SEARCH

SEARCH BY CITATION

Keywords:

  • Aboveground biomass;
  • inflorescence mass;
  • intraspecific variation;
  • net ecosystem carbon exchange;
  • Solidago altissima

Abstract

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

Linking intraspecific variation in plant traits to ecosystem carbon uptake may allow us to better predict how shift in populations shape ecosystem function. We investigated whether plant populations of a dominant old-field plant species (Solidago altissima) differed in carbon dynamics and if variation in plant traits among genotypes and between populations predicted carbon dynamics. We established a common garden experiment with 35 genotypes from three populations of S. altissima from either Tennessee (southern populations) or Connecticut (northern populations) to ask whether: (1) southern and northern Solidago populations will differ in aboveground productivity, leaf area, flowering time and duration, and whole ecosystem carbon uptake, (2) intraspecific trait variation (growth and reproduction) will be related to intraspecific variation in gross ecosystem CO2 exchange (GEE) and net ecosystem CO2 exchange (NEE) within and between northern and southern populations. GEE and NEE were 4.8× and 2× greater in southern relative to northern populations. Moreover, southern populations produced 13× more aboveground biomass and 1.4× more inflorescence mass than did northern populations. Flowering dynamics (first- and last-day flowering and flowering duration) varied significantly among genotypes in both the southern and northern populations, but plant performance and ecosystem function did not. Both productivity and inflorescence mass predicted NEE and GEE between S. altissima southern and northern populations. Taken together, our data demonstrate that variation between S. altissima populations in performance and flowering traits are strong predictors of ecosystem function in a dominant old-field species and suggest that populations of the same species might differ substantially in their response to environmental perturbations.


Introduction

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

Understanding variation in ecosystem function across space and time has become an increasingly important task in ecology especially in the context of global change. Studies have documented threefold variation in ecosystem function, such as net ecosystem CO2 exchange (NEE), among plant communities, which demonstrates variation in the capacity of ecosystems to take up CO2 (Bousquet et al. 2000; Gurney et al. 2002; Janssens et al. 2003; Baldocchi 2008). General patterns of NEE include substantially greater net ecosystem CO2 uptake in temperate (i.e., greater CO2 sinks) than in arid ecosystems (i.e., lower CO2 sinks) (Verburg et al. 2004; Wilsey and Polley, 2004; Patrick et al. 2007; Risch and Frank, 2010), and lower NEE uptake in northern than southern latitude communities (Valentini et al. 2000; Van Dijk and Dolman 2004; Li et al. 2005; Kato and Tang 2008; Yuan et al. 2009). Documenting regional variation in NEE is necessary to understand whether particular ecosystems, or the components of those ecosystems, will be sources or sinks of atmospheric CO2.

Variation in net primary productivity (NPP) is often associated with variation in NEE among plant communities. In general, NPP is higher in temperate than in arid ecosystems (Ni 2000; Huxman et al. 2004a) and in southern latitude ecosystems than in northern latitude ecosystems (Kicklighter et al. 1999; Saugier et al. 2001; Huston and Wolverton 2009); this systematic variation in NPP likely promotes concomitant variation in whole ecosystem CO2 uptake. Although it is clear that climatic variation drives variation in NPP (and therefore whole ecosystem CO2 uptake), variation in biodiversity also influences NPP and whole ecosystem CO2 uptake (Hooper et al. 2005). Generally speaking, previous studies that have manipulated plant biodiversity found that the number of species is positively associated with an increase in NPP (Tilman et al. 2001) and NEE (Wilsey and Polley 2004). One common explanation for the positive effect of interspecific diversity is that trait differences among species increase NPP and NEE (Hooper et al. 2005).

Recently, a growing number of studies have demonstrated that intraspecific diversity, or within species diversity, can also shape NPP (Booth and Grime 2003; Classen et al. 2006; Crutsinger et al. 2006, 2008; Johnson et al. 2006; Whitham et al. 2006; Hughes et al. 2008). The effects of within species diversity might be due to either sampling effects (Huston 1997; Wardle 1999) or niche complementarity (Tilman et al. 2001; Hooper et al. 2005), as is the case with studies on interspecific diversity. Intraspecific trait variance can be as great as interspecific trait variance, thereby generating greater NPP (Crutsinger et al. 2006). While it is increasingly clear that NPP is correlated with ecosystem CO2 uptake at the plot level from several interspecific diversity studies (Wilsey and Polley 2004; Klumpp and Soussana 2009; Hirota et al. 2010), we do not know if this pattern holds for intraspecific diversity and what the mechanisms may be—variation in NPP or variation in soil respiration, driving NEE.

Similarly, understanding variation in patterns of NPP allocation within species should allow us to understand better the underlying processes shaping ecosystem function. Previous studies have documented variation in growth and/or reproduction strategies among plant species including among species within the genus Solidago (Abrahamson and Gadgil 1973; Abrahamson and McCrea 1986; Abrahamson and Weis 1997; Abrahamson et al. 2005) and across environmental gradients (Oleksyn et al. 1998; Ladinig and Wagner 2005; Milla et al. 2009). However, to our knowledge, only a few empirical (Fox and Stevens 1991; Houssard and Escarre 1991; Tilman and Wedin 1991; Abrahamson et al. 2005; Knops et al. 2007; Koenig et al. 2009), and theoretical (Roff 1992; Enquist et al. 1999) studies have examined the links between biomass allocation, growth and reproduction within species, and these studies have produced idiosyncratic results. Further, we are not aware of any studies that show how such variation in life-history traits may shape an ecosystem function, such as NEE, and how life-history traits of populations in high latitude versus low latitude influence ecosystem function.

