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Department of Biological Sciences, P.O. Box 43131, Texas Tech University, Lubbock, Texas 79409
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSANALYTICAL APPROACHESRESULTSDISCUSSIONLITERATURE CITED |
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Conservation methods often are focused on preserving the biodiversity of a particular landscape or ecosystem. Scientists frequently employ species richness as an indicator of biodiversity. However, species richness data are problematic when attempts are made to enumerate microfungi, particularly those from the soil. Many soil fungi fail to sporulate, making identification difficult. Other means of assessing the importance of fungi to ecosystem preservation must be developed. Otherwise, microfungi might be overlooked in discussions of ecosystem management and conservation issues. Herein, we have described a procedure (Soil FungiLog) and analytical techniques that will let investigators examine the functional role that soil fungi play in providing structure and stability to ecosystems. Ecosystem function in many cases might be more important than species diversity in gaining an understanding of ecosystem dynamics. Functional attributes are critical for maintaining ecosystem structure and stability. The preservation of the functions associated with the extant biota, particularly from soil microbes, might be just as important as species diversity in the conservation of ecosystems and biodiversity. The Soil FungiLog procedure was used to assess functional diversity of soil fungi in a Georgia forest disturbed by human activity and along an elevational gradient in the Chihuahuan Desert. Sites within each location were separated on the basis of fungal carbon substrate utilization profiles. These profiles were analyzed to provide information regarding the functional diversity of soil fungal assemblages at each site. The effects of disturbance and elevation were evaluated with respect to soil fungal functional diversity.
Key words: Biolog, discriminant function analysis, functional diversity, FungiLog, soil fungi
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSANALYTICAL APPROACHESRESULTSDISCUSSIONLITERATURE CITED |
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, Vitousek and Reiners 1975, Vitousek and Hooper 1993, Zak and Visser 1996, Schlesinger 1997, Zhang and Zak 1998). Since saprobic soil fungi are involved directly in critical ecosystem processes (Cromack and Caldwell 1992), it is evident that fungi will be one of the first components of an ecosystem biota to be affected by environmental disturbance (Zak 1992, Lodge 1997, Miller and Lodge 1997, Persiani et al 1998). Thus, soil fungi should provide an excellent indicator group for determining changes in ecosystem function arising from natural successional events, climate change, pollution, or following reclamation.
Dobranic and Zak (1999) proposed a theoretical framework and methodology (FungiLog) to relate attributes of ecosystem functioning, which include decomposition and nutrient cycling, with the in vitro catabolic profile of litter fungi. Fungal functional diversity, as defined by FungiLog methodology, is based on the number of compounds used from a suite of 95 carbon compounds by a given fungal assemblage and the rates at which these compounds are catabolized over a specified period at a preset incubation temperature.
Herein, we describe a modification of the FungiLog method, as described for plant litter by Dobranic and Zak (1999), that allows for the evaluation of fungal functional diversity associated with soil organic matter. We have named this method the Soil FungiLog procedure. To evaluate the efficacy of the Soil FungiLog procedure in assessing differences in functional diversity of soil fungi between closely related habitats and across substantially different ecosystems, we have employed it to evaluate soil fungi functional diversity within two distinct systems: 1) from five vegetation zones along an elevational gradient in the Chihuahuan Desert in Texas; and 2) from six forested sites in Georgia that have experienced various degrees of disturbance by humans.
