Phenotypic variation in oak litter influences short- and long-term

nutrient cycling through litter chemistry

Michael D. Madritch*, Mark D. Hunter

Institute of Ecology, University of Georgia, Athens, GA 30602-2202, USA

Received 18 February 2004; received in revised form 8 July 2004; accepted 3 August 2004

Abstract

The influence of intraspecific variation on ecosystem functioning is relatively unknown. We investigated the effects of litter phenotype on

carbon and nitrogen fluxes in the litter and soil, and on microarthropod and bacterial populations over a 3-year period. Different litter

phenotypes significantly affected carbon and nitrogen fluxes. Short- and long-term fluxes within single phenotype treatments were

significantly, but unpredictably, different from a mixed phenotype treatment. Fluxes were associated with variation in litter chemistry which

has a significant genetic component. We found no effects of phenotype identity on soil bacterial or microarthropod communities. However,

persistent litter phenotype effects upon carbon and nitrogen fluxes support our previous suggestion that losses in genetic diversity may

influence ecosystem processes.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: Ecosystem functioning; Intraspecific variation; Decomposition; Tannin

1. Introduction

The species composition of terrestrial ecosystems can

have important ecosystem level consequences, and species

identity and diversity both play important roles in regulating

ecosystem functions (see McCann (2000), Loreau et al.

(2001), Cameron (2002) and Naeem (2002) for reviews). The

vast majority of net primary production eventually enters the

detrital pathway (Coleman and Crossley, 1996), yet we know

very little concerning how tree species composition affects

litter decomposition. Species composition typically has

significant, yet idiosyncratic effects on litter decomposition,

and non-additive effects are usually the result of specific

species combinations rather than species diversity per se

(Chapman et al., 1988; Wardle et al., 1997; Nilsson et al.,

1999). Belowground responses to changes in biodiversity are

highly variable (Loreau et al., 2001). For instance, soil

respiration can respond positively (Briones and Ineson, 1996;

0038-0717/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.soilbio.2004.08.002

* Corresponding author. Present address: Department of Entomology,

University of Wisconsin, 237 Russell Labs, 1630 Linden Drive, Madison,

WI 53706, USA. Tel.: C1 608 262 4319.

E-mail address: madritch@entomology.wisc.edu (M.D. Madritch).

McTiernan et al., 1997), randomly (Chapman et al., 1988), or

not at all (Bardgett and Shine, 1999) to increased litter

diversity. We know even less about the ecosystem level

consequences of intraspecific composition, despite the fact

that genetic variation is important to several population level

processes (Amos and Balmford, 2001). Whitham et al. (2003)

emphasize the potential importance of plant genotype to

ecosystem functioning, especially in light of widespread and

drastic reductions in ecosystem genetic diversity caused by

anthropogenic forces (Ledig, 1992; Vitousek et al., 1997).

The genotypic identity of leaf litter has been linked to

nutrient cycling previously by Driebe and Whitham (2000)

and Treseder and Vitousek (2001). In two cottonwood

species and their hybrids, genetically mediated variation in

condensed tannin concentrations differentially affected in-

stream litter decomposition rates (Driebe and Whitham,

2000). Likewise, genetically distinct populations of

Metrosideros polymorpha trees exhibited considerable

variation in genetically mediated litter chemistries import-

ant to decomposition and nitrogen cycling (Treseder and

Vitousek, 2001). Here, we focus solely on intraspecific

variation and the direct effects of this variation on

ecosystem processes during decomposition.

Soil Biology & Biochemistry 37 (2005) 319–327

www.elsevier.com/locate/soilbio

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327320

We have shown previously that the intraspecific

composition of turkey oak (Quercus laevis) litter can have

ecosystem consequences during the initial stages of litter

decomposition (Madritch and Hunter, 2002). Our initial

results covered an 18-month period and it was unknown

whether or not long-term effects on nutrient cycling would

persist. In addition, it was unclear whether effects of litter

phenotype were due solely to variation in litter chemistry, or

due to associated variation in the soil microbial and/or

microarthropod communities. Here, we present nutrient

cycling data from the extended 3-year litter decomposition

experiment in which we varied the intraspecific composition

of Q. laevis litter. In addition, we also include analyses of

bacterial and microarthropod communities in soil under-

lying litter treatments.

