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Oecologia (2009) 160:119–127
DOI 10.1007/s00442-009-1283-3
E C O S Y S T E M E CO L O G Y - O R I G I N A L P A P E R
Genetic mosaics of ecosystem functioning across aspen-dominated
landscapes
Michael D. Madritch · Samantha L. Greene ·
Richard L. Lindroth
Received: 6 August 2008 / Accepted: 8 January 2009 / Published online: 12 February 2009
© Springer-Verlag 2009
Abstract Genetic diversity is the foundation of all biodiversity, and the genetic variation within species is increasingly recognized as being important to ecosystem level
processes. Recent research demonstrates that plant genotype inXuences above- and belowground communities as
well as basic ecosystem functions. However, the extent to
which plant genotypes create spatial mosaics of genetically
mediated ecosystem processes in natural forests is uncertain. We use Populus tremuloides as a model system to
demonstrate the importance of plant genotype on carbon
and nitrogen cycling in natural systems. We identiWed 24
distinct P. tremuloides clones with multiple ramets across
25 km2 in southern Wisconsin, United States, using microsatellite makers. We then sampled clone leaf chemistry and
belowground nutrient content and microbial extracellular
enzyme activity. Aspen-induced variation in belowground
carbon and nitrogen content, and microbial activity, varied
widely among clones. Variation in green leaf chemistry
and belowground microbial activity were correlated with
genetic distance among clones, such that more genetically
distant clones created more divergent patches of ecosystem
processes. These data suggest that aspen genotypes create
spatial mosaics of genetically mediated ecosystem functioning
Communicated by Amy Austin.
M. D. Madritch · S. L. Greene · R. L. Lindroth
Department of Entomology,
University of Wisconsin, 237 Russell Labs,
1630 Linden Drive, Madison, WI 53706-1598, USA
Present Address:
M. D. Madritch (&)
Department of Biology, Appalachian State University,
572 Rivers Street, Boone, NC 28608, USA
e-mail: [email protected]
across natural landscapes and can therefore have evolutionary
consequences for co-occurring species.
Keywords Community genetics ·
Ecosystem functioning · IntraspeciWc variation
Introduction
Biodiversity includes the diversity of life at all levels, from
genes to ecosystems. It is driven fundamentally, however,
by diversity at the Wnest levels: genetic diversity determines
species diversity, which determines ecosystem diversity.
Despite signiWcant advances resulting from the biodiversity
and ecosystem functioning debate, a preponderance of studies has focused on species or functional group diversity,
and has been limited primarily to grassland ecosystems.
However, the species concept is largely a human construct;
the variation that matters to evolution—and ecology—is
genetic variation (Dawkins 1976). IntraspeciWc genetic
diversity has gained recent attention for its relationship
with species diversity. Positive feedbacks exist between
plant genetic diversity and species diversity such that intraspeciWc diversity encourages interspeciWc diversity and
vice versa (Booth and Grime 2003; Lankau and Strauss
2007). Positive correlations between species diversity and
genetic diversity exist even in species-rich tropical systems
(Wehenkel et al. 2006). Such studies highlight the importance of genetic diversity to species diversity, while others
have highlighted the importance of genetic diversity to ecosystem processes (Madritch and Hunter 2002; Schweitzer
et al. 2005; Crutsinger et al. 2006) and community composition (Dungey et al. 2000; Wimp et al. 2004, 2005;
Johnson and Agrawal 2005; Johnson et al. 2006; Bangert
et al. 2006a, b).
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Recognition that genetic diversity matters to communities and ecosystems has led to a resurgence in the gene’seye-view of natural systems championed by Dawkins
(1982). Extended phenotypes can inXuence multiple ecosystem-level processes (Whitham et al. 2003), including
decomposition and nutrient cycling (e.g., Madritch et al.