To assist in the analysis of Soil FungiLog data, we will discuss several methods for describing and statistically analyzing FungiLog data. Previous investigations of bacterial functional diversity, using the Biolog approach, have relied heavily on multivariate analyses, primarily Principle Component Analysis (PCA) (Zak et al 1994, Haack et al 1995, Garland et al 1997, Engelen et al 1998, Fantroussi et al 1999, Ibekwe et al 2001). However, we ascertained that many PCA analyses are flawed due to the inherent structure of Biolog data, namely numerous variables (95 carbon substrates) and few observations or samples. The methods described herein circumvent this problem by using stepwise discriminant function analysis that adjusts the variable to observation ratio in such a way that tenable patterns arise which are amenable to significance testing. We provide the appropriate references and details so that investigators directly can employ both the Soil FungiLog method and statistical analyses necessary to garner a comprehensive understanding of soil fungal functional diversity at various sites or across selected environmental gradients.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSANALYTICAL APPROACHESRESULTSDISCUSSIONLITERATURE CITED |
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Retrieval of soil organic matter – To ensure that functional diversity estimates were based on fungi growing on soil organic matter, spores and conidia were removed from soil organic matter (SOM) particles by shaking 25–50 g (larger samples can be used for soils with low organic matter content) of soil in 100 mL of reverse osmosis water containing 10 µL of Tween 80 for 1 h. Tween 80 was added to help disperse material. The soil suspension was washed with tap water through 500 and 250 µm sieves for at least 1 min (longer washing is needed for soils with high clay content). SOM particles from the 250 µm sieve were collected in a 500 mL beaker, the beaker was filled with tap water, stirred and allowed to stand 10–20 s to precipitate the mineral fraction. Afterward, SOM particles were decanted into a filtration system under vacuum and collected onto P8 filter paper (Fisher Scientific). A 2–3 mm thick layer of SOM particles on the paper provided ample material for microtiter plate inoculation. However, if the number of SOM particles in a sample was low, multiple subsamples were extracted from the original sample to provide sufficient particles for inoculation. SOM particles were used immediately in experiments or sealed in a petri plate containing a damp piece of filter paper and stored at 4 C. All stored material was used within 24 h of extraction.
Microtiter plate inoculation – The inoculation mixture consisted of water agar, antibiotics, dimethylthiazolyl-diphenyltetrazolium bromide (MTT) and SOM particles. The mixture was prepared in 25 mL glass vials, where each vial contained 50 mg of SOM particles, 2.0 mg of MTT, 2.0 mg streptomycin sulfate, 1.0 mg of chlortetracycline hydrochloride, and 20 mL of 0.2% water agar. To expedite the soil FungiLog procedure, MTT and antibiotic stock solutions were prepared in advance. The MTT stock solution was prepared by suspending 150 mg of MTT in 10 mL of warm, sterile, distilled water. The solution was placed in a sonicating bath set at 55 C for 5–15 min and then vortexed to facilitate dissolution of the MTT. If a sonicating bath is unavailable, the solution should be heated to 58 C and vortexed repeatedly to dissolve the MTT. A small amount of MTT might not dissolve, but the undissolved amount had no noticeable effect on FungiLog results. The antibiotic stock solution was made by dissolving 400 mg of streptomycin sulfate and 200 mg of chlortetracycline hydrochloride in 40 mL of sterile distilled water. Exactly 134 µL of the MTT stock and 200 µL of the antibiotic stock were added to each vial and vortexed 20 s to ensure proper mixing of the solution. We suggest that MTT stock be added immediately to the vials, while the MTT stock solution is warm. MTT will precipitate out of solution during storage and is not readily dissolved thereafter. However, antibiotic stock can be kept frozen until needed. The vials with water agar, MTT and antibiotics can be prepared several days ahead and stored at room temperature in the dark. MTT and antibiotics will remain dissolved in the water-agar solution. Immediately before microtiter plate inoculations, SOM particles were added to the vials containing the water-agar solution and vortexed 10 s to distribute the particles. The viscosity of the 0.2% water agar is sufficient to keep the SOM particles in suspension. To inoculate the microtiter plates, the 20 mL water-agar solution containing SOM particles, antibiotics and MTT was poured into sterile troughs (Biolog, Hayward, California), from which 100 µL of the mixture was transferred to each well in the Biolog SNF-2 microtiter plates (biolog, Hayward, California) using an eight-channel micropipette pump fitted with 200 µL capacity large orifice pipette tips (Fischer Scientific). The use of large orifice tips is important because SOM particles tend to become lodged in small orifice tips. To avoid the formation of a single large bubble in the wells, the micropipette pump should be depressed initially to the second stop to acquire the solution. Subsequent inoculations into the same microtiter plate require that the pipette be depressed to the first stop to acquire the solution and that the pipette tip be placed at the bottom of the well before delivery. Pull up on the micropipette as material is dispensed into the wells to prevent contamination.
