Functional Group Density as an index for assessing habitat quality in tallgrass prairie

Ecological Indicators 11 (2011) 1251–1258

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Functional Group Density as an index for assessing habitat quality in tallgrass prairie Valerie A. Sivicek, John B. Taft ∗ Illinois Natural History Survey, Institute of Natural Resource Sustainability, University of Illinois, 1816 S. Oak Street, Champaign, IL 61820, USA

a r t i c l e

i n f o

Article history: Received 3 April 2010 Received in revised form 6 January 2011 Accepted 7 January 2011 Keywords: Plant functional groups Floristic quality assessment Species diversity Biotic integrity Discriminant Analysis Rapid assessment

a b s t r a c t We propose that patterns of plant functional group occurrences could be a reliable indicator of prairie vegetation quality. A method for assessing tallgrass prairie quality based on density and composition of plant functional groups was developed and tested by comparison with qualitative indices calculated from species data at 17 prairies in Illinois. Species sample data were recorded from quadrats while functional group data were recorded from segments of belt transects overlying the species sample transects. Prairies selected include remnants and restorations and represent a wide range of habitat quality including recognized natural areas, degraded remnants, and prairie plantings of varying age and success. For agglomerative clustering of prairie quality classes, a matrix of habitat indices and metrics was used based on species sample data from all sites. Three groups were identified in cluster analysis that were characteristic of high, medium, and low-quality prairies. Mean Functional Group Density (Mean FGD), an index developed based on the mean products of frequency and density among plant functional groups recorded from belt transects at each site, had the highest correlations to habitat quality indices among two other functional group indices tested. Mean FGD was highly correlated with the mean coefficient of conservatism and floristic quality index, indices calculated from species sample data that have been shown to be reliable indicators of habitat quality. In means comparison tests among prairie quality classes, Mean FGD differentiated high-quality from medium and low-quality prairies, but did not distinguish medium from low-quality sites (only two low-quality sites were identified), although rank order of Mean FGD was as predicted. There is a tradeoff in efficiency and precision between species-level and functional group sample data. Species-level data more precisely discriminate differences in low and medium-quality sites; however, functional group sampling is much more rapid requiring only 20-to-25% of the time required for collecting species-level data. Results from functional group sampling highlight differences in functional group composition among prairie quality classes. High-quality prairies are characterized by greater abundance of sedges and hemi-parasites while lower-quality prairies were affiliated more with non-native perennial forbs and annual/biennial species. © 2011 Elsevier Ltd. All rights reserved.

1. Introduction The tallgrass prairie of the midwestern United States, once an expansive and species-rich grassland, is now a critically endangered ecosystem requiring management to prevent further degradation and species losses (Samson and Knopf, 1994; Gibson, 2009). Much of the prairie has been converted to agricultural land use due to its productive soils and manageable terrain. For example, Illinois formerly was about 55% prairie. Only about 0.01% remains in relatively undegraded condition (White, 1978; IDNR Natural Heritage Database, 2009). Of these prairie remnants, 241 in number, 79% are smaller than 10 acres and 23% are less than one acre (Taft

∗ Corresponding author. Tel.: +1 217 244 5046. E-mail address: [email protected] (J.B. Taft). 1470-160X/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2011.01.003

et al., 2009). Other prairie remnants exist but most are degraded. Historic disturbance regimes, such as periodic fires and grazing by ungulates, have been altered and many remnants have been damaged by post-settlement land uses including disturbance to surface soils and intensive livestock grazing. Prairie remnants often are invaded with non-native and woody species that can compete with and displace native herbaceous species (Leach and Givnish, 1996; Kraszewski and Waller, 2008). The small size and isolation of most remnants renders them vulnerable to physical and genetic degradation. Although few prairie species are in danger of extinction, many are at risk of regional extirpation (Taft, 1995). Due to habitat loss and degradation in tallgrass prairies, it is critical that remnants and plantings be evaluated using ecological measures or indices that provide information about their integrity and restoration potential. Common assessment tools that have been used to evaluate prairie communities include direct diversity measures, floristic