2. Methods

Our field site and experimental design are described in

detail in Madritch and Hunter (2002). Briefly, during leaf

fall of 1999, we hand collected litter from nine Q. laevis

individuals selected randomly from a pool of 1572 adult tree

previously genotyped by Berg and Hamrick (1994) using

Table 1

Initial litter and soil chemistry for the nine individual phenotypes and nine site lo

Initial litter Condensed

tannin (%)

Hydrolysable

tannin (%)

Total phenolics

(%)

L

1 17.84G1.07 16.15G0.35 68.33G7.36 1

2 21.08G3.19 13.27G1.31 57.47G6.15 1

3 18.81G0.58 12.04G0.44 48.89G6.58 1

4 15.96G0.38 7.94G1.57 60.76G3.45 1

5 15.62G1.15 13.40G3.71 67.46G4.10 1

6 10.80G0.98 12.18G0.22 61.29G5.40 2

7 18.66G0.16 10.21G0.17 64.17G2.79 1

8 22.28G0.01 13.05G2.75 63.31G3.91 1

9 25.82G0.41 11.30G2.31 63.61G4.23 1

d.f. 8

F 11.76

P 0.0006

Initial site soils Ammonium

availability

Nitrate

availability

C (%) N

1 12.63G3.98 2.98G0.94 2.107G0.149 0

2 4.43G0.97 1.55G0.51 1.916G0.091 0

3 8.58G2.23 3.66G0.88 1.988G0.092 0

4 8.08G3.12 2.4G0.57 1.957G0.152 0

5 10.89G3.00 2.34G0.42 1.486G0.088 0

6 5.55G1.23 2.8G0.46 1.876G0.153 0

7 3.29G0.67 2.53G0.60 1.413G0.050 0

8 8.30G2.17 1.83G0.36 1.602G0.145 0

9 7.48G1.50 1.32G0.45 1.700G0.139 0

d.f. 8

F 2.74

P 0.0099

ANOVAs performed on transformed data when necessary. Results are presented a

by bold summary statistics given below each chemistry column.

nine polymorphic allozyme loci. We established sets of 10

litter boxes, 9 with single phenotypes and 1 with an equal

mix of all 9, at each of nine sites (nZ90 boxes total). Litter

boxes were open-bottomed, meter square boxes covered

with vinyl coated hardware mesh to exclude non-treatment

litter. Ten litterbags containing 10 g of dry litter were

introduced into each box at the start of the experiment. We

added 150 g of loose treatment litter to each box (equivalent

to average litter fall per m2) at the beginning of the study

and at the beginning of the second and third years. Since we

were unable to locate replicate clones using microsatellite

markers (Klaper et al., 2001), we can only interpret results

as effects of phenotypic composition and not genetic

composition per se.

After an initial 3-month collection, litter bags were

collected each summer and winter (3, 6, 12, 18, 24, 30, and

36 months) and analyzed for carbon, nitrogen, litter

chemistry, and microbial carbon content. Bags were also

removed for microarthropod extractions using Tullgren

funnels. Three 2!10 cm soil cores were taken from each

litter box. Each core was divided into 0–5 and 5–10 cm

depths, coinciding roughly with organic and mineral soil

layers. All three cores were then bulked, mixed and sieved of

root matter before analyses. Soil moisture was also monitored

cations

ignin (%) C:N ratio C (%) N (%)