2006; Classen et al. 2007; Fischer et al. 2007; Schweitzer
et al. 2008). Recent empirical work elucidates speciWc
mechanisms by which genetic variation within plant species inXuences basic ecosystem processes. In particular, the
chemical variation within a dominant forest canopy species
can inXuence belowground processes. IntraspeciWc variation in secondary metabolites (such as tannins and other
phenolics) has been shown to inXuence leaf litter decomposition and belowground nutrient cycling (Madritch and
Hunter 2002; Schweitzer et al. 2004; Madritch et al. 2006)
as well as the diversity of associated plant communities
(Iason et al. 2005). Importantly, these extended phenotypes
have been demonstrated in long-lived forest tree species
and are likely to have legacy eVects on belowground communities and nutrient cycling.
One theoretical consequence of extended phenotypes in
dominant plant species is the creation of genetic mosaics of
ecosystem functioning (GMEFs) across landscapes
(Whitham et al. 2003; Madritch et al. 2006). GMEFs exist
when genetic diVerences within the same species create
patches of distinct ecosystem functioning (e.g., decomposition, primary production, N mineralization rates). DiVerent
species create distinct patches of nutrient cycling and primary productivity (Hobbie 1992; Vogt et al. 1995), as well
as belowground microbial communities (Priha et al. 2001;
Templer et al. 2003). While diVerent species are also necessarily diVerent genotypes, here we make a distinction
between species-level eVects and genetic mosaics: GMEFs
are created solely through intraspeciWc variation in genotype. Identifying and quantifying GMEFs is a major step in
further developing community and ecosystem genetics.
Populations of other species that evolve under the umbrella
of one genetically distinct patch could experience very
diVerent selection pressures than those that evolve under
the inXuence of another. Ultimately, the genetic diversity
within a species may drive evolution of associated species
and communities through the inXuence of plant genotype
on ecosystem processes. In short, plant genotype may be an
important part of the geographic mosaic of coevolution
(Thompson 2005).
While GMEFs have been described theoretically
(Whitham et al. 2003), and their potential mechanisms demonstrated empirically via controlled litter decomposition and
nutrient cycling studies (Madritch et al. 2006; Schweitzer
et al. 2004, 2005), we have little understanding of GMEFs
in natural systems. Moreover, the importance of genetic
diversity in natural ecosystems remains obscure because the
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Oecologia (2009) 160:119–127
relative eVects of genetic diversity and environmental (abiotic and biotic) factors on ecosystem processes are unclear
(Johnson and Stinchcombe 2007; Hughes et al. 2008). Here,
we demonstrate that GMEFs exist in natural ecosystems,
using mature aspen stands that have reclaimed abandoned
agricultural lands in southern Wisconsin, United States.
Methods
Field site description
Trembling aspen (Populus tremuloides) is an abundant,
early successional species that plays a major role in forests
throughout western and north-temperate North America. It
is the most widely distributed native tree species in North
America, and is among the most genetically variable plant
species known to science (Mitton and Grant 1996). Aspen
typically reproduces vegetatively, and a single genotype
(clone) can consist of multiple ramets ranging in number
from a few to several thousand individual trees. Our Weld
site was located at Pine Island State Wildlife Area near Portage, Wisconsin. Numerous distinct clones of aspen occupy
a 25 km2 area of reclaimed farmland, occurring as wooded
“islands” across a prairie landscape. The prairie comprises
a mixture of previously disturbed farmland and managed
grasslands. Reclaimed farmland is dominated by brome
grass (Bromus inermis), bluejoint grass (Calamagrostis
Canadensis), prairie cordgrass (Spartina pectinata), and
some reed canary grass (Phalaris arundinacea). The managed grasslands are dominated by big bluestem (Andropogon gerardii), switchgrass (Panicum virgatum), and little
bluestem (Schizachyrium scoparium). Soils throughout the
area are predominantly alluvial loams. We collected aspen
leaf and soil samples from 31 aspen stands that had minimal woody undergrowth. Tree cores were taken of the
smallest individuals within each aspen stand to ensure that
they were >15 years old. Stand size ranged from 12 to
40 m2, consisting of 30–100 individual ramets.
In June 2006 we collected 15 leaves from each of three
ramets located along transects within each aspen stand,
using a pole pruner or shotgun. Beneath every ramet sampled, we collected two 2.5 £ 10 cm soil cores and then
pooled them to create one composite soil sample per ramet.
