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Journal of Ecology 2003 91, 664 – 676 Luxury consumption of soil nutrients: a possible competitive strategy in above-ground and below-ground biomass allocation and root morphology for slow-growing arctic vegetation? Blackwell Publishing Ltd. M. T. VAN WIJK*§¶, M. WILLIAMS*, L. GOUGH†, S. E. HOBBIE‡ and G. R. SHAVER§ *School of Geosciences, University of Edinburgh, Darwin Building, King Buildings, Mayfield Road, Edinburgh EH9 3JU, UK, †Department of Biology, University of Texas at Arlington, Box 19498, Arlington, TX 76019, USA, ‡Department of Ecology, Evolution & Behaviour, University of Minnesota, 1987 Upper Buford Circle, St Paul, MN 55108, USA, and §Marine Biology Laboratory, The Ecosystem Center, Woods Hole, MA 02543, USA Summary 1 A field-experiment was used to determine how plant species might retain dominance in an arctic ecosystem receiving added nutrients. We both measured and modelled the above-ground and below-ground biomass allocation and root morphology of non-acidic tussock tundra near Toolik Lake, Alaska, after 4 years of fertilization with nitrogen and phosphorus. 2 Compared with control plots, the fertilized plots showed significant increases in overall root weight ratio, and root biomass, root length and root nitrogen concentration in the upper soil layers. There was a strong trend towards relatively more biomass below ground. 3 We constructed an individual teleonomic (i.e. optimality) plant allocation and growth model, and a competition model in which two plants grow and compete for the limiting resources. 4 The individual plant model predicted a strong decrease in root weight ratio with increased nutrient availability, contrary to the results obtained in the field. 5 The increased investment in roots in the fertilized plots found in the field could be explained in the competition model in terms of luxury consumption of nutrients (i.e. the absorbance of nutrients in excess of the immediate plant growth requirements). For slow-growing species with relatively low phenological and physiological plasticity it can be advantageous to increase relative investment into root growth and root activity. This increased investment can limit nutrient availability to other fast-growing species and, thereby, preclude the successful invasion of these species. 6 These results have implications for the transient response of communities and ecosystems to global change. Key-words: arctic, biomass allocation, competition, nutrients, tundra Journal of Ecology (2003) 91, 664–676 Introduction Carbon allocation in plants is a key issue in understanding and describing their response to changes in environmental factors, including nutrients, water and © 2003 British Ecological Society ¶Correspondence address: Mark van Wijk, Wageningen University, Plant Production Systems, Postbus 430, 6700 AK Wageningen, the Netherlands (tel. + 31 317 482141, fax + 31 317 484892, e-mail [email protected]). carbon dioxide (CO2) availability (Chapin 1980; Aerts & Chapin 2000; Poorter & Nagel 2000). Several models exist to describe plant allocation, each with its own hypothesis. In teleonomic or optimality models, some function of the plant is optimized (growth or biomass accumulation), using unspecified mechanisms (Mäkelä & Sievänen 1987; Thornley 1995). In these models, the plant invests in means of obtaining elements that are limiting, in order to maximize growth. An example of a teleonomic model is the functional balance or 665 Biomass allocation of arctic vegetation © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 functional equilibrium theory, which states that plants respond to a decrease in above-ground resources with increased allocation to shoots (leaves) or to a decrease in below-ground resources with increased allocation to roots (Cannell & Dewar 1994; Poorter & Nagel 2000). Only Thornley’s transport-resistance model (Thornley 1972; Thornley 1998) represents a truly mechanistic approach to carbon allocation, including phloem loading, unloading and transport processes. However, the precise mechanisms by which allocation is regulated are still unknown and the processes incorporated in Thornley’s transport-resistance model have not yet been shown to determine carbon transport and allocation: for example, the changes in sucrose concentration in the sinks are quite insufficient to alter phloem turgor significantly (Farrar et al. 1995; Bijlsma & Lambers 2000) and, as noted by Farrar (1992) and discussed by Farrar et al. (1995), there must be a means of communicating the sugar status of the sink to the phloem. In this study, we analyse above-ground and belowground biomass and growth in an arctic field-experiment, to determine how species might retain dominance in an ecosystem receiving added nutrients. High latitude systems are interesting for studying above-ground and below-ground relationships because, in contrast to temperate ecosystems, they have large relative amounts of below-ground biomass, in the order of 75 – 80% (Dennis & Johnson 1970; Jonasson 1982; Hobbie & Chapin 1998). As large amounts of organic matter are stored in arctic and subarctic ecosystems, the study of below-ground processes, including plant allocation, is important from a global change perspective (Hobbie et al. 2000). Another important characteristic of high latitude systems is their extremely low availability of nutrients (Shaver et al. 2001). Nutrients become available only in flushes, followed by periods of very low nutrient availability. Plants growing in these types of ecosystems are thought to be adapted to these characteristics by having a large root biomass, and associated mycorrhizas, achieved in large part through low root turnover, the ability to take up organic nitrogen sources, and effective