8.06G2.04 87.27G0.61 49.97G0.61 0.573G0.004

5.06G0.51 83.08G0.30 51.43G0.30 0.619G0.027

6.20G1.30 83.87G2.14 48.80G2.14 0.582G0.014

5.92G0.54 74.96G1.62 48.69G1.62 0.650G0.014

9.04G0.33 86.73G0.95 49.30G0.95 0.568G0.006

1.73G1.09 87.17G1.18 50.02G1.18 0.574G0.009

7.53G1.55 83.64G1.27 46.88G1.27 0.561G0.035

5.89G2.30 93.54G0.37 48.52G0.37 0.519G0.005

7.93G1.46 96.14G1.69 49.98G1.69 0.520G0.009

8 8

23.38 6.20

!0.00001 0.0065

(%) C:N ratio pH Microbial car-

bon (ug/g soil)

.116G0.027 23.28G1.72 4.14G0.04 124.8G25.9

.082G0.003 23.34G0.94 4.22G0.03 149.6G42.3

.226G0.117 21.35G2.50 4.18G0.04 172.2G29.0

.088G0.005 22.09G1.39 4.26G0.03 157.1G34.6

.078G0.014 25.63G3.77 4.18G0.03 68.38G23.4

.080G0.007 22.89G1.63 4.18G0.04 183.5G39.4

.064G0.006 24.74G1.95 4.13G0.04 221.9G35.7

.062G0.006 25.36G2.61 4.2G0.03 133.0G31.9

.096G0.024 22.78G1.80 4.24G0.03 94.5G25.2

8

3.31

0.0027

s mean of the untransformed dataGSE. Significant differences are indicated

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327 321

every 3 months, and soil temperature measured hourly by

HOBO data loggers at each of the nine sites.

Bacterial communities beneath litter treatments were

estimated at the end of the second year using Biolog ECO

microplates which estimate bacterial community compo-

sition by measuring microbial catabolism of 32 different

carbon substrates. Different bacterial communities

have different patterns of substrate use as indicated by

a colorimetric reaction in 96-well microtiter plates. We

extracted soil bacterial communities and followed dilution

protocols of Zak et al. (1994) and recorded plate

absorbances every 12 for 72 h at 550 nm. At the end of

year 3, we extracted soil bacterial DNA using Qiagen

DNeasy kits and employed T-RFLP techniques (Liu et al.,

1997) to estimate bacterial diversity. PCR products were

digested with the restriction enzymes CfoI and HaeIII, and

final product lengths and quantities were determined by

electrophoresis with an ABI 310 automated DNA

sequencer.

Soil microarthropods were collected in modified

Tullgren funnels (Mallow and Crossley, 1984) and stored

in 70% ethanol. We sorted microarthropods into three

suborders of Acari (Oribatida, Asitgmata, and Prostigmata),

the order Collembola, and ‘others’. Fumigation extraction

(Ross and Sparling, 1993) was used to estimate microbial

biomass carbon as well as nitrogen (details given in

Madritch and Hunter (2002)). Soil respiration was mon-

itored monthly in each box with a portable infrared gas

analyzer (EGM-2 PP Systems).

Table 2

Repeated measures ANOVA of site and phenotype effects on litter and soil respo

Phenotype Date!phenot

Litter responses

Litter carbon change 0.2871; 24.6, !0.0001 0.0368; 1.39,

Litter nitrogen change 0.0404; 8.57, !0.0001

Litter microbial carbon

Percent litter remaining 0.0201; 5.58, !0.0001 0.0215; 1.58,

Soil responses

Respiration

Soil carbon 0–5 cm

Soil carbon 5–10 cm

Soil nitrogen 0–5 cm

Soil nitrogen 5–10 cm

Soil microbial carbon 0–5 cm

Soil microbial nitrogen

Ammonium availability 0.0653; 1.46,

Nitrate availability

Soil pH 0–5 cm

Soil pH 5–10 cm

Litter chemistry

Lignin 0.0085; 3.5, 0.0013 0.0322; 2.04,

Total phenolics 0.0859; 6.37, !0.0001

Condensed tannins 0.0757; 10.74, !0.0001 0.0848; 2.06,

Litter C:N 0.0326; 5.0, !0.0001

Hydrolysable tannins 0.0434; 6.13, !0.0001 0.0688; 1.71,

Litter carbon and nitrogen changes were calculated as (current concentrationKinit

responses for the entire 36-month time period, and F and P values are reported in it

8, date!site d.f.Z56.

