Download Luxury consumption of soil nutrients

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.
Such processes can have profound effects on the transient behaviour of ecosystems, although modelling
procedures need further development before exact predictions about the effects of climate change on ecosystem behaviour can be made.
Acknowledgements
This work is funded by National Science Foundation
grant DEB0087046, ‘LTER Cross site 2000: Interactions between climate and nutrient cycling in arctic and
subarctic tundras’. We also thank Tiffany Miley for her
help in the field.
© 2003 British
Ecological Society,
Journal of Ecology,
91, 664–676
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Received 11 December 2002
revision accepted 1 April 2003