Each aspen clone was paired with a neighboring grassland
site that was also sampled in duplicate at three locations for
soil analysis. The paired grassland sites served as a reference with which to compare the aspen sites, and diVerences
between the two were interpreted as aspen-induced (see
Statistical analysis). All soil cores and leaf samples were
kept on ice and immediately transported to the laboratory
for analysis. Leaf samples were freeze-dried and soil
samples were placed in a ¡20°C freezer. After the leaf
Oecologia (2009) 160:119–127
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samples were freeze-dried, they were Wnely ground and
processed for genetic and chemical analyses.
Microsatellite analysis
We extracted DNA from each of the three leaf samples for
the 31 aspen stands using the Qiagen DNeasy Plant Mini
Kit (Qiagen, Hilden, Germany). We employed 10 microsatellite primers (Table 1) that (1) represent diVerent linkage
groups and diVerent repeat unit sizes, from 2 to 6 base pairs
(Cole 2005); (2) amplify consistently; (3) display scorable
variation; and (4) show no evidence for null alleles. Reactions (in 25 l) were carried out according to Cole (2005),
using 12.5 l Promega PCR Master Mix 2£, 1.25 l of
0.1 g/l of the forward and reverse primers, 2 l of the
DNA template, and 8 l nuclease-free water. We followed
the thermal proWle given by Cole (2005) using a PTC-100
thermocycler (MJ Research, Waltham, MA). PCR products
were diluted with nuclease-free water and then sent to the
University of Wisconsin Biotechnology Center DNA
Sequence Laboratory for sequencing using an ABI 3700
automated DNA sequencer (Applied Biosystems, Foster
City, CA). Output received from the ABI 3700 was analyzed using GeneMarker 1.51 from SoftGenetics (LLC,
State College, PA) and genotypes were scored by eye.
Phytochemical analyses
Leaf tissue was analyzed for chemical constituents likely to
be important in inXuencing soil microbial activity (condensed tannins, phenolic glycosides, carbon and nitrogen
content, and lignin). Condensed tannins were quantiWed
with the n-butanol method of Porter et al. (1986) using a
Table 1 Simple sequence repeat (SSR) marker information
Name
Repeat
Original
size (bp)a
Reference
ORPM-059 (AT)6
213
Tuskan et al. (2004)
ORPM-127 (TG)8
200
Tuskan et al. (2004)
ORPM-149 (AT)4(CT)4
216
Tuskan et al. (2004)
ORPM-206 (GCT)7
196
Tuskan et al. (2004)
ORPM-344 (TC)8
229
Tuskan et al. (2004)
PMGC-575 (GA)n.a.
145
http://www.ornl.gov
WPMS-014 (CGT)28
245
Smulders et al. (2001)
WPMS-015 (CCT)14
193
Smulders et al. (2001)
WPMS-016 (GTC)8(ATCCTC)5 145
Smulders et al. (2001)
252
Smulders et al. (2001)
WPMS-020 (TTCTGG)8
Sequence information is listed at http://www.ornl.gov/sci/ipgc/
ssr_resource.htm
a
Refers to the allele size originally sequenced from Populus trichocarpa (ORPM and PMGC primers) or Populus nigra (WPMS primers)
from Cole (2005)
puriWed aspen tannin standard (Hagerman and Butler
1989). We quantiWed two tannin fractions. In addition to
the commonly measured soluble tannin fraction that is
extracted from plant tissue with 70:30 acetone: water (containing 10 mM ascorbic acid), we also measured the bound
tannin fraction. In short, bound tannins remain bound to the
plant tissue after solvent extraction and are likely important
to leaf litter decomposition. Bound tannins were analyzed
as a suspension of plant material after removal of soluble
tannins. We combined the soluble and bound tannin fractions into a total condensed tannin value for all of the statistical analyses, tables, and Wgures. The phenolic glycosides
salicortin and tremulacin were quantiWed by high-performance thin layer chromatography (HPLC) as described by
Lindroth et al. (1993). Carbon and nitrogen concentrations
were determined by combustion analysis with a ThermoFinnegan CNS analyzer (ThermoFinnegan, San Jose, CA).
