her data, I calculate that flowers of RCBr are three times larger than flowers from A. thaliana. This would facilitate any hand-pollinations needed in the production of progeny or families. In addition, the fruits and seeds produced from RCBr’s larger flowers are larger than the fruits and seeds produced by A. thaliana. Average fruit length in standard RCBr is 37.3 mm (Kelly, unpublished data). While, fruit length across A. thaliana genotypes ranges on average between 9.5 and 15.5 mm (Myerscough and Marshall, 1973; Alonso-Blanco et al., 1999). Average RCBr seed mass (standard genotype) is 1.9 mg (Kelly, unpublished data). Whereas, average seed mass across A. thaliana genotypes ranges between 0.0196 and 0.0028 mg (Myerscough and Marshall, 1973; Alonso-Blanco et al., 1999). Larger flowers, fruits, and seeds in RCBr are easier to handle, count, and measure than are the smaller flowers, fruits, and seeds of A. thaliana. If RCBr is to be applied as a model organism to more substantial ecological questions, it must be established that ecologically relevant phenotypic variation exists in field grown RCBr plants. All of the traits measured, except life span, differ significantly among five genotypes of RCBr grown in the field. In addition, life span, leaf size, height, growth rate, flowers, fruits, and fruit set are each positively correlated with fitness for these RCBr plants. As these traits increase in amount, fitness increases. Murren et al. (2002) while studying phenotypic integration in six species of Brassica (all rapid cycling varieties) determined that plant height and fruit number were positively correlated in RCBr. Byers (personal communication, 2004) used exploratory path analysis to quantify phenotypic selection on life history traits in RCBr. She investigated the effect of different nutrient environments in orienting the path between life history traits. At very low Nitrogen (4.7 ppm), earliness in leafing, earliness in flowering, and leaf size at flowering were positively related to fruit number (Diane Byers, personal communication, 2004). At low Nitrogen (18.8 ppm) earliness in leafing and flower number were positively linked to fruit number (Diane Byers, personal communication, 2004). At high Nitrogen (150 ppm) leaf size at flowering and flower number were positively related to fruit number (Diane Byers, personal communication, 2004).
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Before ecologists adopt RCBr as a model for native or naturalized B. rapa in field research, its ability to respond comparably must be established. Torresen and Lotz (2000) in a comparison of B. rapa grown in growth chambers to the field concluded “It is not possible to use directly growth reactions obtained in growth chambers for field conditions”. Ideally, the reliability of RCBr will be established by performing parallel field trials employing RCBr and naturalized B. rapa, and measuring the same plant traits in the same way. With this type of comparison, it is possible to determine if tradeoffs in measures of plant performance (such as growth versus seed production) in RCBr represent real life history tradeoffs, or are artifacts of breeding. I welcome the interest and efforts of other plant ecologists in this validation. At present, almost all of our knowledge about the performance of RCBr—except Kelly and Terrana (2004)—is based on plants grown in controlled environments. Thus, the discussion of these results in a more general context will compare the ecology of field grown RCBr plants to naturalized B. rapa grown in the field. For five genotypic lines of RCBr grown in the field, all of the phenotypic traits measured, except life span, differ significantly. Similar, significant phenotypic variation between naturalized B. rapa plants from different selection lines or populations, has been reported for field grown plants. For example, Agren and Schemske (1994) after one generation of artificial selection for trichome number found that high trichome plants initiated flowering slightly later than low trichome plants, and produced more fruits than low trichome plants. Nakamura et al. (1995) found population specific differences in plant survival to bolting, fruit set, and seed set. Siemens et al. (2002) observed significant differences in growth rate between B. rapa lines after selection for low myrosinase and low glucosinolate production versus high myrosinase and high glucosinolate production. Lastly, Hauser et al. (2003) found that plant density affected seed production in pure stands of naturalized B. rapa. For RCBr plants grown in the field life span, leaf size, height, growth rate, flowers, fruits, and fruit set were positively correlated with fitness. Similar, significant phenotypic correlations between plant traits and fitness have been reported for field grown B. rapa from different selection lines or naturalized populations. Agren and Schemske (1994) found a negative
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relationship between the start of flowering and flower production; though plants that initiated flowering earlier tended to produce more flowers, they also produced fewer fruits than plants that flowered later. In addition, Nakamura et al. (1995) found that as plant size increased, so did plant fecundity. While Siemens et al. (2002) state that growth rate, as measured by changes in total leaf area across time, was positively correlated with plant mass.