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quality assessment utilizing the mean coefficient of conservatism (Mean C) and floristic quality index (FQI) (Swink and Wilhelm, 1994; Taft et al., 1997), and the Native Species Richness Index (Bowles et al., 2000). Mean C and FQI have been found to be effective at discriminating differences in habitat quality (Taft et al., 2006). These indices are calculated from coefficients, related to individual species’ fidelity to natural areas, that are subjectively assigned in advance by regional experts. Preliminary study suggests that abundance patterns of plant functional groups, such as native C3 grasses, sedges, nitrogenfixers, hemi-parasites, and non-native species, may be useful in constructing an objective method for prairie site evaluations. Furthermore, an index based on functional groups might provide a more rapid assessment method compared to approaches reliant on species-level data. A simple yet sensitive monitoring method using functional groups would, therefore, be a valuable tool for conservation agencies and provide insights to community assemblage characteristics in tallgrass prairie. 1.1. Plant functional groups Functional groups include species with shared sets of attributes and may be constructed based on traits such as morphological or physiological characteristics, ecological roles, resource use, or response to disturbance (Symstad, 2002). Diversity in functional characteristics of species, rather than number of species alone, has been shown to serve as an important indicator of community integrity and ecological functioning (Hooper and Vitousek, 1997; Tilman et al., 1997; Mason et al., 2003). Plant species often have been categorized in ways that reflect their functional roles and responses within their communities and similarly functional groups can be defined according to their contribution to ecosystem processes or response to environmental change (Lavorel et al., 1997; Cousins and Lindborg, 2004). In comparisons of prairie plantings of different ages, Kindscher and Wells, 1995 demonstrated differences among prairie functional groups categorized by a combination of growth form, life history, and ecophysiology. Functional groups included C4 (warm-season) grasses, C3 (cool-season) grasses and sedges, annual and biennial forbs, ephemeral spring forbs, native summer/fall forbs, legumes, and woody shrubs. With the exception of phenology, similar groupings were recognized in the present study.

of non-native and short-lived species while high-quality remnants typically have greater abundance of native perennials, particularly sedges, C3 grasses, nitrogen fixers, and hemi-parasites. C4 grasses are expected to be neutral since they often are dominant in plantings and can be common among remaining species in degraded remnants. Differences in functional group composition among quality classes may suggest patterns of disassembly among degraded remnants and provide guidance in restoration efforts. 2. Methods 2.1. Site selection Seventeen prairies in central and north-central Illinois that ranged widely in quality were selected for study, including prairie remnants and plantings (Table 1). Based on species composition and soil conditions, all sites were considered mesic or dry-mesic tallgrass prairie according to White and Madany (1978). Sites were categorized as high, medium, or low-quality prairie based on results from cluster analysis (see Section 2.5). 2.2. Assignment of functional groups The functional groups used in this study incorporate aspects of life history and growth form, as with Polley et al., 2005, but also include aspects of ecophysiology and taxonomy. The following native functional groups were recognized: sedges, C3 grasses, C4 grasses, perennial forbs, annual/biennial forbs, annual legumes, perennial nitrogen fixers, hemi-parasites, and woody plants. Nonnative species were divided into three broad groups: woody plants, forbs, and grasses. Similar functional groups have been utilized in past studies (e.g., Camill et al., 2004; Fargione et al., 2003; Kindscher and Wells, 1995; Tilman et al., 1996) and in preliminary data (Taft, 2002), and represent aspects of prairie community structure commonly recognized by managers. Most of these groups are easily identifiable based on morphology. The “woody” functional groups were intended primarily as an indicator for managers to identify where encroachment might be occurring. The native nitrogenfixing shrubs Amorpha canescens and Ceanothus americanus were classified in the native perennial nitrogen-fixers category. Functional groups corresponding to each recorded species are listed in Supplementary Online Data (Appendix 1).