The SF-N2 microtiter plates were incubated at 25 C and optical density of each plate taken every 24 h for 5 d with a Biotek Plate Reader set at a 590 nm wavelength. In this analysis, functional diversity was calculated at 120 h, which had been shown to be the period of maximal microtiter plate color development when plates were incubated at 25 C (Dobranic and Zak 1999).
Particle size effects – The effect of SOM particle size on fungal functional diversity was evaluated by screening four size classes of SOM particles in Biolog SF-N2 microtiter plates. These particle size classes were used in this experiment: 1) 1000–500 µm, 2) 500–250 µm, 3) 250–125 µm and 4) 125–53 µm. SOM particles were extracted from 10 combined soil samples collected from the closed-canopy oak forest in the Pine Canyon Watershed at Big Bend National Park. Sieves that corresponded to the four SOM particle size classes were used to obtain soil organic matter, according to the previously describe retrieval procedure. Afterward, five replicate microtiter plates were inoculated with 50 mg of SOM particles from each particle size class as previously described. Microtiter plates were incubated at 25 C and read every 24 h for 5 d as described above.
Particle density effects – The effect of particle density per well on color development and subsequent estimation of fungal functional diversity was evaluated by testing five levels of SOM particles (30, 75, 150, 225 and 300 particles per well) from the 500 µm and 250 µm size class in Biolog SF-N2 microtiter plates. The results of the particle-size experiment (see results section) indicated that SOM particles between 500 µm and 250 µm were the most appropriate size from which to discriminate patterns arising from fungal functional diversity. Particle densities inoculated into the SF-N2 microtiter plates were prepared by diluting a 300 mg suspension of SOM particles to the five aforementioned ranges of densities in each well. Each treatment was repeated five times.
In addition, the effect of particle density on color development in the absence of Biolog carbon substrates was examined. Preliminary experiments had indicated that high particle densities (>200 particles per well) distorted optical density readings. Six particle density classes were used: 28, 69, 113, 143, 161 and 197 particles per well. For this experiment, Biolog MT plates (Biolog, Hayward, California) were used. Biolog MT plates lacked carbon substrates, but the wells contained micronutrients. Each microtiter plate well served as a replicate, and the above six particle densities were assigned as treatments. Consequently, each density treatment was repeated 96 times with one Biolog MT plate inoculated per particle density treatment.
Method evaluation – Two distinct landscapes were used to evaluate the Soil FungiLog method: a) five vegetation zones along an elevational gradient at Big Bend National Park in the Chihuahuan Desert; and b) forested sites in Georgia that have experienced various levels of disturbance by humans. Soil samples were processed following the previously described retrieval and inoculation procedures.
Chihuahuan Desert.
The Pine Canyon Watershed extends from Lost Mine Peak in the Chisos Mountains east to Glenn Springs. The watershed from top to bottom is approximately 19 km long. The watershed comprises five distinct vegetation zones, which correspond to elevation (Dobranic and Zak 1999) and include: 1) Lowland Desert Scrub (LDS; 793 m) consisted of mixed subshrubs dominated by Leucophyllum leiophyllum and Agave lechuguilla and numerous grasses and annuals; 2) Creosote Bush Bajada (CR; 1010 m) that was dominated by Larrea tridentata, Agave lechuguilla and several species of Opuntia; 3) Sotol-Grassland (SG; elevation 1526 m), where dominant plant species included Dasylirion leiophyllum, Nolina texana and Bouteloua ramose; 4) A Closed Canopy Oak Forest (CCO; 1824 m) dominated by Quercus gravesii, Pinus arizonica, Arbutus xalapensis and Juniperus deppeana and a sparse understory of shrubs; and 5) An Oak-Pine Forest (OPF; 2098 m) dominated by Quercus emoryi and Pinus cembroides.