While linking life-history trait variation, both within and among species, to ecosystem function will be key to understanding how traits of species shape ecosystems locally, it is also important to elucidate whether such relationships between traits and ecosystem function vary between populations. A suite of studies has documented spatial variation in NEE, NPP, and plant flowering, with the typical result being that populations from higher latitudes exhibit lower NEE (Valentini et al. 2000; Jarvis et al. 2001; Van Dijk and Dolman 2004; Li et al. 2005; Jacobs et al. 2007; Kato and Tang 2008; Yuan et al. 2009), lower NPP (Oleksyn et al. 1998, 1999), and lower reproductive output (Sills and Nienhuis 1995; Tungate et al. 2002; Bohlenius et al. 2006; Hall et al. 2007; Breen and Richards 2008; Chuine 2010; Dainese 2011) when compared to populations from lower latitudes. Although temperature influences ecosystem function across latitudinal gradients, variation in photoperiod along the same gradient may be just as critical in determining variation in aboveground net primary productivity (ANPP), flowering, and ecosystem function (Enquist 2011).

Here, we ask whether intraspecific variation between Solidago populations from Tennessee and Connecticut in plant performance and flowering shapes ecosystem function within a dominant old-field plant species in the eastern United States. We quantified variation among Solidago altissima (hereafter Solidago) genotypes and between Solidago populations (northern and southern range) in performance traits (total leaf area and productivity), plant reproduction dynamics (first- and last-day flowering, flowering duration, and inflorescence mass), as well as NEE, GEE, and ecosystem respiration. Additionally, we quantified whether intraspecific variation in performance traits and/or plant reproduction predicted NEE and GEE. Specifically, we predicted that (1) southern and northern Solidago populations will differ in aboveground productivity, leaf area, flowering time and duration, and ecosystem functions; (2) intraspecific trait variation (growth and reproduction) will be related to intraspecific variation in GEE and NEE within and between northern and southern populations.

Materials and Methods

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

To test if intraspecific variation in plant performance traits (biomass, and leaf area) and/or reproductive traits (first- and last-day flowering, flowering duration, and inflorescence mass) predicted NEE, we measured traits between and within populations of Solidago (Fig. 1), a dominant old-field plant species in the eastern United States (Schmitz 2003, 2008; Abrahamson et al. 2005; Wise and Abrahamson 2008; Souza et al. 2011a,b); Solidago can make up 20–30% of the total aboveground productivity in old-field communities near our study site (L. Souza and W. A. Bunn, unpubl. data) and influences the structure of subdominant old-field communities and associated ecosystem processes such as productivity (Crutsinger et al. 2006; Souza et al. 2011a,b).

image

Figure 1. Solidago altissima inflorescence.

Download figure to PowerPoint

Solidago collection and site description

In 2009, we collected individual genotypes of Solidago from naturally occurring populations. We collected 15 genotypes among three old-field sites near Stafford, Connecticut (northern populations), and 20 genotypes from three old-field sites near Oak Ridge, Tennessee (southern populations). At each location, we identified Solidago patches growing 50–150 m apart and excavated individual rhizomes within each patch using a small hand trowel. Based on AFLP analyses by Crutsinger et al. (2006), a spatial distance of 50–150 m between Solidago patches ensures a mean genetic dissimilarity of 25.1% between genotypes. We propagated each Solidago genotype by cutting the collected rhizomes into 3-cm sections and planting them in flats containing sterilized potting soil (Pro-Mix BX, Premier Brands, New Rochelle, NY). Although rhizome volume did not vary between southern and northern populations (F= 1.54, P= 0.25), it did vary among genotypes within southern populations (F= 4.5, P= 0.01) and among genotypes within northern populations (F= 3.38, P= 0.02). Given differences in rhizome volume among genotypes in southern and among genotypes in northern populations, we used rhizome volume as a covariate in an analysis of variance model where genotype was the categorical factor predicting NEE. We found that for both southern and northern populations, rhizome volume was not a significant covariate explaining NEE (for northern genotypes P-value = 0.67; for southern genotypes P-value = 0.99). All of the Solidago ramets emerged after seven days and were established in a greenhouse (25°C) for approximately 12 weeks. Plants were watered as needed and fertilized monthly with a water-soluble fertilizer (15:20:25, N:P:K, Scotts Sierra Horticultural Co. Marysville, OH). Root-stimulating solution was applied to all ramets at the onset of the experiment (Roots 2, Roots Inc., OSIA Independence, MO, 1 g per 3.79 L) and similar to other experiments (Crutsinger et al. 2006).

Common garden experiment

In 2009, we transplanted Solidago individuals from the 35 genotypes into 20-gallon pots located within a mown field at the University of Tennessee's Agricultural Experimental Station, Knoxville, TN (35°53′47.84″N, 83°57′22.86″W). Mean annual rainfall at the site is 102 mm, mean air temperature ranges from 7.7°C (January) to 30.6°C (July). We established Solidago monocultures by planting three Solidago individuals from the same genotype within a single pot (n= 2); each pot contained sterilized potting soil (Pro-Mix BX).