nutrient retention strategies (Chapin 1980; Aerts & Chapin 2000). Species with these traits absorb nutrients in excess of their immediate growth requirements during the flushes (luxury consumption), and these reserves are then used to support growth when external nutrients are not available for plant uptake (Chapin 1980). Fast-growing herbaceous species tend to show phenotypic plasticity and clear responses in allocation to root vs. shoot biomass when subjected to experimental nutrient enrichment. Slow-growing species, on the other hand, are less plastic and show little or no response (Lambers & Poorter 1992). Lower rates of both growth and nutrient absorption limit the adaptability of the slow-growing species to changes in the environment (Chapin 1980; Aerts & Chapin 2000). However, a large survey by Reynolds & D’Antonio (1996) found no evidence that fast-growing species that are adapted to high soil fertilities exhibit the highest levels of morphological plasticity, or that plasticity is positively associated with competitive ability. At the Arctic Long-Term Ecological Research (LTER) site at Toolik Lake, Alaska, several longer-term fertilization experiments are being carried out to gain insight into vegetation–environment interactions and into the possible effects of climate change on vegetation distributions (e.g. Shaver et al. 2001). We determined above-ground and below-ground biomass of the vascular plants in control and fertilized plots on non-acidic tussock tundra (Gough et al. 2000; Gough & Hobbie 2003), after 4 years of annual treatment with nitrogen (N) and phosphorus (P). Two models were tested to determine whether they can explain the results of the field experiment. The first model is an individual teleonomic plant growth model, presented in detail in Thornley (1995). Its key hypothesis is that plant production and biomass are optimized, because most effort is allocated towards acquiring the most limiting resource. In the second model two plants compete for the limiting resources (in this case either light or nitrogen). In this model, plant success may be better determined by the degree to which key resources are monopolized, e.g. by luxury consumption (Chapin 1980), and thus denied to competitors, rather than by optimization of individual growth rate. The hypothesis of monopolization is clearly linked to Tilman’s R* rule (Tilman 1982; Holt et al. 1994), which states that the winner in exploitative competition is the species that depresses equilibrium resource abundance (denoted R*) to the lowest amount consistent with its own maintenance, relative to those required by competing species. R* does not include the pool of ‘luxury’ resources, which is an internal plant resource allowing such plants to depress the external resource level further and, thereby, potentially resulting in competitive advantage. The critical R* for a given consumer is defined as the external level of a certain resource at which that consumer’s birth rate just matches its death rate. Even quite complex models of interspecific competition for a single limiting resource, with numerous parameters, can be characterized by the R* rule (Tilman 1982; Holt et al. 1994). Our competition model aimed to link carbon allocation of plants to resource capture, and thereby to competition between plant species with different allocation patterns. The key goal was to determine feasible survival strategies for a plant adapted to an environment with low nitrogen availability when nitrogen availability is increased. Materials and methods A non-acidic moist tussock tundra site (Walker et al. 1994; Gough et al. 2000) in the vicinity of the Arctic LTER site at Toolik Lake (68°38′N, 149°36′W, 760 m above sea level) on the North Slope of the Brooks Range, Alaska, has been subjected to annual N and P 666 M. T. van Wijk et al. fertilization since 1997. The community differs from the more commonly investigated moist acidic tussock tundra (e.g. Chapin et al. 1993; Shaver et al. 2001) because of its soil pH, soil nutrients and its greater species diversity and lower shrub abundance. Common species in this vegetation type are Carex bigelowii, Eriophorum vaginatum, Eriophorum angustifolium ssp. triste, Dryas integrifolia, Cassiope tetragona and Salix reticulata. Three large replicate blocks each contain two 5 by 20 m plots arranged parallel to each other, separated by a 1-m wide buffer strip. One of each pair of plots received 10 g m −2 y −1 N as NH4NO3 and 5 g m −2 y−1 P as P2O 5, in the form of granular agricultural fertiliser, in July 1997 and each June following snowmelt from 1998 onwards. Between late July and early August 2001, four samples were taken in each of the three control and three fertilized plots to estimate above- and below-ground plant biomass. A 20 by 20 cm quadrat was sampled and all living above-ground biomass and below-ground stems (rhizomes) were harvested and sorted to species. Another smaller quadrat (about 7 by 7 cm with exact dimensions recorded) was located along a randomly selected side of the larger quadrat and cut to a depth of 10 cm in the mineral layer. Underneath the quadrat, an additional soil core of 5 cm diameter was taken to the depth of the permafrost layer. The below-ground samples were divided into 0–5 cm deep below the green– brown moss transition, 5–12.5 cm deep, 12.5–20 cm deep and 20 cm and deeper, with a further separation at the organic-mineral border. Roots were plucked from the organic horizon and washed out of the mineral horizon and repeatedly rinsed before spreading out in water, after which root length and root surface area were determined using the Win-Rhizo Scanning System (Regents Instruments, Quebec, Canada). All samples (above-ground tissue, rhizomes and roots) Fig. 1 Schematic overview of the