Litter carbon, nitrogen, total phenolic, condensed tannin,

hydrolysable tannin, and lignin contents were estimated

using previously established techniques (Madritch and

Hunter, 2002). Methods to determine soil total carbon and

nitrogen, microbial carbon, microbial nitrogen, pH,

and nitrogen availability (estimated with resin bags) have

all been described in detail previously (Madritch and

Hunter, 2002).

2.1. Statistics

Assumptions of normality were tested for all data using a

Shapiro–Wilk W test and non-normal data were transformed

as necessary. We used simple one-way ANOVAs to

estimate phenotypic and site effects on initial litter and

soil chemistry. We used repeated measures ANOVA

procedures to estimate phenotypic effects over time using

the nine sites as replicates while local environmental effects

were estimated using the nine phenotypes as replicates. By

comparing the variance explained by phenotype and site, we

were able to estimate the relative contributions of litter

phenotype and local environment to variation in decompo-

sition and nutrient fluxes. We also separated data into two

time sets, 0–18 months (largely reported previously) and

18–36 months, and performed repeated measures ANOVA

and stepwise regressions (described below). To test for non-

additive effects of mixing litter phenotypes, we used

ANOVAs to compare the mean of the single phenotypes

(expected response) with the mixed litter treatment

nses sampled seven times over 36 months

ype Site Date!site

0.0408

0.2768; 8.40, !0.0001

0.0093 0.0088; 2.75, 0.011 0.0179; 1.48, 0.0278

0.065; 1.81, !0.0001

0.0827; 2.35, 0.0282 0.0826; 1.59, 0.0101

0.097; 5.01, 0.0001 0.1387; 1.78, 0.0043

0.0597; 3.03, 0.0063 0.1009; 2.03, 0.0003

0.0313; 2.36, 0.0283 0.1402; 2.14, 0.0236

0.1038; 1.54, 0.045

0.0708; 3.74, 0.0012 0.143; 2.86, 0.0006

0.0074 0.0410; 6.3, !0.0001 0.0798; 1.98, !0.0001

0.0409; 2.98, 0.0025

0.0439; 3.01, !0.0001

0.0064; 2.16, 0.0428 0.0257; 2.59, !0.0001

0.0003 0.0511; 3.64, !0.0001

0.0363; 2.16, 0.0064

!0.0001 0.0414; 6.61, !0.0001 0.1375; 3.76, !0.0001

0.0016 0.0164; 2.61, 0.0156 0.0972; 2.72, !0.0001

ial concentration)/(initial concentration). R2 values are given for significant

alics, respectively. Phenotype d.f.Z9, date!phenotype d.f.Z63, site d.f.Z

Fig. 1. Litter carbon and nitrogen content. Changes in litter nitrogen and carbon were calculated as (current concentrationKinitial concentration)/(initial

concentration). The graph shows the response of each single phenotype litter and the mixed litter treatment. Positive values indicate a net gain of carbon or

nitrogen; negative values indicate a net loss. Results are grouped into short-term (0–18 month) and long-term (18–36 month) effects. While there were significant

differences among litter phenotypes (P!0.0001), they did not deviate in any predictable manner from the mixed litter treatment. Bars are G1SE, nZ9.

Fig. 2. Litter decomposition rates. Litter decomposition from 0 to 18 months differed among litter phenotype treatments (PZ0.0351), but did not deviate from

the mixed litter treatment in any predictable way. Decomposition from 18 to 36 months was unaffected by litter phenotype, but did differ among sites

(PZ0.0445). Rates (k) were calculated as yZeKkt, where y is the proportion remaining and t time in days. Bars are G1SE, nZ9.