1.2. Study questions

2.3. Vegetation sampling methods

The purpose of this study was to develop and test a method for evaluating prairie habitat quality based on plant functional groups by addressing the following questions: Question 1—What is the relationship between indices based on functional groups and indices based on species-level data? Prediction: If a functional-group-based index is an accurate predictor of site quality, then values across sites should correlate closely with indices calculated from species-abundance data shown to be reliable indicators of habitat quality. Question 2—Can an index based on density and richness of functional groups differentiate prairie habitats of differing quality? Prediction: If prairie habitats such as degraded remnants and some plantings often have missing or scarce functional groups that typically are found in high-quality remnants and well-designed plantings, then an index based on functional groups should distinguish between sites of varying quality. Question 3—What are the differences in functional group composition between prairies of differing quality? Prediction: Results from preliminary study and observation suggest degraded sites and some plantings have greater abundance

Species and plant functional group sample data were collected along identical transects at each of the 17 prairies. Species sample data were recorded in quarter-meter square quadrats placed at 5m intervals on alternative sides along transects typically 100-mlong (slightly shorter with fewer quadrats in a few cases due to site geometry) and included frequency and percent cover for each species. Percent cover was estimated for each species using a coverclass scale (0–<1%, 1–<5%, 5–<25%, 25–<50%, 50–<75%, 75–<95%, 95–100%). Functional group sampling was done in 1-m-wide belt transects, centered on the species sample transect. Each belt transect was divided into 5-m long segments. Within each segment, occurrence of each functional group was recorded along with distance (in 1-m increments) to first occurrence. To estimate density of functional groups, the distance score was assigned in descending rank order by meter (e.g., a score of 5 if the functional group was encountered in the first meter, 4 if encountered in the second meter, 3 if encountered in the third meter, etc.). For validation of the functional group sampling method, species data from quadrat samples were transformed to functional groups for comparison.

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Table 1 Study site names, categories (remnant or planting), locations, and management and disturbance histories. No.

Site

Category

Location

Management

Disturbance history

1

Doris Westfall Prairie

Planting

40◦ 0 29 N, 87◦ 33 53 W

2

Loda Cemetery Prairiea

Remnant

40◦ 31 38 N, 88◦ 4 34 W

3

Nachusa—Dot’s Knob

Remnant

41◦ 53 32 N, 89◦ 21 7 W

Former old field stripped of topsoil, planted 1972–1990 Localized soil disturbance from cemetery usage Previously grazed

4

Nachusa—Doug’s Knoba

Remnant

41◦ 53 21 N, 89◦ 21 12 W

5

Nachusa—East Heinkel

Planting

41◦ 52 32 N, 89◦ 21 4 W

6

Nachusa—Isabel’s Knob

Remnant

41◦ 52 41 N, 89◦ 20 35 W

7

Nachusa—Main Unit

Planting

41◦ 52 49 N, 89◦ 20 58 W

8

Nachusa—Potholes

Planting

41◦ 52 56 N, 89◦ 20 57 W

9

Prospect Cemetery Prairiea

Remnant

40◦ 26 42 N, 88◦ 5 49 W

10

Rt 45

Remnant

40◦ 26 5 N, 88◦ 6 24 W

11

Rt 66—Site 1a

Remnant

40◦ 57 18 N, 88◦ 34 10 W

12

Rt 66—Site 2

Remnant

41◦ 3 47 N, 88◦ 27 54 W

Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Periodic burning and weed control Unmanaged

13

Rt 66—Site 3a

Remnant

41◦ 6 55 N, 88◦ 23 58 W

Unmanaged

◦  

◦





14

Rt 66—Site 3b

Remnant

41 7 8 N, 88 23 42 W

Unmanaged

15

Rt 66—Site 4

Remnant

40◦ 59 13 N, 88◦ 32 25 W

Unmanaged

16

Unity East Prairie

Planting

40◦ 1 34 N, 88◦ 9 22 W

17

Weston Cemetery Prairiea

Remnant

40◦ 44 48 N, 88◦ 36 50 W

Periodic burning and weed control Periodic burning and weed control

a

Previously grazed Previously plowed and row-cropped, planted 1995 Previously grazed Previously plowed and row-cropped, planted 1991 Previously plowed and row-cropped, planted 1992 Localized soil disturbance from cemetery usage Soil disturbances, fire absence Possible localized soil disturbance Soil disturbances, fire absence Possible localized soil disturbance Soil disturbances, fire absence Soil disturbances, fire absence Previously plowed and row-cropped, planted 2003 Localized soil disturbance from cemetery usage

Sites that are formally recognized as natural areas; underlined site names are dedicated Illinois nature preserves.