individual plant allocation model (influences as dotted lines). were then dried for several days at 50–60 °C in a field drying oven and weighed. The dried root samples were analysed for total N content using a Perkin-Elmer CHN analyser. The samples were pooled by block for each treatment separately, and the samples from 12.5 to 20 cm were pooled with those deeper than 20 cm. The block means, obtained by averaging the four quadrats, were used to calculate treatment means and standard errors. A paired t-test was used for comparing the fertilized vegetation with the control vegetation, in which the blocks are the pairs (so n = 3, and the test has 2 degrees of freedom). The plant model (Fig. 1, Table 1) is based on the teleonomic model described by Thornley (1995), which differs from his transport–resistance model (Thornley 1972). Plant dry matter (all parts in kg m−2) consists of Table 1 Symbols used in the individual plant and competition models © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 Symbol Description Unit BN C G Grt Gsh IN,S Lrt Lsh M MC MN Msh Mrt N Nsoil OG,C OG,N P P* UN Storage of substrate nitrogen Carbon substrate concentration in plant Structural growth Structural growth going into root Structural growth going into shoot Inflow of nitrogen into soil compartment Litter production of roots Litter production of shoot Total plant mass Carbon substrate in plant Nitrogen substrate in plant Mass of shoot Mass of roots Nitrogen substrate concentration in plant Plant-available nitrogen in soil Carbon substrate output going into growth Nitrogen substrate output going into growth Carbon substrate production through photosynthesis Competition corrected photosynthesis Uptake of nitrogen from soil kg m−2 d−1 kg kg−1 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 kg m−2 kg m−2 kg m−2 kg m−2 kg m−2 kg kg−1 kg m−2 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 kg m−2 d−1 667 Biomass allocation of arctic vegetation structure and of substrates (carbon (C) and nitrogen (N)). Structure is divided into shoot (sh) and root (rt), but the substrate pool is assumed to be common to the whole plant. The shoot and root mass of the plant (respectively Msh and Mrt) depend on structural growth (denoted by G) and litter production (denoted by L): dM sh = Gsh − Lsh , dt dM rt = Grt − Lrt dt eqn 1 Growth is dependent on C and N substrate concentrations through a saturation function: kG MCN G= b1C + b2 N + b3CN eqn 2 The differential equations for the substrate masses of C and N are: dMC = P − OG,C , dt eqn 5 where P is the carbon substrate production through photosynthesis, OG is output to growth (G ), UN is the uptake of nitrogen and BN is the loss of substrate nitrogen due to storage. The function for N-uptake from the soil is: UN = where M C = C, M dM N = U N − OG,N − BN dt kN M rt M rt N 1 + K 1 + J MN N eqn 6 in which kN, KMN and JN are parameters (see Table 2). Nitrogen uptake is related positively to the root mass and negatively to the internal nitrogen substrate concentration. The function for photosynthesis is: M N = N M and M = Msh + Mrt and kG, b1, b2 and b3 are parameters (see Table 2). Partitioning of the growth between shoot and root depends on the growth allocation parameter α: Gsh = αG, Grt = (1 − α)G, with 0=α=1 eqn 3 Litter production occurs through: Lsh = klitt M sh klitt M rt , Lrt = K M , litt K 1+ 1 + M, litt M M P= kC M sh M sh C 1 + K 1 + J MC C eqn 7 in which kC, KMC and JC are constants (see Table 2). Photosynthesis is related positively to the shoot mass and negatively to the internal carbon substrate concentration. The outputs of substrate C and N to growth are: eqn 4 OG,C = fC G, in which klitt and KM,litt are parameters (see Table 2). OG,N = fNG eqn 8 in which fC and fN are constants (see Table 2). Table 2 Overview of parameter values and their units © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 Name of coefficient Unit Value α a1 a2 a3 a4 a5 b1 b2 b3 c1 c2 d ƒC ƒN JC JN kC kG klitt KM, litt KMC KMN kN – d−1 – – – – (kg substrate C) (kg structure)−1 (kg substrate N) (kg structure)−1 (kg substrate C) (kg substrate N) (kg structure)−2 – (kg N in soil)−1 d−1 (kg structure)−1 (kg structure)−1 kg substrate C (kg structure)−1 kg substrate N (kg structure)−1 kg C (kg shoot structure)−1 d−1 (kg substrate C) (kg substrate N) (kg structure)−2 d−1 d−1 kg structure kg structure kg structure kg N (kg root structure)−1 d−1 See text 0.003 0.15 0.3 0.15 0.3 0.001 0.001 1.0 0.55 0.5 See text 0.5 0.025 0.05 0.5 See text 0.05 0.05 0.5 1.0 1.0 See text 668 M. T. van Wijk et al. If the substrate nitrogen concentration is higher than the substrate carbon concentration, the latter will be the limiting factor for growth. If the nitrogen substrate concentration gets very high, this will lead to a limitation of the nitrogen uptake according to equation 6. To test the effects of ongoing nitrogen uptake by the plant, which is not important in the individual plant case but will be important in the competition version of this model (see below), we introduce a capacity for the plant to store nitrogen. This storage capacity prevents the substrate nitrogen concentration from increasing to limiting amounts, but will only be used if the substrate concentration of nitrogen is higher than that of carbon (i.e. if carbon is limiting for growth): if N > C, then dBN = d ( N − C ); dt dN soil = I N , S − U N1, 2 − a1N soil + a2 f N Lsh,1 + a3 f N Lrt,1 + dt a4 f N Lsh,1,2 + a5 f N Lrt,1,2 eqn 10 where Nsoil is the amount of plant available nitrogen in the soil (in g), IN,S is external input (in kg m−2 per day), UN1,2 is the N-uptake by both plants, a1Nsoil stands for leaching of nitrogen from the soil (in kg m−2 per day), Lsh,rt,1 and Lsh,rt, 2 are the shoot and root litter production of plant 1 and 