In August 2009, we measured NEE (µmols CO2 m–2 s–1) on a subset of 10 genotypes by recording the flux of carbon dioxide (CO2) from each Solidago monoculture using a Li-COR 7500 infrared gas analyzer (Lincoln, NE). Measurements were recorded between the hours of 11 am and 2 pm on a clear, sunny day to ensure maximum photosynthetic activity. We placed a portable chamber (0.49 m2 in area and 0.37 m3 in volume) covered with semitransparent woven rip-stop polyethelene material over each experimental pot and recorded CO2 for approximately 120 sec (Arnone and Obrist 2003; Huxman et al. 2004b; Potts et al. 2006). NEE is an integrative measure of CO2 assimilation by photosynthesis and CO2 loss by plant and microbial respiration. We measured whole ecosystem respiration, loss of CO2 via plant, and microbial respiration (Re, µmols CO2 m–2 s–1) by placing a dark cloth over the portable chamber and then recording CO2 for an additional 120 sec. NEE and Re were calculated by measuring and recording the slope of CO2 over time and then correcting for chamber volume and sampled area. Finally, gross ecosystem CO2 exchange (GEE) was calculated by summing NEE and Re. Greater negative values indicate increasing NEE (i.e., greater whole ecosystem CO2 uptake), while greater positive values indicate greater net ecosystem carbon efflux (i.e., greater whole ecosystem CO2 release). Likewise, greater positive values indicate greater total ecosystem CO2 loss, while smaller positive values indicate lower total ecosystem CO2 loss.

We estimated aboveground biomass and total leaf area using allometric equations that were developed by linear regressions of morphological traits (height [cm], stem diameter [cm], leaf width [cm], leaf length [cm], and internode length [cm]) with aboveground biomass on a subset of genotypes collected in CT and in TN. The area of the largest leaf was the best predictor for total plant leaf area (y= 9.986x+ 196, R2= 0.22, P= 0.0036), while both diameter and height were the best predictors for aboveground biomass (y= 0.010x+ 1.055, R2= 0.83, P < 0.0001) among all genotypes.

We measured plant reproduction by recording (1) the Julian day the first flower within an inflorescence flowered (i.e., first-day flowering) and (2) the Julian day that all the flowers within an inflorescence finished flowering (i.e., last-day flowering). We then calculated flowering duration by subtracting the last-day flowering from the first-day flowering. Finally, we collected inflorescence masses from each Solidago genotype, oven-dried at 60°C for approximately 48 h, and recorded their oven-dry mass.

Statistical analyses

We used a one-way analysis of variance (ANOVA) to test for the effects of genotype identity on plant reproduction (first- and last-day flowering, flowering duration, and inflorescence mass), total leaf area, aboveground biomass, NEE, GEE, and Re both within and between Solidago populations (CT or TN). We also performed linear regressions to determine whether plant traits (performance and reproduction) were related to ecosystem function (NEE, GEE, and Re). We also tested for homoscedasticity in all our response variables by calculating the coefficient of variation and found that we met the distribution of our dataset to meet homogeneity of variance. Variables that were not normal were log-transformed and all analyses were performed using JMP 9 software.

Results

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

GEE was 4.8× higher (P= 0.0005) and NEE was 2× higher (P= 0.0005) in southern populations than in northern populations (Table 1, Fig. 2). However, southern and northern Solidago populations did not differ in Re (P= 0.36) (Table 1, Fig. 2). In addition, we found no differences among genotypes within southern or within northern populations in GEE (P= 0.57 and P= 0.92, respectively) or NEE (P= 0.27 and P= 0.90, respectively). On the other hand, Re differed among northern (P= 0.056) genotypes, but not among southern genotypes (P= 0.49) (Table 2, Fig. 2).

Table 1.  ANOVA results testing for the effects of intraspecific variation (CT vs. TN) on gross ecosystem exchange (GEE), net ecosystem exchange (NEE), respiration (Re), aboveground net primary productivity (ANPP), total plant leaf area, first day of flowering, last day of flowering, flowering duration, and inflorescence mass. P-values in bold are statistically significant (P < 0.05).
 dfMean square F-value P-value
GEE1,16120.4923.86 <0.001
NEE1,1699.2331.33 <0.001
Re1,161.220.88 0.360
ANPP1,2763,930.90136.39 <0.001
Total plant leaf area1,273512.90.25 0.619
First day of flowering1,2713,981.409.38 <0.001
Last day of flowering1,2712,796.507.400.011
Flowering duration1,2726.230.49 0.488
Inflorescence mass1,27867.418.130.008
image

Figure 2. Intraspecific variation in GEE (top panel), NEE (middle panel), and Re (bottom panel) within CT genotypes (left panel), within TN genotypes (center panel), and between CT and TN populations (right panel). Values are mean ± SE.

Download figure to PowerPoint

Table 2.  ANOVA results testing for the effects of intraspecific variation (within CT vs. within TN) on gross ecosystem exchange (GEE), net ecosystem exchange (NEE), respiration (Re), aboveground net primary productivity (ANPP), total plant leaf area, first day of flowering, last day of flowering, flowering duration, and inflorescence mass. P-values in bold are statistically significant (P < 0.05).
 dfMean square F-value P-value
Connecticut
GEE 3,49.251.072 0.456
NEE 3,40.460.183 0.902
Re 3,41.276.131 0.056
ANPP10,27247.931.113 0.388
Total plant leaf area10,2736,99241.834 0.102
First day of flowering10,2734,843.99.427 <0.001
Last day of flowering10,2745,457.710.112 <0.001
Flowering duration10,271010.225.551 <0.001
Inflorescence mass10,270.461.077 0.41
Tennessee
GEE 3,421.431.875 0.275
NEE 3,47.141.875 0.275
Re 3,41.430.941 0.499
ANPP10,2715,723.51.449 0.181
Total plant leaf area10,27400,5591.298 0.257
First day of flowering10,277836.723.853 <0.001
Last day of flowering10,277618.22.891 <0.001
Flowering duration10,27289.712.867 <0.001
Inflorescence mass10,270.451.56 0.149

Aboveground biomass in southern Solidago populations was 13× greater (P < 0.0001) than in northern populations (Table 1, Fig. 3), but the populations did not differ in total leaf area (P= 0.62) (Table 1, Fig. 3). There were also no differences within southern or within northern populations in aboveground biomass (P= 0.18 and P= 0.39, respectively) or total leaf area (P= 0.26 and P= 0.10, respectively; Table 2, Fig. 3).

image

Figure 3. Intraspecific variation in aboveground biomass (top panel) and total leaf area (bottom panel) within CT genotypes (left panel), within TN genotypes (center panel), and between CT and TN populations (right panel). Values are mean ± SE.