5. Conclusions Under optimal growth conditions, RCBr is characterized by its reduced size at maturity, minimal time from germination to flowering, increased uniformity at age of first flowering, high flower production, rapid maturation of seeds, and lack of seed dormancy. Musgrave (2000) made the general case for using RCBr in areas of plant research conducted in the laboratory. I make the case that RCBr can valuably serve as a model organism for ecological studies conducted in the field. There are at least six benefits that can be realized by plant ecologists using RCBr in the field. (1) Because of its origin through artificial selection from a global collection of B. rapa, RCBr has substantial allelic variation. (2) Genetic and genotypic variation in RCBr produces significant and informative variation at the phenotypic level in the field. (3) For RCBr grown in the field, all of the phenotypic traits measured, except life span, differed significantly between genotypic lines. It may be that past selection for rapid cycling, has compressed life span to the point where slight differences are statistically difficult to detect in small field grown populations. (4) RCBr is commercially available in a wide variety of known genotypes with distinct, contrasting phenotypes. (5) The close taxonomic relation of Brassica species to A. thaliana makes the molecular genetic tools derived for A. thaliana transferable to RCBr. (6) The larger flowers, fruits, and seeds in RCBr are easier to handle, count, and measure than flowers, fruits, and seeds in A. thaliana. The existing liability in the application of RCBr to plant field ecology is the lack of comparable studies. Torresen and Lotz (2000) concluded that it was not possible to use the results from B. rapa plants grown in growth chambers as predictors for B. rapa plants grown in the field. Presently, all of our knowledge about the performance
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of RCBr—except Kelly and Terrana (2004)—is based on plants grown in controlled, artificial environments. RCBr as a model system for plant ecology remains to be established. I welcome the efforts of other plant ecologists in this validation by performing parallel ecological trials in the field with RCBr and naturalized B. rapa. With this type of comparison, it is possible to know what tradeoffs in RCBr plant performance represent life history tradeoffs also evident in naturalized B. rapa. The opportunity is at hand for plant ecologists to add another “arrow to the quiver” and extend past labbased research on RCBr genetics, physiology, growth, and development to ecological research in the field. Plant ecologists can realize the many practical benefits of RCBr as a model organism with known genotype and phenotype for studies conducted in the field.
Acknowledgements Thanks to the students registered in Plant Ecology: Pat Creamer, Rolfe Freidenberg Jr., Dan Godwin, Tonniele Naeher, David Rosa, and Harry Shoemaker, and especially to the anonymous reviewers who made this a better paper by focusing its central argument.
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Evans, A.S., 1991. Leaf physiological aspects of nitrogen-use efficiency in Brassica campestris L.: quantitative genetic variation across nutrient treatments. Theor. Appl. Genet. 81, 64–70. Gurevitch, J.D., Taub, R., Morton, T.C., Gomez, P.L., Wang, I.N., 1996. Competition and genetic background in a rapidcycling cultivar of Brassica rapa (Brassicaceae). Am. J. Bot. 83, 932–938. Halfhill, M.D., Millwood, R.J., Weissinger, A.K., Warwick, S.I., Stewart, C.N., 2003. Additive transgene expression and genetic introgression in multiple green-fluorescent protein transgenic crop-weed hybrid generations. Theor. Appl. Genet. 107, 1533–1541. Harris, J.W., Stocker, H., 1998. Handbook of Mathematics and Computational Science. Springer-Verlag, New York, NY, USA. Hauser, T.P., Jorgensen, R.B., Ostergard, H., 1997. Preferential exclusion of hybrids in mixed pollination between oilseed rape (Brassica napus) and weedy B. campestris (Brassicaceae). Am. J. Bot. 84, 756–762. Hauser, T.P., Shaw, R.G., Ostergard, H., 1998a. Fitness of hybrids between weedy Brassica rapa oilseed rape (B. napus). Heredity 81, 429–435. Hauser, T.P., Jorgensen, R.B., Ostergard, H., 1998b. Fitness of backcross and F2 hybrids between weedy Brassica rapa oilseed rape (B. napus). Heredity 81, 436–443. Hauser, T.P., Damgaard, C., Jorgensen, R.B., 2003. Frequencydependent fitness of hybrids between oilseed rape (Brassica napus) and weedy B. rapa (Brassicaceae). Am. J. Bot. 90, 571– 578. Jorgensen, R.B., Andersen, B., 1994. Spontaneous hybridization between oilseed rape (Brassica napus) and weedy B. campestris (Brassicaceae): a risk of growing genetically modified oilseed rape. Am. J. Bot. 81, 1620–1626. Kelly, M.G., Terrana, S., 2004. Age-specific survivorship of field grown rapid cycling Brassica rapa (Wisconsin Fast PlantsTM ). J. Nat. Resour. Life Sci. Educ. 33, 40–46. Klaper, R., Frankel, S., Berenbaum, M.R., 1996. Anthocyanin content and UVB sensitivity in Brassica rapa. Photochem. Photobiol. 63, 811–813. Kleier, C., Farnsworth, B., Winner, W., 1999. Biomass, reproductive output, and physiological responses of rapid-cycling Brassica (Brassica rapa) to ozone and modified root temperature. New Phytol. 139, 657–664. Li, G., Gao, M., Yang, B., Ouiros, C.F., 2003. Gene for gene alignment between the Brassica and Arabidopsis genomes by direct transcriptome mapping. Theor. Appl. Genet. 107, 168– 180. Miller, T.E., 1995. Evolution of Brassica rapa L. (Cruciferae) populations in intra- and interspecific competition. Evolution 49, 1125–1133. Miller, T.E., 1996. On quantifying the intensity of competition across gradients. Ecology 77, 978–981. Miller, T.E., Schemske, D.W., 1990. An experimental study of competitive performance in Brassica rapa (Cruciferae). Am. J. Bot. 77, 993–998. Mitchell-Olds, T., 1996. Pleiotropy causes long-term genetic constraints on life-history evolution in Brassica rapa. Evolution 50, 1849–1858.