2, respectively, and a4 ƒNLsh, 1, 2 and a5ƒNLrt,1,2 are N inputs through litter production of the shoot and the shoot (in kg m−2 per day); a1, a2, a3, a4 and a5 are parameters (see Table 2) and ƒN is the parameter for substrate N-uptake during growth (see equation 8). The N-uptake function of equation 6 is adapted to represent possible limitation of nitrogen plant uptake by the available nitrogen in the soil: otherwise dBN =0 dt eqn 9 where BN is the substrate nitrogen storage of the plant and d is the rate constant. The storage is seen as being of infinite size (it does not fill up), although in reality it might be completely filled as well as being emptied when nitrogen-limitation occurs. In this formulation the plant does not use this excess nitrogen to obtain carbon more effectively, although the potential rate of photosynthesis could be increased by channelling it into synthesis, e.g. of Rubisco. A simple N-bucket model is used containing two plants, each growing according to the allocation model described above (Fig. 2). The values of the model parameters and output are not linked to measured values of nitrogen availability and nitrogen fluxes in reality, but the model is used as a conceptual representation of the processes, and will be analysed qualitatively. The change of nitrogen in soil is given by © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 UN = 1 eqn 11 1 − Nsoil c1 + c2 e M rt N + + 1 1 K MN J N kN M rt where c1 and c2 are parameters (see Table 2). Nitrogen uptake is related positively to the root mass, negatively to the internal nitrogen substrate concentration, and positively to the amount of nitrogen in the soil (Nsoil). N-uptake and light interception are partitioned between the two plants to represent the competition between the two plants for the limited available nitrogen and light. The rates of photosynthesis and N-uptake are made proportional to the fraction of available light intercepted by the whole canopy and the fraction of soil volume exploited by all the roots, respectively. According to the partitioning formulas derived in Herbert et al. (1999), the rate of photosynthesis and N-uptake are then multiplied by a weighting function that accounts for the overshadowing of one plant by the other in the manner of the Lambert–Beer law (Monsi & Saeki 1953). As we do not take into account any difference in the canopy or root dominance factor between the two plants, the equation becomes: Fig. 2 Schematic overview of N-bucket model combining growth models for two plants (influences as dotted lines). 669 Biomass allocation of arctic vegetation if Msh_1 > Msh_2 Rabs _1 = (1 − e − k( Msh_1−Msh_2 ) ) + 0.5e − k( Msh_1−Msh_2 ) (1 − e −2 kMsh_2 ); otherwise Rabs _1 = 0.5e − k( Msh_2 −Msh_1 ) (1 − e −2 kMsh_1 ) eqn 12 in which Rabs is the amount of light absorbed, k is the extinction factor of the Lambert–Beer law, and the numbers 1 and 2 denote Plant 1 and Plant 2. For example, in the case of light absorption, the first term in the first part of equation 12 accounts for the radiation absorption by the shoot biomass of Plant 1 that is above the shoot biomass of Plant 2, whereas the second term accounts for the radiation absorption of the rest of the shoot biomass of Plant 1 (which is equivalent to the total shoot biomass of Plant 2). In the second equation of equation 12 (i.e. Msh_2 > Msh_1) radiation absorption by Plant 1 only takes place through the second term of the first equation. The corrected photosynthesis then becomes: P*i = Pi Rabs _i eqn 13 2 ∑R abs _ j j =1 In the same way the root N-uptake is corrected for competition. The k-value for photosynthesis and N-uptake distributions were 0.5 m 2 kg−1 and 0.0008 m2 kg−1, respectively (Herbert et al. 1999). To determine whether the models could explain the measurements obtained in field experiments, we first analysed the effects of increased nitrogen availability in the individual model. Both nitrogen and phosphorus were added to the field plots, but, for simplicity, we simulate the effects of only one element, nitrogen. In the teleonomic model, we hypothesize that the plant uses the optimal value for the growth allocation parameter α during growth (i.e. the one that yields the maximum total plant biomass at steady state). The parameter kN represents the amount of nitrogen uptake per unit of root biomass (equation 6). The value of this parameter is affected by nutrient availability (Thornley 1995) and also, for example, by the root surface area per unit of root biomass. We varied the value of kN to address changes in N-availability, using the values shown in Table 2 for the other parameters. Thus, we determined the relationship between nitrogen availability and the ‘optimal’ α value, and thereby also the relationship between nitrogen availability and steady state root and shoot biomass. We then compared the qualitative behaviour of the biomass of the plant compartments with the results from the field experiments. We next tested the effects of increased nitrogen availability in the competition model by examining the optimality of the growth allocation parameter in the presence of another plant. We explored whether simply decreasing the root allocation was the only effective strategy to cope with increased nutrient availability, or whether there were other responses that allow a plant adapted to low nitrogen availability to survive when nitrogen availability is increased. We therefore tested two situations, one in which two plants with two different growth allocation parameters (α) compete with each other, both taking up only the nitrogen they need to maximize their growth, and one in which one plant with a high root allocation (i.e. a low α) takes up nitrogen in excess of its needs (i.e. through luxury consumption), thereby employing an R* strategy