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327322

Fig. 3. Soil ammonium availability. Ammonium availability in the soil under phenotype litters as measured with ion exchange resin bags. The graph shows the

response of each single phenotype litter and the mixed litter treatment. Ammonium in the soil varied under single phenotype treatments over time (PZ0.0074),

but did not deviate in any predictable manner from the mixed litter treatment. Results are grouped into short-term (18 month) and long-term (36 month) effects.

Bars are G1SE, nZ9.

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327 323

(observed response), similar to analysis suggested

for decomposition experiments by Loreau (1998)

and demonstrated by Wardle et al. (1997). We have

insufficient degrees of freedom to investigate site by

phenotype effects.

When estimating the effects of litter chemistry on carbon

and nitrogen fluxes, we accounted for changes in litter

chemistry over time by employing methods similar to

the analyses of population time series (Royama, 1992;

Table 3

Litter chemistry effects on nutrient dynamics during 18–36 months

Litter chemistry at time t

Lignin content Total phenolics Condensed ta

Litter response at time tC1

D Litter carbon K0.0127*

D Litter nitrogen 0.0141*

D Litter

microbial carbon

D Percent litter

remaining

0.0205*

Soil response at time tC1

D Respiration 0.0652*** 0.014*

D Soil carbon K0.0276* 0.019*

D Soil nitrogen

D Soil microbial

carbon

D Nitrate avail-

ability

K0.0813***

D Ammonium

availability

K0.1162*** K0.0147*

D Soil pH K0.2950***

Litter chemistry at time t was correlated with the change in litter nutrient and soil n

using back-stepped multiple regressions. Most aspects of litter and soil nutrient

partial R2 values are given for litter chemistry indices used in the final regression

*P!0.05, **P!0.01, ***P!0.001.

Berryman, 1999). We calculated the change in the nutrient

under consideration (X) as ln(XtC1/Xt), repeated this for

each of the time steps (0, 3, 6, 12, 18, 24, 30, and 36

months), combined data across all sites, and performed a

stepwise multiple regression between the nutrient flux of

interest and our estimates of litter chemistry (Berryman,

1999; Madritch and Hunter, 2002).

Biolog ECO-plate absorbance data were analyzed

using principal components analysis (PCA, PCord v. 4).

nnin Litter C:N Hydrolysable tan-

nin

Final model R2***

K0.1782*** 0.1908***

0.1820*** 0.1961***

n.s.

0.0549*** K0.0710*** 0.1463***

0.0153* 0.1771*** 0.2716***

0.0659* 0.1125**

n.s.

0.0447** 0.0447**

K0.0421** 0.1234***

0.1308***

K0.0263*** 0.3213***

utrients from time step t to time step tC1 [calculated as ln(conctC1/conct)]

change were significantly affected by some aspect of litter chemistry. The

model. The sign of partial R2 values indicates the direction of relationship.

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327324

Axes with broken-stick eigenvalues less than the actual

eigenvalues were kept for further analysis (Jackson, 1993).

We tested for significant differences among site and litter

phenotype groups using Multi-Response Permutation

Procedure (MRPP, PCord v. 4). Microarthropod data were

non-normal and therefore analyzed using non-metric multi-

dimensional scaling (NMS, PCord v. 4). NMS provides a

more robust analysis of non-normal, heavily skewed data,

whereas PCA is suitable for data sets that approach

normality (McCune and Grace, 2002). MRPP (PCord v. 4)

was also used to test for site and phenotype effects on

microarthropod communities. T-RFLP results were ana-

lyzed using Genescan 3.1.2. software. Electropherograms of

T-RFLP data indicate bacterial diversity by the number

and size of fragment groups after restriction enzyme

digestion, while population sizes of each group are indicated

by relative peak height. We tested for litter phenotype

effects on soil bacterial diversity by overlaying electro-

pherograms from different litter phenotype treatments

and searching for differences in peak location and peak

height.