In each vegetation zone, two 100 x 30 m belt transects were established. Five soil samples were obtained from each transect in August 2000, with 10 samples used to characterize a vegetation zone in the watershed. Samples were taken from a depth of 10 cm. Samples were stored at 4 C until use.
Georgia. Fort Benning is located in the Upper Coastal Plain physiographic province. The region around Fort Benning is characterized by sand hills bisected by a series of 3rd and 4th order watersheds. Upland sites consist of pine-hardwood mosaics, while extensive riparian areas occur along streams. Soil samples were collected in May 2000 from six upland locations within the base itself. The sites were designated either as experiencing low, medium or high disturbance, based on landscape and military records. Forested sites that experienced only limited troop movement in the past 10 years were considered low-impact sites. Sites that experienced moderate troop activity and periodic burning (to reduce understory vegetation) were designated moderate-impact areas. High-impact areas were affected by extensive tank and troop activity, which has eradicated vegetation. In high-impact areas, samples were taken from sites in which revegetation had begun. From each upland location, six composite samples were collected, with each composite sample consisting of six subsamples taken from a depth of 15 cm. The sampling scheme was designed so that one composite sample was collected above and one collected below each of three lysimeters that were established at each site. The final analysis from the Fort Benning area examined 36 composite samples.
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ANALYTICAL APPROACHES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSANALYTICAL APPROACHESRESULTSDISCUSSIONLITERATURE CITED |
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. Substrate activity was defined as the amount of carbon in each microtiter plate well used by at least one member of the soil fungal assemblage inoculated into each plate. Each well can generate an optical density value range of 0–3, in which values of 0 indicated no utilization and 3 indicated maximum use of a specific substrate as measured by the microtiter plate reader. Values were added up across each microtiter plate to provide an index of substrate activity for each sample. The maximum substrate activity measured by the microtiter plate reader for an inoculated Biolog plate is 285 when scanned at 590 nm. Substrate richness is defined as the number of substrates used from a microtiter plate that exhibit an optical density >0.1. The maximum possible substrate richness per microtiter plate is 95.
Stepwise discriminant-function analysis –
To discriminate among sites and determine which substrates gave rise to site discrimination patterns at Big Bend and Fort Benning, Soil FungiLog data were analyzed, using stepwise discriminant-function analysis (Tabachnick and Fidell 1996). Stepwise discriminant-function analysis (Stepwise DFA) has several unique features: (a) condensation of variables, in which only a small subset of the 95 carbon substrates are used in the discriminant analysis; (b) the selected substrates provide the best separation among sites in question; and (c) clarity of interpretation. DFA gives rise to patterns that are easily visualized and delimited in discriminant function graphs. In addition, the stepwise discriminant method prevents the occurrence of singularities and multicolinearities that are present when all 95 carbon substrates are used in the analysis (Tabachnick and Fidell 1996, Stevens 2002). For example, if the number of variables (carbon substrates) is greater than the number of observations (soil samples), a singular solution is provided by the analysis, which when graphed displays perfect separation among groups. However, that solution is only an artifact of the analysis process and the scientific literature unfortunately contains many such faulty analyses. Furthermore, when groups or sites have been identified a priori, DFA is superior to another commonly used method to describe Biolog data, namely Principal Components Analysis (Tabachnick and Fidell 1996).
There are several selection criteria available when performing a stepwise DFA. The criteria are used to determine which variables (in our case carbon substrates), from all possible variables, are to be used in the DFA. We chose RAO V, which is a generalized distance measure that incorporates variables eliciting the greatest change in V, or the distance between groups (Tabachnick and Fidell 1996). Thus, variables are selected based on their contribution to the overall separation of groups.