M.G. Kelly / Environmental and Experimental Botany 55 (2006) 61–69 Mitchell-Olds, T., Shaw, R., 1987. Regression analysis of natural selection: Statistical inference and biological interpretation. Evolution 41, 1149–1161. Myerscough, P.J., Marshall, J.K., 1973. Population dynamics of Arabidopsis thaliana (L.) Heynh Strain “Eastland” at different densities and nutrient levels. New Phytolog. 72, 595–617. Murren, C.J., Pendleton, N., Pigliucci, M., 2002. Evolution of phenotypic integration in Brassica (Brassicaceae). Am. J. Bot. 89, 655–663. Musgrave, M.E., 2000. Realizing the potential of rapid-cycling Brassica as a model system for use in plant biology research. J. Plant Growth Regul. 19, 314–325. Nakamura, R.R., Mitchell-Olds, T., Manasse, R.S., Lello, D., 1995. Seed predation, pathogen infection, and life history traits in Brassica rapa. Oecologia 102, 324–328. Pertl, M., Hauser, T.P., Damgaard, C., Jorgensen, R.B., 2002. Male fitness of oilseed rape (Brassica napus) weedy B. rapa and their F1 hybrids when pollinating B. rapa seeds. Heredity 89, 212–218. Pilson, D., 1996. Two herbivores and constraints on selection for resistance in Brassica rapa. Evolution 50, 1492–1500. Schmitt, J., McCormac, A.C., Smith, H., 1995. A test of the adaptive plasticity hypothesis using transgenic and mutant plants disabled in phytochrome-mediated elongation responses to neighbors. Am. Nat. 146, 937–953. Siemens, D.H., Garner, S.H., Mitchell-Olds, T., 2002. Cost of defense in the context of plant competition: Brassica rapa may grow and defend. Ecology 83, 505–517. Siemens, D.H., Mitchell-Olds, T., 1998. Evolution of pest-induced defenses in Brassica plants: tests of theory. Ecology 79, 632–646. Sleeman, J.D., Dudley, S.A., 2001. Phenotypic plasticity in carbon acquisition of rapid cycling Brassica rapa L. in response to light quality and water availability. Int. J. Plant Sci. 162, 297–307.
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Snow, A.A., Palma, P.M., 1997. Commercialization of transgenic plants: potential ecological risks. Bioscience 47, 86–96. Snow, A.A., Andersen, B., Jorgensen, R.B., 1999. Costs of transgenic herbicide resistance introgressed from Brassica napus into weedy B. rapa. Molec. Ecol. 8, 605–615. StatView Reference, 1999, third ed. SAS Institute Inc., Cary, NC, USA. Stowe, K.A., 1998. Realized defense of artificially selected lines of Brassica rapa: effects of quantitative genetic variation in foliar glucosinolate concentration. Environ. Entomol. 27, 1166–1174. Tomkins, S.P., Williams, P.H., 1990. Fast plants for finer science—an introduction to the biology of rapid cycling Brassica campestris (rapa) L. J. Biol. Educ. 24, 239–250. Torresen, K.S., Lotz, L.A.P., 2000. Growth of crop and weeds in growth chambers and outdoors. Acta Agric. Scand. 50, 66–74. Wade, M., Kalisz, S., 1990. The causes of natural selection. Evolution 44, 1947–1955. Warwick, S.I., Francis, A. 1994. Guide to the wild germplasm of brassica and allied crops, part v: life history and geographical data for wild species in the tribe brassiceae (cruciferae). Technical Bulletin 1994-2E. Centre for Land and Biological Resources Research, Ottawa, Ontario, Canada. Weinig, C., 2002. Phytochrome photoreceptors mediate plasticity to light quality in flowers of the Brassicaceae. Am. J. Bot. 89, 230–235. Williams, P.H., Hill, C.B., 1986. Rapid-cycling populations of Brassica. Science 232, 1385–1389. Zhu, B., Lawrence, J.R., Warwick, S.I., Mason, P., Braun, L., Halfhill, M.D., Stewart Jr., C.N., 2004. Short Communication—stable Bacillus thuringiensis (Bt) toxin content in interspecific F1 and backcross populations of wild Brassica rapa after Bt gene transfer. Molec. Ecol. 13, 237–242.