Fig. 4. Representative T-RFLP electropherograms. Results of bacterial DNA amp

and 9, and from under the mixed phenotype treatment. Peak position along the

whereas peak height indicates relative abundance as determined by fragment co

indicating no difference in bacterial diversity and only slight differences in relati

3. Results

The initial litter from each of the nine phenotypes

significantly differed in their condensed tannin and nitrogen

content, as well as their C:N ratio while the initial soil

chemistry underlying each of the nine sites differed only in

carbon contents (Table 1).

Changes in the concentration of litter nitrogen and

carbon over time were dominated by phenotype effects

(P!0.0001) and unrelated to the site of decomposition

(Table 2, Fig. 1). Litter mass loss over the entire 3-year

period was affected by both litter phenotype treatment and

site location (Table 2). After 18 months of decomposition,

k values varied among litter phenotype treatments (d.f.Z9,

FZ2.16, PZ0.0351), but not among sites (PO0.05)

(Fig. 2). From 18 to 36 months, however, the decomposition

rate was influenced only by site (d.f.Z8, FZ2.12,

PZ0.0445) and not by litter phenotype (PO0.05) (Fig. 2).

Soil ammonium availability varied among phenotype

treatments over time (Table 2). Ammonium was consist-

ently more available during the initial stages of leaf litter

lification and HaeIII digest of DNA from soil under phenotype treatment 1

x-axis indicates bacteria group identity as determined by fragment length,

unts. In all electropherograms, there were no differences in peak position

ve abundances.

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327 325

decomposition from 0 to 18 months (Fig. 3). Other soil

responses including respiration, percent carbon and nitro-

gen, microbial biomass, microbial nitrogen, nitrate avail-

ability, and pH were all dominated by site effects (Table 2).

Neither soil temperature nor soil moisture was varied by

litter treatment or site (PO0.05, data not shown).

Nutrient fluxes in the litter and soil of single phenotype

treatments sometimes differed from the mixed litter

treatment (Figs. 1–3). However, there were no significant

differences between the mean of single phenotype responses

and the mixed litter response for metrics reported in Table 2

(PO0.05 in all cases). Thus, we found no long-term non-

additive effects of phenotype diversity on litter or soil

nutrient dynamics.

All litter nutrient fluxes and most of the nutrient fluxes in

the soil (with the exception of soil nitrogen change) during

18–36 months were influenced by litter chemistries

as indicated by stepwise regression results (Table 3).

The 0–18 month results have been reported previously

(Madritch and Hunter, 2002). In general, hydrolysable

tannin concentrations and C:N ratios exhibited the strongest

influence over litter carbon and nitrogen changes, while

litter lignin content was strongly correlated with long-term

soil pH change (Table 3).

Soil bacterial and microarthropod communities were

unaffected by litter phenotype treatment, but significantly

influenced by site. PCA analysis of Biolog ECO-plate

absorbance data yielded two axes which described signifi-

cant differences among sites but no effects of litter

phenotype treatments (MRPP PZ0.0481 and PO0.05,

respectively). Likewise, T-RFLP electropherograms of soil

bacterial profiles did not differ significantly among litter

phenotype treatments. Fig. 4 compares the electrophero-

grams of three T-RFLP analyses for soils beneath litter

phenotypes one, nine, and the mixed litter treatment. The

lack of significant differences in relative peak size and

location indicates very similar soil bacterial communities.

Although we only show three electropherograms, they are

representative of all our T-RFLP runs and indicate that

bacterial communities were similar across all litter treat-

ments. Similar to PCA analysis of bacterial communities,

NMS analysis of microarthropod data averaged over time

showed significant differences among sites, but no effect

of litter phenotype treatments (MRPP PZ0.0001 and

P!0.05).

4. Discussion

Previous work showed that litter phenotype treatments

can influence nutrient dynamics under relatively short time

periods (Madritch and Hunter, 2002). Our current data show

(1) that the intraspecific composition of leaf litter can

influence short- and long-term carbon and nitrogen fluxes

during decomposition, and (2) that differences in nutrient

fluxes are not associated with variation in soil microar-

thropod or bacterial communities.

