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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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
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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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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