and preventing the other plant with a lower root allocation from taking over. The model parameterizations of these two runs are given in Table 3 (other parameters as in Table 2). The competition modelling exercise does not deal with the presence of individual species, nor with shifts in the dominance of individual species due to fertilization. It simply analyses qualitatively the change in competitiveness between two types of plant species (high root vs. shoot allocation and low root vs. shoot allocation) and quantifies whether luxury consumption can be a successful competitive strategy for the high root investor under fertilization. Results and discussion The results obtained from the field measurements (Table 4, Fig. 3a) show a greater below-ground biomass investment in the fertilized plots compared with the control plots. In the two uppermost layers there is a significant increase in root biomass (paired t-test, P < 0.05 for both layers; Fig. 3a). There was a significant increase in root weight ratio (i.e. root biomass Table 3 Model parameterizations for competition model Run 1 and Run 2 (see Tables 1 and 2 for other parameters and descriptions) © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 IN,S Coefficient Plant 1 Plant 2 Unit Start with 0.015 g day−1, after steady state is obtained 0.15 g day−1 α kN kC d 0.4 0.02 0.04 0 (Run 1) 0.2 (Run 2) 0.6 0.02 0.04 0 – kg N (kg root structure)−1 d−1 kg C (kg shoot structure)−1 d−1 d−1 670 M. T. van Wijk et al. Table 4 Measured values of root biomass, shoot biomass and root length with standard error of the mean in parenthesis Variable Control Fertilized P-value in paired t-test Total biomass (g m−2) Shoot biomass (g m−2) Rhizome biomass (g m−2) Root biomass (g m−2) Biomass below-ground / biomass above-ground Root biomass /total biomass Below-ground biomass /total biomass Root biomass /shoot biomass Root length (km m−2) 677 (32) 194 (7) 224 (10) 259 (36) 2.5 (0.3) 0.37 (0.03) 0.71 (0.02) 1.4 (0.2) 3.0 (0.2) 1000 (106) 240 (28) 208 (23) 552 (114) 3.2 (0.5) 0.54 (0.05) 0.76 (0.03) 2.3 (0.5) 12.9 (2.2) 0.06 0.21 0.48 0.07 0.10 0.02 0.07 0.06 0.01 Fig. 3 Depth distribution of root biomasses (a) and root lengths (b); error bars are standard errors of the mean and the asterisks indicate statistically significant differences (P < 0.05) between control (black) and fertilized (grey) plots. © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 divided by total plant biomass, RWR) and a strong trend for more total root biomass in the fertilized plots as compared with the control plots (Table 4). Another strong effect of fertilization was to cause significantly greater root length (Table 4, Fig. 3b). No significant differences were present in shoot or rhizome biomass, although the increase in total plant biomass neared significance. Strong trends were also present for greater root vs. shoot ratios and below- vs. above-ground biomass in fertilized plots. These results differ from those generally observed. Above-ground vs. below-ground biomass ratios in many biomes tend to decrease strongly with increased fertilization, although this pattern has been observed most clearly in fast-growing species (Chapin 1980; Aerts & Chapin 2000). Our results cannot be explained simply by the presence of slow-growing species with low phenotypic plasticity, because the species that were dominant after 4 years of fertilization had more, principally longer, roots. There was therefore a clear shift in root allocation strategy rather than a lack of responsiveness. Increased root lengths in response to fertilization, and especially in response to increased N availability, have been shown in other studies (Linkohr et al. 2002; Weber & Day 1996) and have also been shown as a response to the presence of nutrient-rich patches (Hodge et al. 1999). The nitrogen content of the roots in the upper soil layers (0–5 cm) increased significantly with fertilization, whereas there was little response below 12.5 cm in N-content (Table 5), or in root biomass or length (Fig. 3). Fertiliser was applied to the top of the soil, and the plants probably reacted by stimulating root growth in the upper layers, where the nutrient concentration will be highest. Due to the permafrost layer, deeper soils are colder, and growth rates at greater soil depth may also be limited by low temperatures. The increased nitrogen concentration combined with increased root biomass, resulted in a large (fivefold) increase in total root nitrogen, suggesting that luxury consumption may have occurred (Chapin 1980; Padgett & Allen 1999; Lawrence 2001). We did not determine the species composition of the harvested roots. From the above-ground harvest we know that biomass and cover of graminoids and forbs increased with fertilization, but 4 years of treatment did not lead to any new species being present or a major shift in species dominance (Gough & Hobbie 2003). As Table 5 Measured nitrogen (N) contents of the roots at three different depths Control site %N SE 0–5 cm 0.153 5–12.5 cm 0.123 > 12.5 cm 0.357 Fertilized site %N SE 0.07 0.553 0.03 0.443 0.11 0.250 P-value in paired t-test 0.04 < 0.01 0.15 0.104 0.08 0.37 671 Biomass allocation of arctic vegetation it is likely that the above-ground patterns are reflected below ground (e.g. Hobbie & Chapin 1998), we assume that no major shift at the species level has taken place, and that the increased root matter was contributed by the species that were dominant above ground. In other words, one or more of the species present in the control plots had invested more in below-ground resource capture in response to fertilization. The above-ground vs. below-ground ratios measured in the control plots were similar to those (around 70–80% of the total vascular plant biomass below ground) that have