Characterizing genotype specific differences in survival, growth, and reproduction for field grown, rapid cycling Brassica rapa Martin G. Kelly∗ Department of Biology, Buffalo State College (SUNY), 1300 Elmwood Avenue, Buffalo, NY 14222-1095, USA Accepted 30 September 2004
Abstract Rapid cycling Brassica rapa (RCBr) develops rapidly, and has both small adult size and a brief life cycle. Yet, in spite of many investigations using RCBr, extremely few plant ecologists have used this plant in the field. This study is the first to describe the genotype specific variation in traits describing survival, growth, and reproduction for field grown, RCBr. I also identify traits associated with fitness. Five genotypes of RCBr were used: standard, anthocyaninless, yellow-green, anthocyaninless and hairless, and anthocyaninless and yellow-green. Plants were grown outside in a “common garden”. Eight plant traits were measured: life span, height, growth rate, leaf size, number of flowers and fruits, fruit set, and fitness. All traits, except life span, differed significantly among the five plant genotypes. Correlation analysis revealed that fitness increased as each of these of seven plant traits increased. This study demonstrates that RCBr can serve as a model organism in ecological field studies. © 2004 Elsevier B.V. All rights reserved. Keywords: Field ecology; Rapid cycling Brassica rapa; RCBr; Model organism
1. Introduction One species that botanists have broadly employed in all areas of research is Brassica rapa (syn. campestris). This species is endemic throughout Europe eastward to Siberia (Warwick and Francis, 1994, Part V, pp. 11, 19). This plant species is also widely cultivated in cooler climates (Pak Choi, Turnip Rape, Choy Sum, Chinese Cabbage, Tendergreen, Turnip, Sarson, and Broccoli Raab; Williams and Hill, 1986). Moreover, ∗
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B. rapa has established weedy and naturalized populations in North and South America, Australia, and Asia (Warwick and Francis, 1994, Part V, p. 19). Thus, many field studies have been done with it to understand the consequences of field release of genetically modified organisms (Jorgensen and Andersen, 1994; Hauser et al., 1997, 1998a, 1998b; Snow and Palma, 1997; Snow et al., 1999; Pertl et al., 2002; Halfhill et al., 2003; Zhu et al., 2004). Of special importance to this investigation is rapid cycling B. rapa (RCBr). RCBr was derived using classical methods of artificial selection and breeding (Williams and Hill, 1986). Under optimal conditions
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Table 1 Selected ecological studies conducted with Brassica rapa presented in chronological order References
Brassica rapa
Experiments in
Miller and Schemske (1990) Agren and Schemske (1992) Agren and Schemske (1993) Agren and Schemske (1994)
Rapid cycling Rapid cycling Rapid cycling Naturalized
Jorgensen and Andersen (1994) Miller (1995) Nakamura et al. (1995) Schmitt et al. (1995) Davis et al. (1996) Gurevitch et al. (1996) Klaper et al. (1996) Miller (1996) Mitchell-Olds (1996)
Naturalized
Growth chamber Growth chamber Growth chamber Field and greenhouse Field
Pilson (1996) Hauser et al. (1997) Hauser et al. (1998b) Hauser et al. (1998b) Siemens and Mitchell-Olds (1998) Stowe (1998) Kleier et al. (1999) Sleeman and Dudley (2001) Pertl et al. (2002) Siemens et al. (2002) Hauser et al. (2003) Kelly and Terrana (2004)
Naturalized Naturalized Naturalized Naturalized Naturalized
Rapid cycling Naturalized Rapid cycling Rapid cycling Rapid cycling Rapid cycling Naturalized Naturalized
Rapid cycling Rapid cycling Rapid cycling Naturalized Naturalized Cultivar Rapid cycling
Greenhouse Field Greenhouse Growth chamber Greenhouse Greenhouse Greenhouse Greenhouse and growth chamber Field Field Field Field Field and growth chamber Laboratory Growth chamber Greenhouse Field Field Field Field
RCBr has a life cycle of 35–40 days, from parental seed sown to offspring seed harvest (Williams and Hill, 1986). Compared to normal B. rapa, which can produce two generations in a year, under optimal conditions RCBr can produce 10 generations in a year. Thus, the potential applications of RCBr to experimental botany are diverse (Musgrave, 2000). After Williams and Hill (1986) summarized the results of their selection for rapid cycling Brassica species, plant ecologists were among the early adopters. Ecologists have since employed both naturalized and rapid cycling varieties of B. rapa in a wide variety of studies (Table 1). Though Miller and Schemske (1990) were the first to publish ecological research using RCBr, almost all of our knowledge about the growth and development of RCBr is based on plants raised in controlled environments (Table 1). However, Torresen and Lotz (2000) in a direct comparison of B.
rapa grown in growth chambers and the field concluded that it was not possible to use the phenotypic outcomes for plants grown in growth chambers as predictors for plants grown in the field. Though several ecological investigations have used RCBr, ecologists—except Kelly and Terrana (2004)—have not employed RCBr in the field (Table 1). Before RCBr can be applied as a model organism to more substantial ecological questions two points must be established. First, that ecologically relevant phenotypic variation exists in field grown plants. Second, that rapid cycling forms of this species grown in a natural setting can be used as model for native or naturalized B. rapa. Here, I characterize the growth of RCBr in a field setting. I identify some individual traits associated with plant survival and reproduction in a field grown population of RCBr. I see this fundamental goal only as a first step. Ultimately, the second goal must be explicitly met before ecologists adopt RCBr as a model organism for native or naturalized B. rapa in experiment based, field research.