Two transects were sampled at most sites and data were averaged (three short prairie segments in narrow RR ROW were sampled with single transects [Rt 66-1, Rt 66-3a, Rt 66-3b]). Transect placement was random or randomly selected from pre-existing set of stratified parallel permanent transects. 2.4. Index and community metrics calculations 2.4.1. Species-level indices from quadrat sample data Species-level metrics were calculated at the quadrat-scale and included native species density, Shannon–Wiener diversity, Simpson’s dominance index, evenness, mean coefficient of conservatism, and floristic quality index (Whittaker, 1975; Taft et al., 2006) calculated as follows: Native species density: mean native richness/quadrat, Non-native species Density: Mean non-native richness/quadrat, Proportion of exotic species: % of non-native  species/quadrat, Shannon–Wiener index of diversity (H n): − [pi ln(pi )], where pi is the relative abundance of each native species (based on importance values [IV200] calculated as the sum of relative cover and relative frequency),  2 Dominance: pi , where pi is the importance for each (native and non-native) species, Evenness: H /ln (native species density),  Mean coefficient of conservatism (Mean C): CC/S, where CC = coefficient of conservatism and S = total species richness, and √ Floristic quality index (FQI): Mean C ( N) where N = native species richness. Mean Cn and FQIn are calculated using only native species.

2.4.2. Functional group indices calculated from belt transect data Three indices based on plant functional groups were calculated for each belt transect, and averaged per site. The first index is the average count of native, non-woody functional groups per transect segment; the other two calculate density scores for each functional group as the product of frequency among transect segments and either the sum of density scores or the average of density scores for each functional group. The final index is a sum of these values across native functional groups subtracting the sum of density scores for non-native groups. An example field data form with each functional group index is available as an online resource (Appendix 2). The indices are calculated as: Native Functional Group Density (FGDn): Mean count of native functional groups occurring in transect segments (%FGDn [Appendix 2] provides equivalent information), Sum Functional Group Density (SumFGD): [(sum density score for each native functional group) × (frequency of native functional groups)] − [(sum density score for alien functional groups) × (frequency of each alien functional group)], and Mean Functional Group Density (Mean FGD): [(average density score for native functional groups) × (frequency of native functional groups)] − [(average density score for alien functional groups) × (frequency of alien functional groups)]. 2.5. Data analysis Site membership in high-quality, medium-quality, and lowquality prairie classes was determined from patterns of agglomerative clustering (McCune and Mefford, 1999) based on a matrix of habitat quality and diversity indices calculated for each site and defined in Section 2.4.2. Cluster analysis was based on the

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Fig. 1. Dendrogram of results from cluster analysis. Three prairie quality groups (high-quality [♦], medium-quality [], and low-quality prairie []) were identified with classification and site membership was supported 100% by Discriminant Analysis. Site numbers to the left of the abbreviated site names correspond to full site names in Table 1.

3. Results Results from cluster analysis indicated three broad prairie quality classes can be distinguished with 70% of the information retained in the dendrogram (Fig. 1). Group number was supported by the distinct site clusters and group membership mostly was consistent with empirical field assessments. (One prairie considered medium quality based on field observations [Rt66 2] was classified by cluster analysis with high-quality prairies and one prairie considered low quality [Potholes] was classified with medium-quality

prairies.) Frequency distribution was 9, 6, and 2 for high-quality, medium-quality, and low-quality prairies, respectively, and site classifications were supported 100% by Discriminant Analysis. Mean values for indices used in cluster analysis are summarized by prairie quality class in Table 2. Of the three functional group indices tested, Mean FGD had the highest Pearson r values in correlations with 6 of 10 indices calculated from species-sample data and Mean FGD correlated nearly equally well to Native FGD in correlations with diversity (H ) and dominance (Table 3). Consequently, Mean FGD was used as the primary functional group index for addressing research questions. Mean C and FQI are highly correlated to Mean Cn and FQIn, respectively; only results for Mean Cn and FQIn are shown. Question 1—Mean Functional Group Density (Mean FGD) is correlated with several habitat indices. However, following Bonferroni correction for multiple comparisons (P = 0.05/8 [P < 0.0063]), Mean FGD is correlated significantly only with Mean Cn, FQIn, and nearly (P = 0.0065) native species density (Table 3). Question 2—Differences in Mean FGD among the three prairie quality classes were significant (F = 7.49, P = 0.006) with pairwise significant differences from planned Tukey post-hoc tests between the high-quality and both the medium and the lowquality prairies. However, no difference was detected in Mean FGD