Lignin fractions were estimated using the acetyl-bromide
method reported by Brinkmann et al. (2002).
Soil analysis
Soil carbon and nitrogen concentrations were measured
using a ThermoFinnegan CNS analyzer. We analyzed soil
microbial activity to identify the impacts of aspen clonal
variation on belowground processes. General microbial
activity was determined by measuring soil respiration in
July of 2006 using a PP Systems infrared gas analyzer (PP
Systems, Amesbury, MA). We also measured six extracellular enzymes to better describe soil microbial activity in
aspen clones and associated grassland sites: cellobiohydrolase and -glucosidase (involved in the degradation of
cellulose), leucine aminopeptidase (involved in the degradation of proteins), phenol oxidase and peroxidase (involved
in the degradation of aromatic compounds), and urease
(degrades urea). Enzyme assays, based on protocols by
Sinsabaugh et al. (2000) and Saiya-Cork et al. (2002), are
described in detail by Madritch et al. (2007). BrieXy, 1–2 g
equivalent dry mass soil from each sample were blended in
15 ml 50 mM acetate buVer using steel balls and a modiWed
paint shaker. Soil extract (400 l) was added to 2 ml microcentrifuge tubes in duplicate for each of the six enzyme
assays along with a set for sample blanks. Substrates for
each of the six enzymes were added to samples and allowed
to react for 2–3 h. Aliquots were removed from duplicate
tubes and placed in 96-well microplates to measure absorbance at the appropriate wavelengths. All activities are
expressed as mol substrate h¡1 g¡1 soil.
Statistical analysis
Of the 31 aspen stands, 24 were identiWed as genetically
distinct clones on the basis of ten microsatellite markers.
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The remaining seven aspen stands were comprised of two
genotypes. We excluded stands with multiple genotypes
from all subsequent data analyses because our sampling
protocol (one composite soil sample per ramet; three ramets
per clone) precluded treating each ramet as a separate clone
with replicated soil samples.
Because the aspen clones in this study were mature and
long-lived, we have no pre-colonization data for soil nutrient
content or microbial activity. Consequently, belowground
diVerences among aspen clones are confounded with spatial
location, as each genotype occurred only once. To account
for spatial variation, and to strengthen the inferences that can
be drawn from a correlative study, we paired each aspen
clone with an adjacent grassland site. The pairing facilitated
attribution of the diVerences between clonal soils and grassland soils to colonization by speciWc aspen genotypes. Thus,
our belowground responses are quantiWed as the diVerence
between the aspen and paired grassland sites.
In addition to using paired aspen and grassland sites, we
address the lack of genotype replication across space by
performing Mantel tests between genetic distance and ecosystem responses (Madritch and Hunter 2002). We calculated genetic distance between pairs using Microsatellite
Analyzer v4.05 (Dieringer and Schlotterer 2003). We then
correlated genetic distance with soil response distance by
using Mantel tests. Here, we treat the activity of the soil
microbial community as an extended phenotype of the
overlying aspen clone. Covariance between phenotypic
similarity of the aspen-induced soil community (measured
here as the diVerence between paired aspen and grassland
sites) and aspen relatedness would indicate that there is signiWcant genetic variation among extended phenotypes and
that they occur in natural landscapes (Bailey et al. 2006).
We used simple ANOVAs to test for the eVects of aspen
genotype on leaf chemistry and on the diVerence between
grassland and aspen soil metrics, using ramets and paired
soil sample locations as units of replication (three per
clone). We also used simple correlations to show the relationship between average clonal leaf chemistry and the
average diVerence in soil metrics.
Results
As expected from previous work, aspen clones used in our
study varied signiWcantly in all measurements of leaf chemistry, except for carbon content (Fig. 1; Table 2). Likewise,
variance in soil carbon and nitrogen content, as well as
microbial activity, was also inXuenced strongly by aspen
clone (Figs. 1, 2; Table 3). We hypothesized that variation
in leaf, and subsequent litter, quality would inXuence
belowground microbial activity. However, because we had
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Oecologia (2009) 160:119–127
123
䉳 Fig. 1 Clonal variation in aspen leaf chemistry and in soil respiration,
nitrogen, and carbon content. For clarity, soil data shown are for
variation among clones rather than for diVerences between aspen
clones and grasslands, although both approaches yield similar results.