been found in acidic tussock tundra and wet tundra systems in Alaska (Dennis & Johnson 1970; Hobbie & Chapin 1998), and alpine shrub tundra (Webber & May 1977). However, within the subarctic this ratio can vary between ecosystems: Jonasson (1982) found a below-ground biomass percentage of only 54% in a subarctic Betula-Salix shrub tundra, whereas Webber and May (1977) found values of 92–96% in alpine fellfield and moist/wet meadow tundra, respectively. The depth distribution of the roots was somewhat less consistent with earlier results, where quantitative assessment of root patterns indicate that up to 90% of roots occur in the top 10 cm of soil in tundra (Dennis & Johnson 1970; Dennis 1977; Webber & May 1977; Gebauer et al. 1996). Only 50% of the total root biomass occurred in the top 10 cm of our control plots, although, because rhizomes are not present below 10 cm, approximately 70% of total below-ground biomass was present in the upper 10 cm. About 40% of the total root length was present in the top 10 cm. The responsiveness of arctic tundra vegetation to experimental manipulations has been shown to be slow (see for example Shaver & Chapin (1986) and Chapin et al. (1993)), and so our 4-year results can be considered to represent short-term responses. A short-term increase in below-ground investment by one or more species is, however, totally opposite to results obtained in many other experimental manipulations (Chapin 1980; Aerts & Chapin 2000). We therefore used the models to explain this increased investment in belowground resource capture from a strategic point of view. © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 Despite the high variability and the small number of replicates, the differences we found were large enough to show several clear effects of fertilization: significant increases in root weight ratio, root length and in the uppermost two soil layers in root biomass. Trends for increased allocation to total root as compared with total shoot biomass and to total below-ground as compared with total above-ground biomass, although not significant, were strong enough, and in an unexpected direction, to be of considerable biological interest. We tested whether the two models could explain the observed increases in root weight ratio (calculated as the root biomass divided by the root biomass plus the shoot biomass, with no distinction made between rhizome and shoot biomass) and increased root length with fertilization. Simulations of the individual model result in increasing values for the optimal value of α (the growth allocation parameter) with increasing values of kN (Fig. 4). A rise in kN represents increased nitrogen availability in the soil, which also leads to a strong increase in growth. At low N availabilities the increased growth results in an increase in absolute root biomass (Fig. 4b), despite the increasing optimal value of α (Fig. 4a), but root biomass decreases if kN continues to increase. The root weight ratio always decreases with increasing nitrogen availability (Fig. 4c). The individual model clearly cannot explain the increased below-ground investment observed following fertilization. From an optimization point of view, the best strategy is to invest most of the assimilated plant carbon into the plant compartment that tries to capture the most limiting element. As the nitrogen availability of the soil increases, nitrogen becomes less and carbon becomes more limiting to growth, so that the optimal strategy is to shift the investment from the roots to the shoots, in order to capture more light and thus increase photosynthesis. Although the individual teleonomic type of model and its related partner, the functional equilibrium model, can capture a large variety of responses that have been found in experiments (see for example Poorter & Nagel 2000) it cannot explain the results from our field experiment. The model starts with a low N inflow into the soil compartment (0.015 g N day−1) under which Plant 1, with a low α-value (i.e. high root vs. shoot allocation), clearly obtains a higher plant biomass than Plant 2 (Fig. 5). However, when the inflow rate is increased to 0.15 g N day−1 at t = 1000 days, Plant 2, with the higher α, gradually becomes dominant (Fig. 5a). The slow response is mainly caused by the time taken for Plant 2 to build up biomass from an initially low level to the point where it can out-compete Plant 1. The success of Plant 2 can, however, be prevented by Plant 1 if the latter keeps on accumulating N by storing it, so that no self-limitation comes into play in equation 11 (Fig. 5b). In this simulation the values of soil N (Fig. 5c) stay stable even though inflow is increased, and Plant 2 remains out-competed. This effect is observed at N inflow rates between 0.12 and 0.17 g N day−1, suggesting that Plant 1 can ‘regulate’ soil N-availability over a range of inflow rates up to 40% higher than that at which Plant 2 would otherwise be the superior competitor. Inflows in excess of 0.175 g N day−1 exceed Plant 1’s capacity for regulation and Plant 2 out-competes Plant 1, giving patterns as in Fig. 5(a,c) (without storage). 