Download figure to PowerPoint

Relative to genotypes from northern populations, Solidago genotypes from southern populations flowered later and produced greater inflorescence mass, but did not differ in flowering duration. Solidago genotypes from southern populations began flowering approximately 45 days later (P= 0.006), finished flowering 44 days later (P= 0.011), and produced 1.6× more inflorescence mass (P= 0.0082) than did genotypes from northern populations (Table 1, Fig. 4). Solidago genotypes from southern and northern populations did not differ (P= 0.4877) in length of flowering (CT = 25.6 ± 2.8 days, TN = 23.6 ± 1.35 days) (Table 1). However, there were differences within southern and within northern populations in length of flowering (P= 0.0004 and P < 0.0001, respectively), first day of flowering (P < 0.0001 and P < 0.0001, respectively), and last day of flowering (P= 0.0004 and P < 0.0001, respectively) (Table 2, Fig. 4). For example, genotypes in northern populations showed larger variation in the range of first- and last-day flowering (77–224, 92–249 Julian day, respectively). Similarly, genotypes from southern populations also varied significantly in first- and last-day flowering (226–265, 262–281 Julian day, respectively).

image

Figure 4. Intraspecific variation in first day flowering (top panel), last day flowering (middle panel), and inflorescence mass (bottom panel) within CT genotypes (left panel), within TN genotypes (center panel), and between CT and TN genotypes (right panel). Values are mean ± SE.

Download figure to PowerPoint

Intraspecific variation in plant performance (i.e., productivity) and reproduction (inflorescence mass) between southern and northern Solidago populations was related to GEE and NEE. Productivity explained 57% (P= 0.011) and 76% (P= 0.0009) of the total variation in both GEE and NEE, respectively (Fig. 5). Likewise, inflorescence mass accounted for 43% (P= 0.038) and 42% (P= 0.039) of the total variation in both GEE and NEE, respectively (Fig. 5). Neither productivity (R2= 0.05, P= 0.523) nor inflorescence mass (R2= 0.00, P= 0.901) predicted Re between southern and northern populations. On the other hand, intraspecific variation in plant performance and reproduction was not related to GEE and NEE within southern and northern populations. We found no relationship between total aboveground biomass and GEE (southern: P= 0.475; northern population: P= 0.691) or NEE (southern: P= 0.980; northern population: P= 0.572) within populations. Likewise, we found no relationship between total inflorescence mass and GEE (southern: P= 0.394; northern population: P= 0.964) or NEE (southern: P= 0.530; northern population: P= 0.818) within populations. Finally, we also found no relationship between total aboveground biomass or total inflorescence mass and ecosystem respiration within northern (total aboveground biomass and ecosystem respiration: P= 0.753; total inflorescence mass and ecosystem respiration: P= 0.841) and within southern populations (total aboveground biomass and ecosystem respiration: P= 0.371; total inflorescence mass and ecosystem respiration: P= 0.739).

image

Figure 5. Linear regressions of gross ecosystem CO2 exchange and aboveground biomass (a); gross ecosystem CO2 exchange and inflorescence mass (c); net ecosystem CO2 exchange and aboveground biomass and inflorescence mass (b and d). Black circles illustrate CT genotypes and clear circles illustrate TN genotypes.

Download figure to PowerPoint

Discussion

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

The most salient result of our study is that ecosystem function varied among genotypes within the same population and between populations within a dominant old-field species. In particular, southern Solidago populations assimilated more atmospheric CO2 (i.e., GEE and NEE) than northern Solidago populations. The results of our experiments generally agree with studies investigating interspecific variation in ecosystem CO2 assimilation in grasslands. For example, (Wilsey and Polley 2004) documented significant interspecific variation in NPP and NEE, and they found that NEE and net C uptake were greater in species-rich plots than species-poor plots. They suggest that these patterns were the result of high interspecific variation among species in NPP and NEE. Our study emphasizes the role of intraspecific variation in CO2 exchange found between northern and southern old-field ecosystems.

Other studies investigating variation in NEE across latitudinal gradients have documented that NEE declines with increasing latitude; thus, southern ecosystems have greater net ecosystem C uptake when compared to northern ecosystems (Valentini et al. 2000; Jarvis et al. 2001; Van Dijk and Dolman 2004; Li et al. 2005; Jacobs et al. 2007; Kato and Tang 2008; Yuan et al. 2009). Yuan et al. (2009) suggested that latitudinal patterns of NEE are likely determined by a decline in gross primary production (GPP) and/or an increase in ecosystem respiration in higher latitudes compared to lower latitudes. Across European forest ecosystems, Valentini et al. (2000) demonstrated that increases in ecosystem respiration in higher latitudes, rather than changes in GPP, influenced NEE and overall CO2 balance at the regional scale. However, Van Dijk and Dolman (2004) suggested that variation in GPP, not ecosystem respiration, determined NEE across a latitudinal gradient. Finally, Yuan et al. (2009) found that latitudinal variation in GPP shapes NEE in evergreen boreal forests, whereas latitudinal variation in ecosystem respiration influences NEE in deciduous broadleaf forests. In our study, variation in GPP rather than ecosystem respiration drives NEE within a dominant old-field species. While it has been documented that respiration has an effect on NEE at higher latitudes, the bulk of studies suggests that variation in GPP influences NEE more strongly. Our study contributes to the majority finding and shows that variation in GPP rather than ecosystem respiration drives NEE within a dominant old-field species.