25 (F-ratio 7.61, P = 0.006) 20

Mean FGD

Sørensen similarity distance measure and flexible Beta linkage method (ˇ = −0.25); these settings provided the most biologically interpretable dendrogram. The metrics were log-transformed to address the differing scales of the indices. All log-transformed values for Dominance and Evenness were negative and for cluster analysis were further transformed to positive values. Question 1—The Pearson coefficient of correlation was calculated as an estimate of the comparative correlation strength of the three functional group indices to the species-level habitat indices. The functional group index selected for analysis was determined from the index with the greatest number of highest r values. The Bonferroni adjustment for multiple comparisons (P = 0.05/8) was then used in determining significance of the correlations of the selected functional group index with the selected habitat indices and metrics. Question 2—Differences in the functional group index among prairie quality classes were examined with Analysis of Variance (ANOVA) followed by planned Tukey post-hoc tests. Question 3—Differences in functional group composition between qualitative site categories were examined with Discriminant Analysis (SPSS, 2000). Results from functional group sampling were contrasted with functional group data derived from specieslevel sampling. Differences in rank order of functional group importance among the high-quality prairie sites, comparing results from the two sample methods, were examined using Spearman’s rho (SPSS, 2000). Using species sample data, indicator species analysis (McCune and Mefford, 1999) was used to identify taxa from any functional groups found in Discriminant Analysis to be significantly affiliated with particular prairie quality classes.

15

a

b b

10 5 0 High-Quality Prairie Medium-Quality Prairie Low-Quality Prairie

Fig. 2. Comparison of Mean Functional Group Density (Mean FGD) among prairie quality classes. Different letters indicate significant differences (P < 0.05) from Tukey post-hoc tests. Error bars are standard error.

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Table 2 Mean scores and standard error for habitat indices and metrics used in classification of habitat quality classes. Refer to Fig. 1 for site quality-class membership. SE = standard error. Line gap separates direct measures (above) from species-weighted indices. Acronyms are defined in Section 2. Indices/metrics

High-quality prairie (N = 9)

Medium-quality prairie (N = 6)

Low-quality prairie (N = 2)

Mean

SE

Mean

SE

Mean

SE

Native Spp. density Non-native Spp. density %Non-native Dominance Diversity (H ) Evenness

10.73 2.08 15.44 0.05 3.32 0.86

0.46 0.39 2.40 0.01 0.09 0.01

7.84 2.77 25.43 0.08 2.84 0.81

0.59 0.46 3.01 0.01 0.12 0.03

3.79 2.35 37.51 0.15 2.34 0.77

0.66 1.00 14.23 0.02 0.27 0.09

Mean Cn Mean C FQI FQIn

4.56 3.85 12.55 14.79

0.15 0.18 0.41 0.30

3.70 2.77 7.78 10.28

0.23 0.26 0.81 0.85

2.80 1.78 3.61 5.43

0.08 0.45 1.06 0.55

Table 3 Pearson’s coefficients (r) for correlations with each functional group (FG) index and habitat indices and metrics. Significant coefficients (P < 0.05) are indicated in bold. Underlined coefficients indicate significance following Bonferroni correction (P < 0.0063). FGD = Functional Group Density. X = non-native, N = native. See Section 2 for definition of indices. FG indices

N Spp density

X Spp density

%Non-native

Mean Cn

FQIn

Dominance

H

Evenness

Native FGD Sum FGD Mean FGDa

0.587 0.585 0.632

0.002 −0.292 −0.281

−0.301 −0.549 −0.561

0.606 0.683 0.744

0.661 0.699 0.780

−0.603 −0.481 −0.576

0.546 0.396 0.504

0.605 0.408 0.482

a

Index selected for further analysis based on frequency of highest r values among other functional group indices.