2. Materials and methods 2.1. Plant material RCBr was derived from a global collection of B. rapa (L.) varieties (Williams and Hill, 1986). Plants were selected for the following six qualities: reduced size at maturity, minimum time from germination to flowering, uniformity of age at first flowering, high flower production, rapid maturation of seeds, and lack of seed dormancy (Tomkins and Williams, 1990). Individuals that flowered fastest were used as the base population. These individuals were out-crossed to generate seeds. In the next generation, the 10% of the offspring population that flowered first were selected as parents. These 288, or more, plants were mass pollinated to produce the next generation of seeds; artificial selection continued until the response to selection was stabilized (Williams and Hill, 1986). Under optimal laboratory conditions, RCBr flowers within 16 days of seed germination (Williams and Hill, 1986). It is important to recognize that RCBr retains considerable isozymic variation (Williams and Hill, 1986). In addition, when inbreeding is forced, fitness is significantly reduced (Evans, 1991). Both outcomes suggest
M.G. Kelly / Environmental and Experimental Botany 55 (2006) 61–69
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Table 2 Five genetically defined varieties of RCBr known to differ in phenotype Variety
Genotype
Phenotype
Standard
Rbr/Rbr
Normal chlorophyll production, anthocyanin expression, and epidermal hair density
Anthocyaninless
anl/anl
Normal chlorophyll production and epidermal hair density, lacks anthocyanin
Yellow-green
ygr/ygr
Deficient in chlorophyll production, normal anthocyanin expression and epidermal hair density
Anthocyaninless and hairless
anl/anl and Hir (0–1)
Normal chlorophyll production, lacks anthocyanin and has a very reduced number of epidermal hairs
Anthocyaninless and yellow-green
anl/anl and ygr/ygr
Deficient in chlorophyll production and lacks anthocyanin, has normal epidermal hair density
that B. rapa did not pass through a genetic bottleneck in order to establish the rapid cycling lines. RCBr are available in a wide variety of known genotypes with distinct phenotypes. For this experiment, I compared five self-incompatible genotypes known to differ in plant pigmentation and trichome production (Table 2). I chose phenotypic traits that alone, or in combination, might affect plant survival or reproduction. I used 48 seeds per genotype, for a total of 240 seeds. I used seeds purchased from Carolina Biological Supply Company (USA). RCBr seed stocks for these same five self-incompatible genotypes may also be purchased from Blades Biological Ltd. (UK). 2.2. Field study design Plants were grown outside in a “common garden” experiment and experienced the same general growing conditions. Over the course of the experiment mean daily high and low air temperatures were 24.1 and 14.6 ◦ C, and mean daily precipitation was 0.2 cm. Weather data were recorded at a station located 2.3 km from the field site. The total experimental area was watered and fertilized three times per week with 30 L of water containing all-purpose fertilizer (water soluble, 20–20–20 with micronutrients) at the concentration recommended for outdoor use (4 cc/L). Six plots were laid out in the most compact arrangement possible (2 × 3). Plots were laid out on August 29, 2002. The total experimental area was 1.6 m wide and 1.1 m tall. Five plots were used for this experiment; the sixth plot contained a different set of seeds for another project. To reduce the chance effect of local environ-
mental differences, seeds were randomly assigned to one of the five plots and to a grid position in the plot. Seed sowing positions were marked as a grid with a #804 SoilMasterTM dibble board (6 × 8 pegs spaced 4.4 cm× 6.4 cm apart). Each 6 × 8 plot of 48 seeds was 53.5 cm wide and 27 cm tall. All seeds were sown on August 29, 2002. Experimenter sown seeds were used for two reasons: (1) the randomization of individuals across the environment minimizes the correlation between genotype and environment, or the correlation of the phenotype with the environment because of past environmental effects (Mitchell-Olds and Shaw, 1987) and (2) Wade and Kalisz (1990) convincingly argued that the application of quantitative methods to measure natural selection was not sufficient to determine why selection operated in the manner observed; they recommended the use of experimental manipulation to identify the agents of viability and fecundity selection as a complement to quantitative analysis. Six students in a Plant Ecology course collected data. This semester long course met one evening per week (Fall 2002). The site was surveyed every week to monitor plant presence (September 5–October 17). Each seed’s place was marked with an individually numbered plastic stake. Death was recorded when a plant could not be located at the base of its marker, or the plant had browned and turned brittle. Individual plants were identified every census; the plant’s marker was not removed until the end of the experiment (October 17th). The experiment was terminated and all plants harvested three weeks after the last fruits were initiated.