Southern Solidago populations had significantly greater aboveground productivity likely contributing to greater ecosystem carbon uptake than northern Solidago populations, indicating that there is a lot of variation in carbon uptake within Solidago across its range. This interpretation is supported by previous work that documented that an increase in intraspecific variation leads to an increase in productivity within Solidago (Crutsinger et al. 2006, 2008), as well as within other plant species (Hughes et al. 2008; Fridley and Grime 2010). Latitudinal gradient studies have also documented an increase in among population productivity with decreasing latitudes (Oleksyn et al. 1999, 2000). For example, Oleksyn et al. (2000) found that European Pinus sylvestris populations vary in ANPP across their geographic ranges and northern populations had significantly lower productivity when compared to central populations. They suggest that variation in productivity between northern and central P. sylvestris populations resulted form variation in biomass allocation whereby northern populations had lower shoot:root allocation than that of central populations. Strong variation in productivity between northern and southern populations may also represent local adaptations to different climatic conditions in northern versus southern geographic ranges (Angert 2011; De Frenne 2011). Our results show that Solidago ANPP varies dramatically near the edges of its geographic range; specifically, southern populations produce more biomass than northern populations, which is likely due to local adaptations to the regional climate.

Northern Solidago populations began reproducing (i.e., first day of flowering) significantly earlier and producing lower inflorescence mass than southern Solidago populations; such differences are likely associated with local adaptations across Solidago's geographic range. A recent study by Genung et al. (2010) supports these results; their study documented intraspecific variation in flowering traits within local populations of Solidago. Intraspecific variation among Solidago genotypes in inflorescence abundance promoted greater inflorescence abundance with increasing genotypic diversity likely through complementarity effects (Hooper et al. 2005).

Differences in intraspecific flowering time and overall reproduction output (i.e., seed mass production) across tree/shrub/herbaceous populations along latitudinal and altitudinal gradients has also been documented, suggesting longer flowering intervals and greater reproduction output in southern than northern populations (Sills and Nienhuis 1995; Tungate et al. 2002; Bohlenius et al. 2006; Hall et al. 2007; Breen and Richards 2008; Chuine 2010; Dainese 2011). For instance, Dainese (2011) found variation in the reproductive performance of Dactylis glomerata across hay meadows in an elevational gradient with low-elevation individuals having larger inflorescences and larger seed masses than higher elevation individuals, meaning higher reproductive output in warmer/longer growing seasons than in colder/shorter growing seasons. Dainese (2011) suggested that longer growing seasons and/or warmer climates in lower elevation sites promote higher photosynthetic rates (i.e., greater C acquisition toward growth) and/or higher nutrient availability generating greater plant allocation toward reproductive performance than colder climates in higher elevations. Likewise, De Frenne (2011) documented increases in reproductive output of understory herbs in southern than northern populations. Our results documenting lower inflorescence mass in northern than southern Solidago populations support the finding by Dainese (2011) and De Frenne (2011) that northern climate-adapted populations (e.g., higher latitude/elevation) are functioning on lower levels of photosynthesis, where warm climate-adapted populations (e.g., lower latitude/elevation) are functioning on higher levels of photosynthesis that promote greater reproductive output (i.e., inflorescence mass).

Intraspecific variation in plant traits, in particular productivity and inflorescence mass, predicted an ecosystem function within a dominant old-field species. More productive individuals from southern populations delayed reproduction and were able to uptake more C than individuals from northern populations. Because of the shorter growing season in the northeastern United States (Conover and Present 1990), we suspect that individuals in northern populations were less productive and thus switched their allocation from growth to reproduction earlier in the year. A study by Abrahamson et al. (2005) supports this finding; they found great interspecific variation in allocation among five cooccuring Solidago species. Specifically, S. altissima had lower biomass allocation to roots relative to shoots and inflorescence mass, whereas S. juncea had high allocation toward roots and inflorescence mass, but low-biomass allocation to shoots. Abrahamson et al. (2005) suggested that interspecific variation in biomass allocation among cooccurring Solidago species was associated with local adaptations to environmental gradients. Likewise, we have shown that intraspecific variation in biomass allocation within S. Altisima varied among Solidago populations preadapted to varying environmental conditions. Our data extend the work by Abrahamson et al. (2005) to include intraspecific variation in allocation toward reproduction and growth, which then leads to changes in ecosystem functions.

Our work contributes to the growing body of literature demonstrating that intraspecific variation in plant performance traits can influence ecosystem function. Old fields, like grassland ecosystems, are C sinks (Gilmanov et al. 2010; Peichl et al. 2011; Zhang et al. 2011). However, grasslands can switch from carbon sinks to sources during drought years (Zhang et al. 2011), suggesting that abiotic stresses can alter the function of grassland ecosystems. As the climate continues to warm, the southern most Solidago populations may become larger C sinks, and northern populations must be able to change allocation regimes in order to accommodate the possible warmer and longer growing season. Future work should consider belowground production of rhizomes to assess biomass allocation more completely, which could be another important predictor of whole ecosystem carbon exchange.