between medium and low-quality prairies, although the rank order sequence was as predicted (Fig. 2). Question 3—Results from Discriminant Analysis using functional group sample data indicate that functional groups can distinguish prairie quality classes and sedges, hemi-parasites, and non-native perennial forbs were identified through stepwise forward selection as functional groups with significant association to a particular prairie quality class (Table 4). These same functional groups along with annual/biennial forbs and native C3 grasses were identified using species sample data converted to functional group data (Table 4). Separation of prairie quality classes based on canonical scores plots is greater using species sample data converted to functional groups compared to functional group sample data (Fig. 3). However, functional group sampling was much more rapid requiring approximately 1 h per transect compared to 4–5 h for species-level sampling depending on levels of species richness, plus additional time after sampling to identify sterile material. Several sedge species identified by indicator species analysis are affiliated with the high-quality prairies (e.g., Carex bicknellii, C. brevior, C. gravida, C. meadii, C. pellita, and C. pensylvanica). However, none were statistically significant indicators. Two hemi-parasites were encountered in species sampling: Comandra umbellata, and Pedicularis canadensis. C. umbellata was a significant indicator for high-quality prairies. Numerous non-native forbs were characteristic of the medium-to-low quality prairies, but only three are

significant indicators (Chenopodium albidum, Cirsium arvense, and Taraxacum officinale). The pattern of descending rank order of functional groups, based on relative abundances in high-quality prairies, is similar between the functional group and species sample data (Fig. 4). The rank orders of these groups derived from quadrat and belt-transect sample methods are highly correlated (Spearman’s rho 0.96, P < 0.00001). 4. Discussion Study results indicate that Mean Functional Group Density (Mean FGD) is positively correlated with Mean Cn and FQIn, which have been shown to be sensitive indicators of vegetation quality in tallgrass prairie (Taft et al., 2006). Mean FGD provides information about habitat quality, differentiating high-quality from lower quality prairies. Other studies have indicated functional diversity is an important component of biodiversity, and that functional composition may be critical to ecosystem functioning (Hooper and Vitousek, 1997). Functional diversity may be even more ecologically meaningful than species diversity in measuring biodiversity (Diaz and Cabido, 2001) and predicting invasion resistance of a community (Fargione et al., 2003; Prieur-Richard and Lavorel, 2000; Symstad, 2000; Dukes, 2001a,b). Thus, diversity and composition of plant functional groups appear to have sound basis as measures of habitat quality.

Table 4 Results from Discriminant Analysis stepwise forward selection using both functional group sample data and species sample data, the latter transformed to functional groups. Functional groups shown are those indicated by Discriminant Analysis to be significantly affiliated with one of the habitat quality classes. AB = annual/biennial, HemiPar = hemiparasite, X = non-native, P = perennial. Variable entered

F-to-remove

Functional group data from functional group sampling Sedge 10.88 HemiPar 3.42 XPForb 5.00 Functional group data transformed from species sampling ABForb 18.73 XPForb 5.28 HemiPar 5.90 Sedge 2.77 C3NGrass 4.39

Wilks’ lamda

Approx. F-value

df1

df2

0.3915 0.2566 0.1400 0.2721 0.1502 0.0757 0.0504 0.0268

P

10.88 6.33 6.70

2 4 6

14 26 24

0.0014 0.0011 0.0003

18.73 10.27 10.54 9.50 10.22

2 4 6 8 10

14 26 24 22 20

0.0001 <0.0001 <0.0001 <0.0001 <0.0001

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3 2

FACTOR(2)

1 0 -1 Classes

-2

1-HQ Prairie 2-MQ Prairie 3-LQ Prairie

-3 -4 -4

-3

-2

-1

0

1

FACTOR(1)

2

3

8 Classes 1-HQ Prairie 2-MQ Prairie

FACTOR(2)

4

3-LQ Prairie

0

-4 -4

0

FACTOR(1)

4

8

Fig. 3. Canonical scores plots from Discriminant Analysis comparing differences between functional group sampling (top) and species-level sample data transformed to functional groups (bottom figure). Eigenvalues for functional group sample data are 4.47 and 0.33 for factors 1 and 2, respectively, with cumulative proportion of total dispersion is 93% on the first axis (factor 1). Eigenvalues for species sample data are 9.78 and 2.46 for factors 1 and 2, respectively, with cumulative proportion of total dispersion is 80% on the first axis (factor 1).