Data analysis – The fungal functional diversity data from Big Bend National Park and Fort Benning were analyzed with SPSS 9.0 (www.spss.com) and the computer language program Matlab 6.0 (www.mathworks.com). All Matlab programs used for the analyses presented here are available at the Texas Tech Biological Sciences Website (www.biol.ttu.edu/Faculty/FacPages/Strauss/Matlab/matlab.htm). These Matlab functions were used to conduct these analyses: (a) DISCRIM, (b) ANOVA, (c) STEPDISC and (d) CLASSIFY. All analyses were executed in both Matlab and SPSS. The data presented in this paper were obtained using Matlab, with the exception of ANOVA and the DFA inclusion variables, which were conducted in SPSS. The Matlab function STEPDISC and SPSS Discriminant function chooses variables based on the RAO V criterion. However, the SPSS program defines a particular cutoff point for the number of variables to be included in the analysis. STEPDISC does not and leaves the decision up to the investigator. With no a priori knowledge of the number of variables to include in the DFA analysis, we decided to follow SPSS criteria.
Statistical analyses –
One-way ANOVA (Sokal and Rohlf 1995) was used to evaluate the effects of particle size and density on substrate activity and substrate richness. When significance was detected, post hoc analysis using the Student-Newman-Keuls test was conducted to determine which treatments were significantly different (Sokal and Rohlf 1995).
The degree to which variation in fungal functional diversity among sites at Big Bend or Fort Benning can be expressed as site-specific differences can be visualized by plots generated from discriminant-function analysis (Tabachnick and Fidell 1996). If the Biolog SF-2 carbon suite is effective in discriminating differences in soil fungal functional diversity among sites, samples within sites should form distinct clusters in multidimensional space. DFA was conducted in a stepwise fashion (Tabachnick and Fidell 1996) to reduce the number of variables so that it incorporates only the most pertinent variables for discriminating among sites from each field location. RAO V was the stepwise criterion employed for datasets from Big Bend National Park and Fort Benning. The SPSS default F-values were used in the analyses (SPSS version 9.0). For each DFA, 5000 bootstrap iterations were performed to build a sampling distribution against which the observed data was compared. In addition, not only were P-values provided by this technique but confidence intervals were generated for the point estimates based on the sampling distribution. When employing any stepwise method, it often is desirable to determine the efficiency of the analysis via a cross-validation procedure. The cross-validation analysis determines the percentage of observations correctly classified. In essence, a cross-validation procedure determines the level of resolution the chosen stepping procedure has in classifying observation to groups. We used the Matlab's CLASSIFY as the cross-validation procedure, in which each observation was removed from the dataset and a DFA was conducted. Afterward, the removed observation was reclassified to a group based on the discriminant structure. This approach provides an unbiased estimate for determining to which group or treatment a particular observation belongs. When the classification procedure is bootstrapped using the CLASSIFY function in Matlab, an index of percent correct classification is produced for each site.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSANALYTICAL APPROACHESRESULTSDISCUSSIONLITERATURE CITED |
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= 0.05).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSANALYTICAL APPROACHESRESULTSDISCUSSIONLITERATURE CITED |
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, Zak et al 1994, Willig et al 1996, Broughton and Gross 2000) and agroecosystems (Di Giovanni et al 1999, Schutter et al 2001). In addition, Biolog Analysis has been used to assess bacterial dynamics in contaminated soils (Fantroussi et al 1999, Ibekwe et al 2001), waste treatment processes (Guckert et al 1996, Victorio et al 1996) and water quality (Lehman et al 2001).
Species diversity often correlates positively with species function for many different taxa, although most of the research in this area has dealt with plant communities (Naeem and Li 1997, Tilman 1999, 2001). However, our objective is not to quantify the relationship between fungal richness and fungal function. Rather, we take the alternative stance that, regardless of richness, function is more appropriate for studies concerning ecosystems processes. The Soil FungiLog procedure linked with litter fungal functional diversity should provide significant insight into ecosystem processes and fungal assemblages.