See Tables 2 and 3 for statistical results of leaf and soil analyses,
respectively. All concentrations are given as % dry weight. Bars
Average (§SE) of three ramets or locations (each sampled twice)
along a transect
Table 2 Analysis of variance (ANOVA) results showing the eVect of
aspen clone on green leaf chemistry
F-value
R2 a
P
2.67
0.56
0.002
C:N
2.92
0.58
%P
11.56
0.85
<0.0001
5.86
0.74
<0.0001
Leaf chemistry
%N
%C
Condensed tannin
Salicortin
Tremulacin
Lignin
NS
<0.001
7.28
0.78
<0.0001
14.33
0.87
<0.0001
2.26
0.55
0.008
DF = 23, 71
a
Calculated from ANOVA sums of squares
no pre-colonization information for soil microbial activity,
we used paired aspen and grassland data. While the common land use history and relatively homogeneous landscape would suggest a similar starting point for all sites
prior to aspen colonization, some spatial heterogeneity
exists and the paired design allows us to isolate the aspeninduced changes in belowground processes. Henceforth, we
focus on the diVerence (denoted by “”) between aspen
clones and the surrounding grasslands.
DiVerences in belowground microbial enzyme activity
were correlated with leaf chemistry (Table 4), suggesting
that clonal diVerences in belowground microbial activity
were caused by diVerences in aboveground inputs into the
detrital pathway. As expected, belowground soil microbial
activity was also correlated with soil carbon and nitrogen
content (Table 5).
Genetic distance was correlated with both leaf chemistry
and soil microbial activity, indicating that genetically similar
clones had similar chemistry and similar microbial activity
belowground (Table 6). Green leaf chemistry was also correlated with belowground microbial activity, but weakly so.
Fig. 2 Clonal variation in extracellular enzyme activities. For clarity, 䉴
data shown are for variation among clones rather than for diVerences
between aspen clones and grasslands, although both approaches yield
similar results (see Table 3 for statistical results). All values based on
per gram soil dry weight. Bars Average (§SE) of three locations (each
sampled twice) along a transect
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Oecologia (2009) 160:119–127
Table 3 ANOVA results showing the eVect of aspen clone on soil responses
F-value
(clone)
R2 (clone)a
P (clone)
Respiration
3.12 (2.09)
0.60 (0.50)
%N
5.27 (9.16)
%C
3.83 (5.62)
C:N
Soil response
Table 5 Correlations between microbial enzymes and soil carbon
and nitrogen (calculated as the diVerence between aspen clones and
adjacent grassland sites)
Enzyme activity
Soil carbon
Soil nitrogen
<0.001 (0.015)
-Glucosidase
0.60 (0.002)
0.61 (0.002)
0.72 (0.82)
<0.001 (<0.001)
Cellobiohydrolase
0.63 (<0.001)
0.66 (0.001)
0.65 (0.74)
<0.001 (<0.001)
Leucine aminopepsidase
0.56 (0.005)
0.57 (0.003)
2.08 (2.64)
0.50 (0.57)
0.016 (0.002)
Urease
0.56 (0.005)
0.56 (0.005)
-Glucosidase
6.29 (17.22)
0.75 (0.90)
<0.001 (<0.001)
Cellobiohydrolase
6.46 (8.42)
0.76 (0.80)
<0.001 (<0.001)
R values (P values in parentheses). Peroxidase and phenol oxidase
activity were not correlated with either soil carbon or soil nitrogen
Leucine
aminopepsidase
6.07 (6.41)
0.74 (0.76)
<0.001 (<0.001)
Urease
NS (5.81)
NS (0.74)
NS (<0.001)
Peroxidase
3.85 (3.85)
0.65 (0.73
<0.001 (<0.001)
Phenol oxidase
2.11 (2.11)
0.50 (0.66)
<0.001 (<0.001)
Results from analyses of aspen¡grassland sites () are presented, with
the results from the same analysis of the raw clone data given in
parentheses (clone). DF = 23, 71
a
Calculated from ANOVA sums of squares
Spatial distance had no eVect on microbial activity: sites
closer to each other were not more similar than those separated by larger distance. Conversely, soil carbon and nitrogen content were inXuenced more by spatial distance than by
genetic distance of the overlying clone. Consequently, the
strong correlation between soil microbial activity and soil
carbon and nitrogen content was probably driven more by
spatial variation in soil nutrient availability than by aspeninduced variation in extracellular enzyme activity (Table 6).