672 M. T. van Wijk et al. Fig. 4 Effect of kN (nitrogen availability) on optimal α (growth allocation parameter) in the individual plant allocation model. (a) Optimal α-values at different kN values. (b) Root biomass in steady state at the optimal α for a particular kN. (c) root weight ratio (i.e. root biomass/total biomass) values in steady state at the optimal α for each kN. Fig. 5 Simulations using the competition model with two plants (see Table 3). Plant 1 has a high growth allocation parameter (α) and Plant 2 a low α; at time = 1000 days, the inflow-rate of nitrogen is increased from 0.015 to 0.15 g N day −1. (a) Biomass of Plants 1 (–) and 2 (- -), assuming Plant 1 cannot store excess N. (b) Biomass of Plants 1 (–) and 2 (- -), allowing Plant 1 to store excess N. (c) N-availability in the soil using Plant 1 with (heavy line) and without (light line) storage. (d) Total root weight ratios in system using Plant 1 with (heavy line) and without (light line) storage. © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 If Plant 1 cannot store, N concentrations increase until Plant 2, with much lower N-uptake rates because of the lower root weight ratio, takes over, reaching a steady state value after the definitive Plant 1–Plant 2 biomass distribution is reached (Fig. 5a,c). The simulated values of N-availability should not be compared directly with values measured in the field (see, for example, Giblin et al. 1991), and should only be interpreted qualitatively. Nevertheless, the simulated values are close to the range expected in the field. The root weight ratio at the system level (calculated by dividing the total of Plant 1 and 2 root biomass by the total of Plant 1 and 2 plant biomass) rises after the increase in the nitrogen inflow into the system regardless of whether Plant 1 shows storage (Fig. 5d). Only after a prolonged period, in which Plant 2, which has a 673 Biomass allocation of arctic vegetation low growth allocation factor, slowly takes over dominance in the system, does the root weight ratio at system level start to decrease. The increase in root weight ratio that was found in the field measurements (Table 4) can therefore be explained by both forms of the competition model. This could, however, be a temporary effect, whereby the higher initial presence of a high root investor leads to an increase in system root weight ratio that is not maintained once the shoot investor becomes favoured. We added the extra pulse of N when the plants were of equal age, rather than equal size. In the latter case, simulations showed that luxury consumption only allowed Plant 1 to out-compete Plant 2 at inflow rates up to 0.13 g N day−1. Thus the strategy of luxury consumption is more suited to preventing the invasion of higher shootinvesting species than to out-competing such species if they show high abundance even at low N availability. Increased below-ground biomass allocation can decline after a species with high allocation to roots is no longer able to take up all of the added N, allowing a species with low allocation to roots to dominate. The threefold greater root length per unit surface area observed in the fertilized plots cannot be explained by such a temporary shift in balance, as no major shifts occurred in species dominance, although graminoid abundance did increase somewhat. There must therefore have been increased investment in capturing below-ground resources. We therefore tested whether the strategy of luxury consumption could also explain the increased root length. In equation 11, kN represents the amount of N that can be captured per unit root biomass and, as increased root length will mean increased surface area of roots for the same amount of root biomass, this can be represented by an increase in kN. In two further runs of the competition model we parameterized Plant 1 and Plant 2 to have more similar growth allocation parameter values than before (α = 0.4 and 0.5, respectively, Table 6), but made Plant 2 a superior carbon fixer per unit of shoot biomass (kC = 0.04 and 0.06 kg C (kg shoot structure)−1 d−1, respectively, for Plants 1 and 2) (Table 6). We tested whether the success of this superior carbon fixer at higher rates of N inflow can be prevented by Plant 1 increasing its effectiveness of soil N uptake by increasing the kN for Plant 1 from 0.02 in Run 3 to 0.025 kg N (kg root structure)−1 d−1 in Run 4. In both runs Plant 1 has the ability to store all its extra-acquired N. Increasing root length per unit root biomass again allows Plant 1 to prevent the dominance of Plant 2 at higher nutrient inflows, despite the latter making more effective use of its shoot biomass (Fig. 6a,b). Plant 1 increases its uptake of N, and thereby limits the increase of nitrogen availability in the soil (Fig. 6c). The extra N taken up by the increased root length is stored, rather than further increasing the growth of Plant 1, which is now limited by carbon fixation. Modelling either increased root biomass or increased root length can therefore achieve control of N availability. The above-ground/below-ground ratio of about 1 : 3 is more marked than suggested by the allocation parameter (α) of 0.4 used in the model. So far, we have assumed that Plant 1 does not make use of the N that is taken up, but if it can be used to out-compete other plants with lower root investment, then investment in roots makes even more sense. We therefore incorporated a quantitative relationship between the amount of N stored and the photosynthetic parameter kC of equation 7. kC = 0.04 + fNstorage if Nstorage < 0.01 kg kC = 0.044 if Nstorage > 0.01 kg eqn 14 in which f is a coefficient with unit kg C (kg shoot structure)−1 d−1 (kg N in storage)−1, and tested competition model at different values of α. The influence of this ‘effective’ use of the available N in the storage on the ‘best’ allocation strategy of Plant 1 was assessed by estimating Plant 1 biomass in the steady state. The parameterization of Plant 2 was equivalent to model runs 1 and 2, with a high shoot/root allocation (α = 0.6), ‘normal’ root N uptake and shoot C uptake efficiencies (kN = 0.02 kg N (kg root structure)−1 d−1 and kC = 0.04 kg C (kg shoot structure)−1 d−1) and, of course, no storage (d = 0 d−1). Effective use of the stored nitrogen clearly makes a higher allocation to roots more favourable (Fig. 7), such that over the whole range of α-values tested in Fig. 7 Plant 1 out-competes Plant 