Particle size –
Fungal functional diversity from the FungiLog procedure is determined by a color change that results from metabolic activity within each well of the Biolog microtiter plate (Dobranic and Zak 1999). Hence, selection of the appropriate particle size was critical because SOM particles that were too large or too small distorted optical density readings. Our goal when developing the Soil FungiLog procedure was to obtain a SOM particle size that ensured minimum optical density interference, one fungal isolate per particle and maximization of fungal growth. A 1.0 mm or less particle size is recommended to maximize fungal species richness when isolating from litter and soil organic matter (Bääth 1987, Bills and Polishook 1994, Zak and Rabatin 1997). The final range selected for the Soil FungiLog procedure (500 µm to 250 µm) satisfied all of our prescribed conditions. Nearly 99% of the SOM particles that were plated from Big Bend yielded a single isolate, and, depending on site, 70%–99% of these isolates did not sporulate in culture (unpubl data). Results of this component of the investigation will be published later. In terms of fungi present in the microtiter plates, it is unlikely that there are significant numbers of unculturable saprobic fungi from soil (Cannon 1996) that would not grow in the microtiter plate wells. Bill and Polishook (1994) reported that the majority of soil isolates are likely hyphomyetes or coelomycetes. Our species data (unpubl) from Big Bend suggest that zygomycetes make up 5–10% of fungal isolates that contributed to fungal functional diversity, where the majority of isolates were ascomycetes (hyphomycetes and coelomycetes) or sterile forms. Although basidomycetes might have been present in our SOM samples, we did not observe a single sterile isolate with clamp connections.
Questions regarding how fragmentation affects fungal mycelium might arise in discussions concerning fungal isolations from soil. Soil washing and sieving are standard protocols used to isolate soil fungi (Bääth 1987, Bills and Polishook 1994, Thorn et al 1996, Zak and Rabatin 1997). We argue that by sieving rather than grinding soil particles to a standard size hyphal fragmentation is minimized. Bääth (1988) isolated soil fungi from a range of SOM particle sizes similar to sizes used herein. He was unable to detect differences in species diversity or composition among size particles. Moreover, his data suggested "small particle sizes give a more realistic picture of the fungal community." Thorn et al (1996) isolated basidiomycetes from SOM particles that were washed and put through 250 µm and 53 µm sieves. Sieving seemed to have little effect on isolations in that they were able to isolate 67 basidiomycetes from 40 of 64 soil samples. Their data suggested that the particle sizes we used for the Soil FungiLog procedure should not preclude the inclusion of basidiomycetes.
Functional diversity –
Tilman (2001) defined functional diversity as "the range and value of those species and organismal traits that influence ecosystem functioning," where ecosystem functioning is observed from the standpoint of several parameters, including primary productivity, total biomass or resource use. Hence, ecosystems having higher values in these parameters also are likely to possess greater functionality.
We provide our own definition for functional diversity that encompasses Tilman's basic idea but which we believe reflects fungal assemblage dynamics in ecosystems: Functional diversity is the cumulative effect of species interactions expressed at the ecosystem level, where species interactions include all aspects of an organism's manner of living (e.g., competition, predation, symbioses, etc.). Since ecosystem processes comprise species interactions, the status of an ecosystem's health (stability, resilience, nutrient cycling, clean air and water), in theory, can be captured in measurements of functional diversity.