Discussion
This work demonstrates that aspen genotypes can create
genetic mosaics of ecosystem functioning (GMEFs) across
natural landscapes. Previous work that investigated the
inXuence of intraspeciWc genetic variation on ecosystem
processes has been limited largely to plot-level manipulative research. Such experiments are valuable because they
show causality of ecosystem response to plant genotypes,
yet they trade ecologically realistic conditions for tightly
controlled experimental conditions. IntraspeciWc interactions
are particularly important in natural ecosystems (Hughes
et al. 2008), and the presence of co-occurring species has the
potential to override any ecosystem level eVects of intraspeciWc variation (Madritch and Hunter 2004). Despite the limitations inherent in observational studies (discussed below),
our data show that genetic variation in a single species can
create spatial patches of genetically mediated ecosystem
functioning in natural forests. The genetics of foundation
species may be an important component of the spatial variation that leads to variance in the strength and speed of
coevolution among species (Thompson 1999).
The aspen genotypes we sampled were not replicated
across the landscape; every aspen clone was a diVerent
genotype in a diVerent location. Consequently, genotype
and spatial location were confounded. We addressed this
limitation in two ways. First, all of our response variables
were calculated as the diVerence between an aspen clone
and an adjacent grassland site, to account for large-scale
spatial variation in soil nutrient availability and grassland
species community. Second, we addressed the lack of genotype replication across space by performing Mantel correlations with genetic distance and belowground response,
similar to the approach used by Bangert et al. (2006a). We
found signiWcant, albeit weak, relationships between
genetic distance and green leaf chemistry, and genetic distance and soil microbial enzyme activity. Conversely, spatial distance did not inXuence either leaf chemistry or soil
enzyme activity. The weak correlation between genetic variation and microbial activity is not surprising as variation in
Table 4 Correlations between leaf chemistry and soil microbial enzymes (calculated as the diVerence between aspen clones and adjacent grassland sites)
Leaf chemistry
-Glucosidase
Cellobiohydrolase
Leucine
aminopepsidase
Urease
Peroxidase
Phenol oxidase
C:N
¡0.53 (0.008)
¡0.51 (0.010)
¡0.50 (0.012)
¡0.33 (0.10)
NS
NS
Condensed tannins
NS
NS
NS
¡0.39 (0.06)
¡0.71( <0.0001)
¡0.70 (<0.0001)
Lignin
NS
NS
NS
NS
¡0.66 (<0.001)
¡0.73 (<0.0001)
R values (P values in parentheses). There were no signiWcant correlations involving soil respiration or leaf %P
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125
Table 6 Mantel tests describe correlations between genetic, spatial, chemical, and microbial activity matrices
Distances
Genetic
Spatial
NSa
Spatial
Green leaf chemistry
Green leaf chemistry
0.22 (0.038)
NS
Enzyme activities
0.23 (0.031)
NS
0.18 (0.015)
Soil C and N
NS
0.48 (0.012)
NS
Enzyme activities
0.57 (0.001)
Mantel statistics (r) from 9,000 randomized runs are reported as measures of eVect size (P values in parentheses)
a
P > 0.1
neutral molecular marker underestimates non-neutral
genetic diversity (Reed and Frankham 2001; Merila and
Crnokrak 2001). Despite the fact that neutral molecular
markers underestimate adaptive variance, the two are often
well correlated (Merila and Crnokrak 2001, but see Holderegger et al. 2006). Genetic distance in neutral markers has
also been correlated with arthropod communities at both
large and small scales in other Populus systems (Bangert
et al. 2006a, 2008). Given that our soil metrics were calculated as the diVerence between paired aspen and grassland
sites, and thus aspen-induced, and that genetic distance was
positively correlated with leaf chemistry and soil microbial
activity response, we conclude that more genetically diVerent aspen clones create more diVerent patches of belowground microbial activity.