2. In the steady state Table 6 Model parameterizations for competition model Run 3 and Run 4 © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 IN,S Coefficient Plant 1 Plant 2 Unit Start with 0.015 g /day, after steady state is obtained 0.15 g /day α kN 0.4 0.02 (Run 3) 0.025 (Run 4) 0.04 0.2 0.5 0.02 – kg N (kg root structure)−1 d−1 0.06 0 kg C (kg shoot structure)−1 d−1 d−1 kC d 674 M. T. van Wijk et al. Fig. 6 Competition model simulations for Runs 3 and 4 (see Table 6). (a) Biomass of Plants 1 (–) and 2 (- -), Plant 1 without increased root length. (b) Biomass of Plants 1 (–) and 2 (- -), Plant 1 with increased root length. (c) N-availability in the soil using Plant 1 with (heavy line) and without (light line) increased root length. Fig. 7 Simulated steady state biomass of Plant 1 for different values of the growth allocation parameter (α) assuming an effective use of stored nitrogen in photosynthesis. Plant 2 is present in only very low amounts, because of the increased amount of photosynthesis by Plant 1 due to its extra nitrogen uptake. Reducing α from 0.4 to 0.3 in the original model parameterizations (Table 3, Fig. 5) would have led to Plant 2 out-competing Plant 1, regardless of storage, whereas allowing N-use suggests that an α of 0.3 gives maximum biomass and is thus the optimal value for allocation. Even limited effective use of absorbed N (the photosynthetic capacity increases by no more than 10%) strongly favours investment in the root system. However, incorporating such N-use into the individual allocation model had little effect apart from a slight decrease in optimal growth allocation parameter. © 2003 British Ecological Society, Journal of Ecology, 91, 664–676 The simulation results of the individual teleonomic model cannot explain the increased root investment. This model predicts a decrease in root weight ratios, because the limitation of below-ground resources is decreased by fertilization. The competition model can, however, explain the field-measurements if luxury consumption is incorporated. By limiting the nitrogen availability to a value at which the lower root investing plant cannot increase its growth, the higher root investing plant can retain dominance. Observed increases in root length, and nitrogen content, and, to a lesser extent, in shoot biomass (Tables 4 and 5), are consistent with strategies predicted by the competition model. In the slow-growing arctic vegetation, adapted to low nutrient levels, Tilman’s R*-strategy of limiting resources to the lowest amount consistent with a species’ maintenance (Tilman 1982) suggests that plants that absorb nutrients in excess of their needs (i.e. luxury consumption) may prevent other species from taking over. The competition model suggests that within certain limits of nutrient availability this strategy can be achieved by increased below-ground investment. Nutrient availability in arctic vegetation increases only during pulses, which are unpredictable both in time and space. Alternatively therefore, plants may be selected to maximize nutrient uptake from these pulses for later use. Luxury consumption has been shown to occur during nutrient flushes (Chapin 1980), and may simply continue if levels are increased (e.g. with extended fertilization). The selective pressure was not therefore the benefit of preventing other species from taking the nutrients up and thereby out-competing the slow-growing plants, but that of enabling continued growth when nutrients are not available. The competitive hypothesis could, in fact, be just a side-effect of the individual hypothesis. However, luxury consumption is found in other vegetation types (Padgett & Allen 1999; Lawrence 2001). Tilman & Wedin (1991) showed that for five grasses 675 Biomass allocation of arctic vegetation growing on a nitrogen gradient, the soil nitrate concentration was an inverse function of root mass, and that the two species that reduced soil solution nitrogen to the lowest amounts had the highest root allocation. Hodge et al. (1999) showed that in a heterogeneous environment root length of two grasses was dramatically increased in organic patches and that the species with the higher root length density in a patch took up more N than its competitors. Root investment and root proliferation may therefore be an important competitive strategy for plants. Spatial and temporal variability of nutrient availability, and the different forms in which nutrients are available, will influence competition and coexistence, beyond our simple model (Hooper & Vitousek 1998; McKane et al. 2002). We do not know for how long luxury uptake can benefit the plant or the relative roles of the enlarged root systems with respect to resource capture and resource storage, a critical factor in its adaptive significance. Roots showed a strong increase in N concentration, although the rhizome is probably the predominant storage organ in arctic species. Distinguishing between the two hypotheses should be possible in experimental research in which plant types are included that all have luxury uptake (Grime 2001). Our results have implications for prediction of the effects of climate change on increases in nutrient availability, and thereby on the distribution of plant species. We showed that plant characteristics, as well as chemical and microbial immobilization (Chapin et al. 1986), can influence the response of ecosystems to increased nutrient availability. The potential for storage or buffering of important drivers of ecosystem change, such as shown by plants with high below-ground investment for significant changes in N-input, is currently missing in models such as TEM (McGuire et al. 2000) that describe biogeochemical changes in Arctic ecosystems. 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