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2.3. Individual traits measured Eight traits were measured for each plant. Life span was the total days from emergence to death. As plants were measured across time, some data were treated as repeated measures for analysis. For example, plant height was measured at 14, 21, 28, 35, and 42 days; these five measures of height across time were used as repeated measures of height per plant (height, mm). Similarly, knowing plant height at 14, 21, 28, 35, and 42 days permitted me to calculate the growth rate for each plant over time. Growth rates over 14, 21, 28, 35, and 42 days were treated as repeat measures of growth rate per plant (growth rate, mm/day). At 14 days, each plant’s largest cotyledon was measured for length and width. Based on the cotyledon’s heart shape (Tomkins and Williams, 1990), linear size was used to calculate the area of the cotyledon based on the formula for a cardioid (Harris and Stocker, 1998, p. 323). Similarly at 21 days, each plant’s largest leaf was measured for length and width. Based on the leaf’s oval shape (Tomkins and Williams, 1990), leaf length and width were used to calculate the area of the leaf based on the formula for an ellipse (Harris and Stocker, 1998, p. 93). As these two measures of area (cotyledon and leaf) were correlated (r = 0.734, P < 0.0001, N = 111) they were used as repeat measures of leaf size (mm2 ). If a plant flowered, the number of flowers present on a given survey date were counted. Flowers were counted for each flowering plant on September 19th and 26th, and October 3rd and 10th. These four measures of flower production per plant were used as repeat measures of flowering per plant (flowers). If a plant fruited, the number of fruits present on a given survey date were counted. Fruits were counted for each flowering plant on September 26th, and October 3rd and 10th. These three measures of fruit production per plant were treated as repeat measures of fruiting per plant (fruits). The ability of an individual plant to convert its flowers into fruits was measured by its average, proportional fruit set (fruit set). The number of seeds per mature fruit differed across genotypes (F1,3 = 44.5, P = 0.006). Seed set per fruiting plant was calculated by multiplying the number of fruits per plant by the average number of seeds per fruit on a genotype specific basis. The average numbers of seeds per fruit for each genotype were: anl/anl (3.8, S.E. = 0.58, N = 21 fruits), anl/anl and Hir 0-1 (4.9, S.E. = 0.47, N = 29 fruits),
Rbr/Rbr (6.7, S.E. = 0.58, N = 42 fruits), and ygr/ygr (7.0, S.E. = 0.0, N = 2 fruits). If a plant grew but did not fruit, its fitness (seeds per plant) was set to zero. 2.4. Statistical methods The mean and its standard error, and sample size (N) for each measured trait was calculated. Repeated measures analysis of variance (ANOVA) compared the five genotypes to determine if they differed in the average value for a trait measured across time (StatView Reference, 1999, p. 83). The null hypothesis was that there were no differences in RCBr traits associated genotype. Pearson correlation analysis of life history variables with Fitness was performed. The statistic (r) measures the proportionate linear increase or decrease of two variables. The null hypothesis was that there was no correlation between the two variables being compared (r = 0). To determine if a correlation coefficient differed significantly from zero, the correlation statistic was transformed into a variable (z) with a standardized normal distribution; this gives the probability that r = 0 (StatView Reference, 1999, p. 44). For fruit set (a proportion) the Kruskal–Wallis test was used as nonparametric equivalent to the one-way ANOVA comparing three or more groups (StatView Reference, 1999, p. 121). The null hypothesis was that the distribution of fruit set was equivalent for the five genotypic groups of RCBr. Both H and P as reported were corrected for the effect of ties. Similarly, Spearman correlation analysis of fruit set with fitness was performed (StatView Reference, 1999, p. 121). This nonparametric test measures the linear increase or decrease of ranks for two variables. The null hypothesis was that there was no correlation between fruit set and fitness (ρ = 0). Both ρ and P as reported were corrected for the effect of tied ranks.
3. Results The average level of germination in the field for RCBr was 66.7%. Germination ranged from 50.0% for the anthocyaninless mutant to 79.2% for the yellowgreen mutant. Standard, RCBr seeds germinated at a level of 62.5%. There were no genotype specific differences in the level of seed germination (G = 3.86, 2 χcritical = 9.49). On germination, a field grown RCBr
22.2 ± 2.1 (N = 18) 72.0 ± 9.2 (N = 59v 69.8 ± 3.2 (N = 126) 2.6 ± 0.1 (N = 126) 1.6 ± 0.3 (N = 92) 0.6 ± 0.3 (N = 32) 13.4 ± 0.05 (N = 32) 1.3 ± 0.6 N = 38 20.0 ± 2.3 (N = 14) 75.0 ± 8.9 (N = 50) 58.7 ± 2.7 (N = 104) 2.4 ± 0.1 (N = 104) 0.9 ± 0.2 (N = 76) 0.5 ± 0.2 (N = 17) 13.5 ± 0.07 (N = 17) 0.6 ± 0.4 N = 32 25.2 ± 2.8 (N = 10) 96.8 ± 8.9 (N = 66) 93.0 ± 4.1 (N = 141) 3.6 ± 0.1 (N = 141) 3.2 ± 0.4 (N = 105) 2.0 ± 0.2 (N = 57) 49.6 ± 0.05 (N = 57) 6.2 ± 1.2 N = 37 Life span (days) 21.6 ± 3.3 (N = 12) 26.0 ± 5.2 (N = 7) Leaf size† (mm2 ) 172.0 ± 30.0 (N = 45) 112.5 ± 16.5 (N = 39) 112.5 ± 5.7 (N = 101) 102.4 ± 5.8 (N = 93) Height† (mm) Growth rate† (mm/day) 4.3 ± 0.2 (N = 101) 3.8 ± 0.2 (N = 93) Flowers† (number/flowering plant) 4.3 ± 0.6 (N = 77) 4.9 ± 0.5 (N = 71) Fruits† (number/flowering plant) 2.9 ± 0.5 (N = 43) 3.2 ± 0.5 (N = 41) 53.0 ± 0.13 (N = 43) 48.5 ± 0.06 (N = 41) Fruit Set† (%/flowering plant) Fitness† (seeds/plant) 10.6 ± 3.2 (N = 30) 7.5 ± 1.8 (N = 24) Values are mean ± S.E. (N = the number of measurements made). † The compacted variable analyzed by repeated measures ANOVA.