Acknowledgments

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

We thank J. Areson, E. Austin, M. Cregger, L. Marsh, A. Proffit, O. Schmitz, T. Simberloff, H. Smith, K. Stuble, H. Tran, J. Welch, and P. Wright for help with field and laboratory work. K. McFarland provided space, advice, and resources at the UT greenhouse. This work was funded by grants from the UT Undergraduate Research Office (Summer Research Internship to L. B.) and from the UT Science Alliance Program (Junior Directed Research and Development to A. T. C.). L. S. was supported by an American Fellowship from AAUW.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
  • Abrahamson, W. G., and M. Gadgil. 1973. Growth and reproductive effort in goldenrods (Solidago, compositae). Am. Nat. 107:651661.
  • Abrahamson, W. G., and K. D. McCrea. 1986. Nutrient and biomass allocation in Solidago altissima—effects of 2 stem gallmakers, fertilization, and ramet isolation. Oecologia 68:174180.
  • Abrahamson, W. G., and A. E. Weis. 1997. Evolutionary ecology across three trophic levels: goldenrods, gallmakers, and natural enemies, Princeton Univ. Press, New Jersey .
  • Abrahamson, W. G., K. B. Dobley, H. R. Houseknecht, and C. A. Pecone. 2005. Ecological divergence among five co-occurring species of old-field goldenrods. Plant Ecol.177:4356.
  • Angert Al, C. L., L. J. Rissler, S. E. Gilman, J. J. Tewksbury, and A. J. Chunco. 2011. Do species’ traits predict recent shifts at expanding range edges? Ecol. Lett.14:677689.
  • Arnone, J. A., and D. Obrist. 2003. A large daylight geodesic dome for quantification of whole-ecosystem CO2 and water vapour fluxes in arid shrublands. J. Arid. Environ. 55:629643.
  • Baldocchi, D. 2008. Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56:126.
  • Bohlenius, H., T. Huang, L. Charbonnel-Campaa, A. M. Brunner, S. Jansson, S. H. Strauss, and O. Nilsson. 2006. CO/FT regulatory module controls timing of flowering and seasonal growth cessation in trees. Science 312:10401043.
  • Booth, R. E., and J. P. Grime. 2003. Effects of genetic impoverishment on plant community diversity. J. Ecol. 91L:721730.
  • Bousquet, P., P. Peylin, P. Ciais, C. Le Quere, P. Friedlingstein, and P. P. Tans. 2000. Regional changes in carbon dioxide fluxes of land and oceans since 1980. Science 290:13421346.
  • Breen, A. N., and J. H. Richards 2008. Irrigation and fertilization effects on seed number, size, germination and seedling growth: implications for desert shrub establishment. Oecologia 157:1319.
  • Chuine, I. 2010. Why does phenology drive species distribution? Philos. Transac. R. Soc. B Biol. Sci. 365:31493160.
  • Classen, A. T., J. Demarco, S. C. Hart, T. G. Whitham, N. S. Cobb, and G. W. Koch. 2006. Impacts of herbivorous insects on decomposer communities during the early stages of primary succession in a semi-arid woodland. Soil Biol. Biochem. 38:972982.
  • Conover, D. O., and T. M. C. Present. 1990. Countergradient variation in growth-rate—compensation for length of the growing-season among altantic silversides latitudes. Oecologia 83:316324.
  • Crutsinger, G. M., M. D. Collins, J. A. Fordyce, Z. Gompert, C. C. Nice, and N. J. Sanders. 2006. Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313:966968.
  • Crutsinger, G. M., L. Souza, and N. J. Sanders. 2008. Intraspecific diversity and dominant genotypes resist plant invasions. Ecol. Lett.11:1623.
  • Dainese, M. 2011. Impact of land use intensity and temperature on the reproductive performance of Dactylis glomerata populations in the southeastern Alps. Plant Ecol. 212:651661.
  • De Frenne, P., J. Brunet, A. Shevtsova, A. Kolb, B. J. Graae, O. Chabrerie, S. Cousins, G. Decocq, A. N. Deschrijver, M. Diekmann, et al . 2011. Temperature effects on forest herbs assessed by warming and transplant experiments along a latitudinal gradient. Glob. Change Biol. 17:3240–3253.
  • Enquist, B. 2011. Forest annual carbon cost: comment. Ecology 92:19941998.
  • Enquist, B. J., G. B. West, E. L. Charnov, and J. H. Brown. 1999. Allometric scaling of production and life-history variation in vascular plants. Nature 401:907911.
  • Fox, J. F., and G. C. Stevens. 1991. Costs of reproduction in a willow—experimental responses vs natural variation. Ecology 72:10131023.
  • Fridley, J. D., and J. P. Grime. 2010. Community and ecosystem effects of intraspecific genetic diversity in grassland microcosms of varying species diversity. Ecology 91:22722283.
  • Genung, M. A., J. P. Lessard, C. B. Brown, W. A. Bunn, M. A. Cregger, W. N. Reynolds, E. Felker-Quinn, M. L. Stevenson, A. S. Hartley, G. M. Crutsinger, J. A. Schweitzer, and J. K. Bailey. 2010. Non-additive effects of genotypic diversity increase floral abundance and abundance of floral visitors. PLoS One 5:1. doi: 10.1371/journal.pone.0008711
  • Gilmanov, T. G., L. Aires, Z. Barcza, V. S. Baron, L. Belelli, J. Beringer, D. Billesbach, D. Bonal, J. Bradford, E. Ceschia, et al . 2010. Productivity, respiration, and light-response parameters of world grassland and agroecosystems derived from flux-tower measurements. Rangeland Ecol. Manag. 63:1639.
  • Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler, Y. H. Chen, P. Ciais, S. Fan, et al . 2002. Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature 415:626630.
  • Hall, D., V. Luquez, V. M. Garcia, K. R. St Onge, S. Jansson, and P. K. Ingvarsson. 2007. Adaptive population differentiation in phenology across a latitudinal gradient in European Aspen (Populus tremula, L.): a comparison of neutral markers, candidate genes and phenotypic traits. Evolution 61:28492860.
  • Hirota, M., P. C. Zhang, S. Gu, H. H. Shen, T. Kuriyama, Y. N. Li, and Y. H. Tang. 2010. Small-scale variation in ecosystem CO2 fluxes in an alpine meadow depends on plant biomass and species richness. J. Plant Res. 123:531541.
  • Hooper, D. U., F. S. Chapin, J. J. Ewel, A. Hector, P. Inchausti, S. Lavorel, J. H. Lawton, D. M. Lodge, M. Loreau, S. Naeem, et al . 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75:335.
  • Houssard, C., and J. Escarre. 1991. The effects of seed weight on growth and competitive ability of rumex acetosella from 2 successional old fields. Oecologia 86:236242.
  • Hughes, A. R., B. D. Inouye, M. T. J. Johnson, N. Underwood, and M. Vellend. 2008. Ecological consequences of genetic diversity. Ecol. Lett. 11:609623.
  • Huston, M. A. 1997. Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia 110:449460.
  • Huston, M. A., and S. Wolverton 2009. The global distribution of net primary production: resolving the paradox. Ecol. Monogr. 79:343377.
  • Huxman, T. E., M. D. Smith, P. A. Fay, A. K. Knapp, M. R. Shaw, M. E. Loik, S. D. Smith, D. T. Tissue, J. C. Zak, J. F. Weltzin, et al . 2004a. Convergence across biomes to a common rain-use efficiency. Nature 429:651654.
  • Huxman, T. E., K. A. Snyder, D. Tissue, A. J. Leffler, K. Ogle, W. T. Pockman, D. R. Sandquist, D. L. Potts, and S. Schwinning. 2004b. Precipitation pulses and carbon fluxes in semiarid and arid ecosystems. Oecologia 141:254268.
  • Jacobs, A. F. G., B. G. Heusinkveld, and A. a. M. Holtslag. 2007. Seasonal and interannual variability of carbon dioxide and water balances of a grassland. Clim. Change 82:163177.
  • Janssens, I. A., A. Freibauer, P. Ciais, P. Smith, G. J. Nabuurs, G. Folberth, B. Schlamadinger, R. W. A. Hutjes, R. Ceulemans, E. D. Schulze, et al . 2003. Europe's terrestrial biosphere absorbs 7 to 12% of European anthropogenic CO2 emissions. Science 300:15381542.
  • Jarvis, P. G., A. J. Dolman, E. D. Schulze, G. Matteucci, A. S. Kowalski, R. Ceulemans, C. Rebmann, E. J. Moors, A. Granier, P. Gross, et al . 2001. Carbon balance gradient in European forests: should we doubt “surprising” results? A reply to Piovesan & Adams. J. Veg. Sci. 12:145150.
  • Johnson, M. T. J., M. J. Lajeunesse, and A. A. Agrawal. 2006. Additive and interactive effects of plant genotypic diversity on arthropod communities and plant fitness. Ecol. Lett. 9:2434.
  • Kato, T., and Y. H. Tang. 2008. Spatial variability and major controlling factors of CO2 sink strength in Asian terrestrial ecosystems: evidence from eddy covariance data. Glob. Change Biol. 14:23332348.
  • Kicklighter, D. W., A. Bondeau, A. L. Schloss, J. Kaduk, A. D. Mcguire, and N. P. P. M. I. Participants Potsdam. 1999. Comparing global models of terrestrial net primary productivity (NPP): global pattern and differentiation by major biomes. Glob. Change Biol. 5:1624.
  • Klumpp, K., and J. F. Soussana. 2009. Using functional traits to predict grassland ecosystem change: a mathematical test of the response-and-effect trait approach. Glob. Change Biol. 15:29212934.
  • Knops, J. M. H., W. D. Koenig, and W. J. Carmen. 2007. Negative correlation does not imply a tradeoff between growth and reproduction in California oaks. Proc. Natl. Acad. Sci. USA 104L:1698216985.
  • Koenig, W. D., J. M. H. Knops, W. J. Carmen, and R. D. Sage. 2009. No trade-off between seed size and number in the valley oak Quercus lobata. Am. Nat. 173:682688.
  • Ladinig, U., and J. Wagner. 2005. Sexual reproduction of the high mountain plant Saxifraga moschata Wulfen at varying lengths of the growing season. Flora 200:502515.
  • Li, S. G., J. Asanuma, W. Eugster, A. Kotani, J. J. Liu, T. Urano, T. Oikawa, G. Davaa, D. Oyunbaatar, and M. Sugita. 2005. Net ecosystem carbon dioxide exchange over grazed steppe in central Mongolia. Glob. Change Biol. 11:19411955.
  • Milla, R., L. Gimenez-Benavides, A. Escudero, and P. B. Reich. 2009. Intra- and interspecific performance in growth and reproduction increase with altitude: a case study with two Saxifraga species from northern Spain. Funct. Ecol. 23:111118.
  • Ni, J. 2000. Net primary production, carbon storage and climate change in Chinese biomes. Nord. J. Bot. 20:415426.