The transfer of carbon and nitrogen to and from litter, litter

decomposition rate, and the availability of ammonium in the

soil beneath litter treatments were all significantly affected by

litter phenotype treatment (Figs. 1–3). Variation in litter

chemistry among phenotype treatments seemed to play

a major role in ecosystem responses, as stepwise regression

results showed that litter chemistry was important to virtually

all measured aspects of nutrient cycling (Table 3). Tannin and

lignin contents are particularly important and have been

shown by others to correlate with intraspecific variation in

litter decomposition (Driebe and Whitham, 2000; Sariyildiz

and Anderson, 2003).

Although only some of the litter chemistries varied by

phenotype in the litter initially, repeated measures ANOVA

showed significant phenotype effects on all litter chemistries

over the entire 3-year period. Previous work estimated that

34–40% of the variation in chemical phenotype is due to

genetic variation among individual trees (Madritch and

Hunter, 2002; Klaper and Hunter, 1998). Thus, phenotypic

litter composition is most likely affecting nutrient dynamics,

in part, due to genetically mediated variation in litter

chemistries. It is important to recognize that the approxi-

mate 60–65% of the variation in chemical phenotype not

genetically mediated is also important to nutrient dynamics.

Several researchers have found evidence that microbial

communities can adapt to the quality of leaf litter input

(McClaugherty et al., 1985; Hunt et al., 1988; Clein and

Schimel, 1995). However, we found no effects of litter

phenotype on bacterial community diversity (as measured

by Biolog and T-RFLP assays). Instead, only strong site

effects were detected by the Biolog assay. Both of the

microbial analyses we used were limited to bacterial

diversity, and it is known that fungal communities play

major roles during litter decomposition in forest ecosys-

tems. For instance, Neely et al. (1991) found that the

fungal:bacterial biomass ratio in Quercus prinus litter

increased to 4.3 after 100 days. Unfortunately our attempts

to use T-RFLP assays with fungal DNA were unsuccessful.

Though microarthropod communities are also known to

shift with changes in leaf litter species diversity (Hansen,

2000), in our study, microarthropod community structure

was influenced only by site and not by litter phenotype. In

general, the soil fauna that we surveyed was not affected by

litter phenotype, nor was variation in the community

composition of either bacterial or microarthropod commu-

nities responsible for differences in nutrient cycling.

Given that nutrient cycling differed among litter

phenotype treatments whereas the biotic communities did

not, it is likely that the biotic community was sufficiently

flexible to process variable litter inputs. While essential for

decomposition and nutrient cycling during leaf litter

decomposition, soil microbial communities may be fairly

redundant and even simple communities appear to be able to

process most litter (Andren et al., 1995; Wardle et al., 1997).

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327326

However, our community analyses were fairly coarse,

well above the species resolution level, and it is possible

that our inability to detect community changes at a finer

taxonomic resolution precluded detection of litter diversity

effects on soil biota. It is also important to note that our

failure to characterize fungal communities severely restricts

our ability to determine the full microbial response to

differences in intraspecific litter composition.

A primary goal of this study was to compare short- and

long-term effects of litter phenotype on nutrient cycling

during decomposition. The importance of previous short-

term effects could be diminished if no long-term effects

persisted. In general, the influence of litter phenotype on

decomposition was similar after 18 and 36 months. In both

cases, individual litter treatments varied idiosyncratically

from each other and from the mixed litter treatment. Over

both time scales, genetically mediated litter chemistries

were correlated with nutrient cycling in the leaf litter and

underlying soil. While litter chemistries in general were

useful predictors of nutrient fluxes, phenotypic composition

alone was not. Despite these short- and long-term

similarities, several differences are worth noting.

We previously found short-term, non-additive effects of

litter phenotype on soil carbon and nitrogen content,

microbial biomass, and pH (Madritch and Hunter, 2002).