Past work has shown large chemical variation in both
green leaf and senesced litter in aspen (Lindroth et al.
2002). This chemical variation has been correlated with
diVerences in litter mass loss (Madritch et al. 2006) and soil
respiration (Madritch et al. 2007). As expected, we found
large variation in all measured aspects of chemical quality
in green leaves (Table 2). Leaf quality (C:N, tannin, lignin)
was correlated with aspen-induced variation in soil enzyme
activity (“ enzyme”, Table 4). Clones with high C:N
leaves had less enzymatic activity in underlying soils. Likewise, clones with high tannin and lignin were associated
with soils that had low urea, peroxidase, and phenol oxidase activities. Phenolics have long been known to decrease
extracellular enzyme activity (Benoit and Starkey 1968).
However, there is often an interactive eVect of nitrogen and
phenolics on peroxidase and phenol oxidase activity. For
instance, Carreiro et al. (2000) found that high nitrogen
combined with high lignin availability decreased phenol
oxidase activity, whereas high nitrogen and low lignin
availability increased phenol oxidase activity. Similarly,
Waldrop et al. (2004) found that the eVect of N deposition
on peroxidase and phenol oxidase activity is ecosystemspeciWc and likely depends upon the presence of speciWc
belowground fungal communities. While some evidence
suggests that enzyme activities can increase when substrates are available in complex forms (Allison and Vitousek 2005), here all enzyme activities declined in clones
with increasing C:N and phenolic compounds.
Leaf litter inputs provide roughly half of the organic
inputs to the detrital pathway, with the other half being
provided by belowground inputs, especially Wne root turnover (Coleman and Crossley 1996). Variation in plant
chemistry has long been known to drive belowground processes (Cadisch and Giller 1997). Although not measured
here, Wne roots can contain substantial amounts of tannins
and likely aVect belowground processes in a similar way as
does chemical variation in aboveground inputs (Kraus et al.
2003). We chose to measure green leaf chemistry to ensure
that chemistry and genotype information were collected
from the same tree. Although leaf chemistry changes as
leaves senescence, there is a strong correlation between
green leaf and senesced litter chemistry (Lindroth et al.
2002). Of key interest to us, is that variation in chemistry is
maintained throughout leaf senescence, and repeated studies have shown that this is the case in aspen (Lindroth et al.
2002; Madritch et al. 2006, 2007). Leaf chemistry was correlated with several belowground processes, although
weakly so in some cases. Genetic variation is likely more
important at local scales, while environmental variation
(e.g., soil C and N) probably trumps genetic variation at
larger scales (Stiling and Rossi 1995; Johnson and Agrawal
2005, but see Bangert et al. 2006b).
Genetic diversity can have large inXuences on ecological
processes, and in some cases can exceed the eVects of
species diversity (Bangert et al. 2005; Shuster et al. 2006;
Schweitzer et al. 2008). Here, the phenotypic similarity of
aspen-induced soil communities was correlated with aspen
genotype relatedness. A clonal, pioneer species such as
trembling aspen has the ability to create genetic mosaics of
ecosystem functioning that dictate future selection pressures for associated plants and animals at local sites across
a landscape. Furthermore, factors (e.g., browsing, climate)
that inXuence the survival of young clones may have enduring ecosystem-level impacts as early selection pressures
dictate which clones survive to create large patches. By
creating biotically mediated variation in belowground processes, genetic variation likely plays an important role in
the evolutionary and co-evolutionary dynamics in natural
forests (Whitham et al. 2006). We conclude that evolutionary linkages between plant genetic variation and soil microbial communities are an important, and likely widespread,
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phenomenon in natural forest communities (Schweitzer
et al. 2008).
Acknowledgments Funding was provided by an REU supplement to
NSF DEB-0344019 to R.L.L. and M.D.M. We thank the Pine Island
Wildlife Area and the Wisconsin Department of Natural Resources for
site use permission. All experiments comply with the current laws of
the country in which they were performed.
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