anl/anl and ygr/ygr anl/anl and Hir (0–1) anl/anl Rbr/Rbr Trait
Table 3 Genotype specific means in survival, growth, and reproduction for field-grown RCBr
plant lived an average of 22.5 days (Table 3). Though average life span ranged from 20.0 days (anthocyaninless and yellow-green mutants) to 26.0 days (anthocyaninless mutants), life span did not vary significantly with genotype (F1,4 = 0.642, P = 0.6349). At 14 days, the area of one cotyledon averaged 67.8 mm2 in size. The average size for one cotyledon ranged from 51.2 mm2 (yellow-green mutants) to 99.3 mm2 (standard RCBr). Similarly at 21 days, the area of the largest leaf averaged 147.8 mm2 . Average leaf size ranged from 72.0 mm2 (yellow-green mutants) to 172.0 mm2 (standard RCBr). Differences in average leaf size (mm2 ) were found to be significantly associated with genotype (F1,4 = 4.763, P = 0.0014). Average plant height for field grown, RCBr plants was 86.6 mm. Average plant height ranged from 58.7 mm (anthocyaninless and yellow-green mutants) to 112.5 mm (standard RCBr, Table 3). Genotype specific differences in average height were found (F1,4 = 9.523, P < 0.0001). Similarly, growth rate (mm/day) for these B. rapa plants was 3.3 mm/day. Average growth rate ranged from 2.4 mm/day (anthocyaninless and yellow-green mutants) to 4.3 mm/day (standard RCBr, Table 3). Growth rate was also found to differ on average for these five genotypes of RCBr (F1,4 = 8.825, P < 0.0001). The average number of flowers per flowering plant for field grown, RCBr plants was 2.9 flowers. Average flower number per flowering plant ranged from 0.9 flowers (anthocyaninless and yellow-green mutants) to 4.9 flowers (anthocyaninless mutants, Table 3). Average flower production (per flowering plant) was significantly affected by plant genotype (F1,4 = 8.128, P < 0.0001). The average number of fruits per flowering plant for field grown, RCBr plants was 2.1 fruits. Average fruit number per flowering plant ranged from 0.5 fruits (anthocyaninless and yellow-green mutants) to 3.2 fruits (anthocyaninless mutants, Table 3). A significant genotype specific affect on the number of fruits per flowering plant was observed (F1,4 = 3.493, P = 0.0159). The average level of fruit set in the field was 40.8%. Fruit set ranged from 13.4% (yellowgreen mutant) to 53.0% (standard RCBr, Table 3). The Kruskal–Wallis test showed that fruit set (per flowering plant) differed significantly among genotypes (4 d.f., H = 19.426, P = 0.0006). Average fitness for field grown RCBr was 4.9 seeds per plant. Average fitness ranged from 0.6 seeds per plant (anthocyaninless and
ygr/ygr
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yellow-green mutants) to 10.6 seeds per plant (standard B. rapa, Table 3). Average fitness differed significantly across genotypes (F1,4 = 6.682, P < 0.0001). The correlation of measured traits with fitness revealed that, on average, each of these traits was individually and positively correlated with fitness. As plant life span increased, fitness increased (r = 0.454, N = 61, P = 0.0002). As leaf size increased, fitness increased (r = 0.597, N = 259, P < 0.0001). As plant height increased, fitness increased (r = 0.557, N = 565, P < 0.0001). As plant growth rate increased, fitness increased (r = 0.575, N = 565, P < 0.0001). For flowering plants, as flowers (r = 0.507, N = 421, P < 0.0001) and fruits (r = 0.789, N = 190, P < 0.0001) increased, fitness increased. Lastly, as the proportion of flowers converted into fruits increased, fitness increased (ρ = 0.697, N = 190, P < 0.0001).
4. Discussion For field studies that require known genotypes and phenotypes, there are two taxonomically related plant model systems commonly used. One of these is Arabidopsis thaliana, a small mustard whose entire genome has been sequenced (Arabidopsis Genome Initiative, 2000). For research that requires Brassica species, both cultivated and wild lines of B. oleracea, B. rapa, and B. napus are available. The relatedness of Brassica species to A. thaliana has permitted the identification of consensus genetic markers (Brunel et al., 1999). In addition gene for gene alignment has been observed between B. oleracea and A. thaliana (Li et al., 2003). Thus, the close relation of Brassica species to A. thaliana makes the molecular genetic tools derived for A. thaliana transferable to RCBr. If these model systems exist and are commonly used in field studies, does RCBr have anything to offer plant ecologists? Like A. thaliana, RCBr is characterized by its rapid development, brief life cycle, small adult size, and the wide availability of defined genotypic lines. Because RCBr has such a brief life span (22.5 days), it is readily possible to study the evolving genetic structure of RCBr populations in the field in a reduced amount of time. More importantly, can RCBr provide any advantages over A. thaliana in ease of use? Many experimental manipulations in plant ecology rely on breeding designs to generate seeds. Weinig (2002)
measured flower traits for both A. thaliana and RCBr grown in the same controlled environments. From her data, I calculate that flowers of RCBr are three times larger than flowers from A. thaliana. This would facilitate any hand-pollinations needed in the production of progeny or families. In addition, the fruits and seeds produced from RCBr’s larger flowers are larger than the fruits and seeds produced by A. thaliana. Average fruit length in standard RCBr is 37.3 mm (Kelly, unpublished data). While, fruit length across A. thaliana genotypes ranges on average between 9.5 and 15.5 mm (Myerscough and Marshall, 1973; Alonso-Blanco et al., 1999). Average RCBr seed mass (standard genotype) is 1.9 mg (Kelly, unpublished data). Whereas, average seed mass across A. thaliana genotypes ranges between 0.0196 and 0.0028 mg (Myerscough and Marshall, 1973; Alonso-Blanco et al., 1999). Larger flowers, fruits, and seeds in RCBr are easier to handle, count, and measure than are the smaller flowers, fruits, and seeds of A. thaliana. If RCBr is to be applied as a model organism to more substantial ecological questions, it must be established that ecologically relevant phenotypic variation exists in field grown RCBr plants. All of the traits measured, except life span, differ significantly among five genotypes of RCBr grown in the field. In addition, life span, leaf size, height, growth rate, flowers, fruits, and fruit set are each positively correlated with fitness for these RCBr plants. As these traits increase in amount, fitness increases. Murren et al. (2002) while studying phenotypic integration in six species of Brassica (all rapid cycling varieties) determined that plant height and fruit number were positively correlated in RCBr. Byers (personal communication, 2004) used exploratory path analysis to quantify phenotypic selection on life history traits in RCBr. She investigated the effect of different nutrient environments in orienting the path between life history traits. At very low Nitrogen (4.7 ppm), earliness in leafing, earliness in flowering, and leaf size at flowering were positively related to fruit number (Diane Byers, personal communication, 2004). At low Nitrogen (18.8 ppm) earliness in leafing and flower number were positively linked to fruit number (Diane Byers, personal communication, 2004). At high Nitrogen (150 ppm) leaf size at flowering and flower number were positively related to fruit number (Diane Byers, personal communication, 2004).