In biodiversity theory, two models (sampling effect and niche differentiation) predict that ecosystem function is a product of species richness and species composition in the ecosystem being considered. Most ecological studies examining functional diversity accordingly use species diversity as a metric for evaluating function. It is important to note that the soil FungiLog approach directly measures soil saprobic fungal assemblage functionality and does not rely on a surrogate metric to estimate fungal functional diversity. The FungiLog procedure uses carbon source utilization profiles as a means of extracting fungal functionality data from a Biolog array of 95 carbon substrates. The degree to which a fungal assemblage uses or decomposes the suite of carbon substrates represents an index of fungal functional diversity. This is analogous to the sampling-effect model that predicts that ecosystems containing greater numbers of species traits should on average be more functionally diverse. In this view, functional diversity is related directly to a range of traits that on average would enhance the ability of species to use a greater range of resources. Likewise, fungal assemblages using a greater range of FungiLog carbon substrates should on average possess a greater range of traits that control decomposition ability. Any explicit increase in the ability of a fungal assemblage to use a wider variety of carbon substrates, and thus contribute to increased carbon dynamics (decomposition), may be interpreted as enhanced functional diversity.
The use of carbon substrates to determine fungal functional diversity is not a new concept (Miller 1995). Kjøller and Struwe (1987) provided evidence that substrate-use patterns were useful in describing decomposition patterns of litter under field conditions. Flanagan (1981) examined physiological groups of fungi, based on carbon substrate use, from soil and litter in the Arctic and detected functional differences based on temperature, where Arctic fungi used more carbon substrates than temperate fungi when incubated at 5 C. While these studies contribute significantly to the understanding of fungal functional diversity, they have limitations that the Soil FungiLog avoids, namely, few carbon substrates were used as functional indicators and individual isolates were screened, rather than whole fungal assemblages.
Functional diversity analysis can be used to address questions as varied as ecosystem stability, productivity, decomposition, resilience and perturbation responses. Fungal functional diversity provides such a metric and is related directly to fungal metabolism. Fungal assemblages that use a wide range of carbon substrates are likely to reside in stable and unperturbed systems. For example, our data indicated that disturbed sites or environmentally stressed sites are colonized by fungal assemblages that are less functionally diverse than assemblages colonizing undisturbed sites or mesic forests. Furthermore, fungal functional diversity (as measured by the Soil FungiLog method) is a standardized measure, allowing for functional comparisons across a variety of ecosystems or habitats. Hence, surveys of fungal functional diversity should provide researches with the means to assess ecosystem health.
Functional determinants –
Soil fungi rely on the extant vegetation in their immediate environment to provide carbon sources for growth and reproduction (Cromack and Caldwell 1992, Kjøller and Struwe 1992). Hence, it is reasonable to infer that fungal functional diversity is linked directly to the physiognomy of the vegetation (Wardle 2002, Zimmer 2002). We propose three related characteristics of plant-produced carbon that could account for the potential linkage between soil fungi and vegetation: 1) complexity of carbon sources produced by the vegetation (Wardle and Lavelle 1997), 2) quantity of organic matter input by the vegetation assemblage (Catovsky et al 2002), and 3) spatial and temporal heterogeneity of carbon substrates in a system (Zak et al 1993). A vegetation assemblage that is more complex in the types of carbon compounds it produces should provide a strong diversifying effect on soil fungi, selecting for groups of decomposers that can use a wide variety of carbon substrates. Similarly, substrate quantity also might influence soil fungal functional diversity in two ways. For example, if a compound is rarely produced by plants within a vegetation assemblage, the probability that soil fungi will use it as a food source is low. However, if a compound is abundantly produced, that compound has a high probability of being used. Furthermore, substrate quantity must be reconciled with substrate heterogeneity. The distribution of substrates may be clumped in space and time due to the structure and composition of the vegetation assemblages. While a substrate may be produced in large quantities, clumping will dampen the homogenizing effect of quantity and may select for low fungal functional diversity.
Plant community species richness might not be a good predictor of vegetation-related carbon substrate complexity. In a species-rich plant community, where all species belong to a similar taxonomic group, carbon substrate diversity may be minimal, as compared to a less species-rich community that contains a diverse set of plant families. In other words, richness at the species level might not be appropriate when inferences are to be made concerning the functional abilities of the soil fungi. Higher-level taxon diversity might serve as a better correlate. In regression analysis, the hypothesis of equality of slopes can be explored with an F test (Sokal and Rohlf 1995). Thus, measurements of plant diversity at several taxon levels (e.g., species, genera, family) could be compared on the basis of the magnitude of their slopes. The taxon(s) having the highest significant slope value should be used to determine the effect of plant diversity on fungal functional diversity.