However, none of these non-additive effects persisted over

the long-term. Instead, long-term nutrient fluxes were only

influenced by phenotypic identity. In addition, the influence

of site and date!site effects on nutrient fluxes increased in

the long-term (Table 2). The importance of site effects

increased over the long-term, and in some cases became

more important to nutrient fluxes than were the litter

phenotype treatments.

Several individual phenotype litter treatments elicited

different ecosystem responses compared to the mixed litter

treatment. These differences were sometimes large, but

always unpredictable. While litter chemistries were corre-

lated with nutrient dynamics, there was no overall pattern of

single phenotype treatment nutrient dynamics compared to

the mixed litter treatment. Idiosyncratic responses are

common in biodiversity and ecosystem function studies

pertaining to litter decomposition, and may be due to

overwhelming effects of species identity on litter decompo-

sition (Wardle et al., 2003).

While we can be certain that the trees from which we

collected litter were of different genotypes (Berg and

Hamrick, 1994; Klaper et al., 2001), we were unable to

find replicate clones of these genotypes within the stand.

Consequently, interpretation of our results is limited to a

discussion of phenotypic effects. Nonetheless, previous

work on the same stand of Q. laevis used here (Klaper and

Hunter, 1998; Klaper et al., 2001; Madritch and Hunter,

2002) and work on other species (Driebe and Whitham,

2000; Treseder and Vitousek, 2001) has shown a genetic

component to phenotypic variation in litter chemistries

important to decomposition. However, if different tree

genotypes establish preferentially under specific soil

nutrient conditions, soil chemistry could be the sole cause

of variation in litter chemistries. We have previously

addressed this issue (Madritch and Hunter, 2002) and

concluded that a lack of soil nutrient patterns (Klaper et al.,

2001), the small-scale random dispersal of adults (Berg and

Hamrick, 1994), and the random selection of individuals for

inclusion into our experiment effectively eliminate the

possibility that all litter chemistry differences were caused

only by growing environment. The differences in litter

chemistries important to nutrient cycling were influenced by

both genotype and environment. Therefore, our results are

relevant to the current anthropogenic decline of genetic

diversity within forest ecosystems (Ledig, 1992); a decline

in genetic diversity, or a change in genetic composition, can

influence carbon and nitrogen cycling during

decomposition.

Intraspecific litter composition can influence carbon and

nitrogen fluxes, and the effects shown here are the result of

litter identity. In addition, differences in ecosystem

functioning were attributable only to variation in litter

chemistry and not any detected changes in bacterial or

microarthropod communities. However, we re-emphasize

that fungal communities need to be considered in future

work. Leaf chemistries have long been recognized as

important to plant–herbivore interactions, but increasing

evidence suggests they play an equally important role in

nutrient cycling (Hattenschwiler and Vitousek, 2000).

Our work suggests that genetically mediated variation in

secondary metabolites can have important ecosystem

consequences; in the turkey oak sandhills system, sufficient

variation exists within a single species such that changes in

intraspecific composition elicits ecosystem responses.

Intraspecific variation may be more important when the

variation in chemical phenotype is larger (such as the

145-fold difference in condensed tannin concentrations in

Leucaena trichandra, Dalzell and Shelton, 2002). Con-

versely, speciose communities may be influenced more by

interspecific variation. However, without knowing exactly

when and where intraspecific composition is important to

ecosystem functioning, it would seem prudent to include the

maintenance of intraspecific diversity within conservation

plans.

Acknowledgments

This research was supported by the National Science

Foundation and the Andrew W. Mellon Foundation.

We especially thank J. Sullivan for help with T-RFLP

analyses. We also thank the Savannah River Ecology

Laboratory for the use of their facilities and M. Cabrera,

D. Coleman, J. Hamrick, P. Hendrix, R. Pulliam, R. Sharitz,

L. England, R. Klaper, S. Connelly, S. Eustis, B. Nuse,

J. Rogers, and S. Scott for comments and/or laboratory

assistance.

M.D. Madritch, M.D. Hunter / Soil Biology & Biochemistry 37 (2005) 319–327 327

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