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Before ecologists adopt RCBr as a model for native or naturalized B. rapa in field research, its ability to respond comparably must be established. Torresen and Lotz (2000) in a comparison of B. rapa grown in growth chambers to the field concluded “It is not possible to use directly growth reactions obtained in growth chambers for field conditions”. Ideally, the reliability of RCBr will be established by performing parallel field trials employing RCBr and naturalized B. rapa, and measuring the same plant traits in the same way. With this type of comparison, it is possible to determine if tradeoffs in measures of plant performance (such as growth versus seed production) in RCBr represent real life history tradeoffs, or are artifacts of breeding. I welcome the interest and efforts of other plant ecologists in this validation. At present, almost all of our knowledge about the performance of RCBr—except Kelly and Terrana (2004)—is based on plants grown in controlled environments. Thus, the discussion of these results in a more general context will compare the ecology of field grown RCBr plants to naturalized B. rapa grown in the field. For five genotypic lines of RCBr grown in the field, all of the phenotypic traits measured, except life span, differ significantly. Similar, significant phenotypic variation between naturalized B. rapa plants from different selection lines or populations, has been reported for field grown plants. For example, Agren and Schemske (1994) after one generation of artificial selection for trichome number found that high trichome plants initiated flowering slightly later than low trichome plants, and produced more fruits than low trichome plants. Nakamura et al. (1995) found population specific differences in plant survival to bolting, fruit set, and seed set. Siemens et al. (2002) observed significant differences in growth rate between B. rapa lines after selection for low myrosinase and low glucosinolate production versus high myrosinase and high glucosinolate production. Lastly, Hauser et al. (2003) found that plant density affected seed production in pure stands of naturalized B. rapa. For RCBr plants grown in the field life span, leaf size, height, growth rate, flowers, fruits, and fruit set were positively correlated with fitness. Similar, significant phenotypic correlations between plant traits and fitness have been reported for field grown B. rapa from different selection lines or naturalized populations. Agren and Schemske (1994) found a negative
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relationship between the start of flowering and flower production; though plants that initiated flowering earlier tended to produce more flowers, they also produced fewer fruits than plants that flowered later. In addition, Nakamura et al. (1995) found that as plant size increased, so did plant fecundity. While Siemens et al. (2002) state that growth rate, as measured by changes in total leaf area across time, was positively correlated with plant mass.
5. Conclusions Under optimal growth conditions, RCBr is characterized by its reduced size at maturity, minimal time from germination to flowering, increased uniformity at age of first flowering, high flower production, rapid maturation of seeds, and lack of seed dormancy. Musgrave (2000) made the general case for using RCBr in areas of plant research conducted in the laboratory. I make the case that RCBr can valuably serve as a model organism for ecological studies conducted in the field. There are at least six benefits that can be realized by plant ecologists using RCBr in the field. (1) Because of its origin through artificial selection from a global collection of B. rapa, RCBr has substantial allelic variation. (2) Genetic and genotypic variation in RCBr produces significant and informative variation at the phenotypic level in the field. (3) For RCBr grown in the field, all of the phenotypic traits measured, except life span, differed significantly between genotypic lines. It may be that past selection for rapid cycling, has compressed life span to the point where slight differences are statistically difficult to detect in small field grown populations. (4) RCBr is commercially available in a wide variety of known genotypes with distinct, contrasting phenotypes. (5) The close taxonomic relation of Brassica species to A. thaliana makes the molecular genetic tools derived for A. thaliana transferable to RCBr. (6) The larger flowers, fruits, and seeds in RCBr are easier to handle, count, and measure than flowers, fruits, and seeds in A. thaliana. The existing liability in the application of RCBr to plant field ecology is the lack of comparable studies. Torresen and Lotz (2000) concluded that it was not possible to use the results from B. rapa plants grown in growth chambers as predictors for B. rapa plants grown in the field. Presently, all of our knowledge about the performance
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of RCBr—except Kelly and Terrana (2004)—is based on plants grown in controlled, artificial environments. RCBr as a model system for plant ecology remains to be established. I welcome the efforts of other plant ecologists in this validation by performing parallel ecological trials in the field with RCBr and naturalized B. rapa. With this type of comparison, it is possible to know what tradeoffs in RCBr plant performance represent life history tradeoffs also evident in naturalized B. rapa. The opportunity is at hand for plant ecologists to add another “arrow to the quiver” and extend past labbased research on RCBr genetics, physiology, growth, and development to ecological research in the field. Plant ecologists can realize the many practical benefits of RCBr as a model organism with known genotype and phenotype for studies conducted in the field.
Acknowledgements Thanks to the students registered in Plant Ecology: Pat Creamer, Rolfe Freidenberg Jr., Dan Godwin, Tonniele Naeher, David Rosa, and Harry Shoemaker, and especially to the anonymous reviewers who made this a better paper by focusing its central argument.
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