  • Oleksyn, J., J. Modrzynski, M. G. Tjoelker, R. Zytkowiak, P. B. Reich, and P. Karolewski. 1998. Growth and physiology of Picea abies populations from elevational transects: common garden evidence for altitudinal ecotypes and cold adaptation. Funct. Ecol. 12:573590.
  • Oleksyn, J., P. B. Reich, W. Chalupka, and M. G. Tjoelker. 1999. Differential above- and below-ground biomass accumulation of European Pinus sylvestris populations in a 12-year-old provenance experiment. Scand. J. Forest Res. 14:717.
  • Oleksyn, J., P. B. Reich, L. Rachwal, M. G. Tjoelker, and P. Karolewski. 2000. Variation in aboveground net primary production of diverse European Pinus sylvestris populations. Trees-Struct. Funct. 14:415421.
  • Patrick, L., J. Cable, D. Potts, D. Ignace, G. Barron-Gafford, A. Griffith, H. Alpert, N. Van Gestel, T. Robertson, T. E. Huxman, et al . 2007. Effects of an increase in summer precipitation on leaf, soil, and ecosystem fluxes of CO2 and H2O in a sotol grassland in Big Bend National Park, Texas. Oecologia 151:704718.
  • Peichl, M., P. Leahy, and G. Kiely. 2011. Six-year stable annual uptake of carbon dioxide in intensively managed humid temperate grassland. Ecosystems 14:112126.
  • Potts, D. L., T. E. Huxman, B. J. Enquist, J. F. Weltzin, and D. G. Williams. 2006. Resilience and resistance of ecosystem functional response to a precipitation pulse in a semi-arid grassland. J. Ecol. 94:2330.
  • Risch, A. C., and D. A. Frank. 2010. Diurnal and seasonal patterns in ecosystem CO2 fluxes and their controls in a temperate grassland. Rangeland Ecol. Manag. 63:6271.
  • Roff, D. A. 1992. The evolution of life histories: theory and analysis, Chapman and Hall, New York .
  • Saugier, B., J. Roy, and H. A Mooney. 2001. Estimations of global terrestrial productivity: converging toward a single number? Academic Press, San Diego , CA .
  • Schmitz, O. J. 2003. Top predator control of plant biodiversity and productivity in an old-field ecosystem. Ecol. Lett. 6:156163.
  • Schmitz, O. J. 2008. Effects of predator hunting mode on grassland ecosystem function. Science 319:952954.
  • Sills, G. R., and J. Nienhuis. 1995. Maternal phenotypic effects due to soil nutrient levels and sink removal in Arabidopsis thaliana (Brassicaceae). Am. J. Bot. 82:491495.
  • Souza, L., W. A. Bunn, J. F. Weltzin, and N. J. Sanders. 2011a. Similar biotic factors affect early establishment and abundance of an invasive plant species across spatial scales. Biol. Invasions 13:255267.
  • Souza, L., J. F. Weltzin, and N. J. Sanders. 2011b. Differential effects of two dominant plant species on community structure and invasibility in an old-field ecosystem. J. Plant Ecol. UK 4:123131.
  • Tilman, D., and D. Wedin. 1991. Plant traits and resource reduction for 5 grasses growing on a nitrogen gradient. Ecology 72:685700.
  • Tilman, D., P. B. Reich, J. Knops, D. Wedin, T. Mielke, and C. Lehman. 2001. Diversity and productivity in a long-term grassland experiment. Science 294:843845.
  • Tungate, K. D., D. J. Susko, and T. W. Rufty. 2002. Reproduction and offspring competitiveness of Senna obtusifolia are influenced by nutrient availability. New Phytol.154:661669.
  • Valentini, R., G. Matteucci, A. J. Dolman, E. D. Schulze, C. Rebmann, E. J. Moors, A. Granier, P. Gross, N. O. Jensen, K. Pilegaard, et al . 2000. Respiration as the main determinant of carbon balance in European forests. Nature 404:861865.
  • Van Dijk, A., and A. J. Dolman. 2004. Estimates of CO2 uptake and release among European forests based on eddy covariance data. Glob. Change Biol. 10:14451459.
  • Verburg, P. S. J., J. A. Arnone, D. Obrist, D. E. Schorran, R. D. Evans, D. Leroux-Swarthout, D. W. Johnson, Y. Q. Luo, and J. S. Coleman. 2004. Net ecosystem carbon exchange in two experimental grassland ecosystems. Glob. Change Biol. 10:498508.
  • Wardle, D. A. 1999. Is “sampling effect” a problem for experiments investigating biodiversity-ecosystem function relationships? Oikos 87:403407.
  • Whitham, T. G., J. K. Bailey, J. A. Schweitzer, S. M. Shuster, R. K. Bangert, C. J. Leroy, E. V. Lonsdorf, G. J. Allan, S. P. Difazio, B. M. Potts, et al . 2006. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7:510523.
  • Wilsey, B. J., and H. W. Polley. 2004. Realistically low species evenness does not alter grassland species-richness-productivity relationships. Ecology 85:26932700.
  • Wise, M. J., and W. G. Abrahamson. 2008. Ducking as a means of resitance to herbivory in tall goldenrod, Solidago altissima. Ecology 89:32753281.
  • Yuan, W. P., Y. Q. Luo, A. D. Richardson, R. Oren, S. Luyssaert, I. A. Janssens, R. Ceulemans, X. H. Zhou, T. Grunwald, M. Aubinet, et al . 2009. Latitudinal patterns of magnitude and interannual variability in net ecosystem exchange regulated by biological and environmental variables. Glob. Change Biol. 15:29052920.
  • Zhang, L., B. K. Wylie, L. Ji, T. G. Gilmanov, L. L. Tieszen, and D. M. Howard. 2011. Upscaling carbon fluxes over the Great Plains grasslands: sinks and sources. J. Geophys. Res.-Biogeosci. 116. doi: 10.1029/2010JG001504