Field tests – The FungiLog procedure provided sufficient resolution to discriminate differences in fungal functional diversity among sites at Big Bend and Fort Benning. Big Bend sites separated concurrently with elevation, while Fort Benning sites separated by degree of disturbance. Functional diversity of the soil fungi at Big Bend most likely is related to environmental constraints and the structure of vegetation assemblages. As the land rises in Pine Canyon, soil temperature falls, rainfall increases and potential evapotranspiration decreases. Thus, in the low desert, where vegetation is sparse and provides only modest amounts of litter for decomposition, soil fungal functional diversity most likely is constrained indirectly via the environmental limitations imposed on the vegetation assemblages. Due to the heterogeneity of carbon substrate input in the low desert, soil fungi may be exposed to relatively few carbon sources, resulting in low fungal functional diversity. However, in the high desert, and particularly in the closed-canopy oak forest, environmental stresses are alleviated, resulting in substantial plant productivity. Furthermore, plant litter production is much greater than such produciton in the low desert, giving rise to a homogenous input of carbon substrates. The effect of homogenous carbon substrate input in the high desert might account for greater soil fungi functional attributes, when compared with less-productive locations. However, the extent to which plant productivity versus environmental constraints control soil fungal functional diversity is unclear at this time.
All Fort Benning sites had similar climatic and environmental parameters, thus, soil functional diversity within those sites likely is regulated by differences in vegetation structure and composition, coupled with degrees of disturbance. Consequently, heterogeneity of carbon substrate input at Fort Benning increases along with disturbances and might account for reduced soil fungal functional diversity in the most highly disturbed sites. However, it is unclear why a medium amount of disturbance tends to result in the highest soil fungal functional diversity. It is possible that such sites have a greater complexity of carbon substrates resulting from intermediate disturbance. According to the intermediate-disturbance hypothesis (Connell 1978), plant species diversity should be highest in intermediate degrees of disturbance. Indeed, Fort Benning sites might fall into the context of the intermediate-disturbance hypothesis. However, further studies on plant diversity within the sites are required to make such a determination.
Most biodiversity research, whether microbial, plant or animal, most often is aimed toward understanding patterns of species richness and how richness relates to environmental parameters, including geography, climate and nutrient dynamics (Vitousek and Hooper 1993, Naeem et al 1994, Tilman and Downing 1994, Naeem and Li 1997, Chapin III et al 1998, Waide et al 1999). However, other aspects of diversity might be as important as or more important than species numbers (Walker 1992, Lawton and Brown 1993, Naeem 1998). In the case of soil fungi, functional diversity might be one such measure (Zak et al 1995, Dobranic and Zak 1999). The Soil FungiLog procedure should enable investigators to discern the functional structure of soil fungal assemblages; the procedure is applicable in different areas of study, including agriculture, ecosystem restoration, revegetation efforts, disturbance effects and the stability or successional state of natural ecosystems. Moreover, the type of carbon substrate class that accounts for the observed pattern in fungal functional diversity might provide insights into the physiology that is driving the functional response. For example, fungal assemblages at Big Bend were dependent on amino acid and carbohydrate classes of Biolog SF–N2 substrates, whereas assemblages at Fort Benning used a variety of substrate classes. The substrate classes that give rise to the discriminate function patterns might be useful in drawing conclusions concerning nutrient cycling and decomposition dynamics by providing a link among soil fungal functional diversity, fungal physiology and relevant ecosystem parameters. An upcoming paper will detail the underlying mechanisms that contribute to patterns of fungal functional diversity portrayed in this manuscript.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Accepted for publication December 23, 2002.
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LITERATURE CITED |
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