4.1. Patterns of functional diversity and composition with disturbance Results from this study highlight differences among prairie quality classes and are suggestive of a pattern of disassembly. High-quality prairies were found to contain more native functional groups than medium to low-quality prairies, consistent with other results (Polley et al., 2005), and in this study high-quality remnants were more strongly associated with native sedges, hemiparasites, and C3 grasses compared with most degraded remnants and plantings. Native perennial nitrogen fixers (primarily legumes) also appeared to be correlated with higher quality prairies, but contrary to initial expectations were not identified as being significantly affiliated with prairie quality classes. Characteristics of functional group composition in high-quality remnant prairies provide vital benchmarks for both designing planting mixtures so that restorations might more closely resemble and function as natural prairies and in interpreting the assemblage of functional groups in degraded sites for evidence of restoration potential. The medium-to-low quality prairies in this study occur either in RR ROW, formerly were utilized as pastures, or are prairie restorations (plantings) of varying age. The prairie restorations all occur on former cropland. In addition, many of the degraded remnant prairies likely also have had soil disturbances related to devel-

opment and maintenance of road, railway, and utility corridors and other disturbances. As expected, the lower quality prairies were composed of more ruderal species and a greater proportion of non-native and short-lived species. Soil disturbances have been shown to increase abundance of non-native forbs and grasses relative to native species (Biondini et al., 1998) and can include early-successional (ruderal) species with long-lived seed banks (Thompson et al., 1996; Lavorel et al., 1998, 1999a). The greater sensitivity to patterns of annual/biennial species detected in transformed species sample data suggests these sometimes diminutive groups may have been overlooked somewhat in the functional group sampling effort, something that could be addressed with renewed focus in future applications. In many restorations, either some functional groups including sedges, native C3 grasses, and perennial nitrogen fixers are not included in seed mixes in adequate quantities or these groups are more difficult to establish. Studies have shown that restorations actually tend to lose species and maintain lower levels of species richness than remnant prairies (Kindscher and Tieszen, 1998; Martin et al., 2005), and that C4 grasses often dominate restorations within a few years of planting to the exclusion of other species (Camill et al., 2004; Kindscher and Tieszen, 1998; Sluis, 2002). This effect may be exacerbated at some plantings by frequent burning (Collins et al., 1998; Copeland et al., 2002). A notable exception is the Doris Westfall Prairie, a planting aligned with the high-quality prairie class in this study that is remarkable for its age (35 years at the time of sampling) and quality resulting from unique site conditions including scraped surface soils on former cropland (perhaps lowering fertility), a diverse planting design including over-seeding, and intensive management including control of nonnative species. In recognition, this is the only prairie restoration in Illinois dedicated as a nature preserve. Groupings based on broad categories, such as “forbs”, risk loss of sensitivity from contrasting responses among members (Lavorel et al., 1999b). The functional groups utilized in this study (combining elements of life history, growth form, and ecophysiology), meet the requirement of ease of recognition and in many cases sensitivity to habitat disturbance. Groupings based on functional responses alone have been found to be useful in land management as indicators of vegetation trends (Diaz et al., 2002; Gondard et al., 2003a). Jauffret and Lavorel (2003) and Gondard et al. (2003b) developed a state and transition model for using functional response groups to assess plant community degradation and to monitor community response to management or disturbance. However, distinguishing response types may be limited at times by incomplete knowledge. 4.2. Practical applications Based on canonical scores plots and Discriminant Analysis, functional group data transformed from species-level sample data recorded in quadrats, compared to direct measures of functional groups from belt-transects, differentiate habitat quality classes and functional group affiliations somewhat more effectively than data from functional group sampling. A larger sample size of low-quality prairies may improve the capacity for Mean FGD to discriminate medium and low-quality sites. There is a trade off in precision and efficiency between functional group and species sample efforts with functional group sampling requiring only 20-to-25% of the time required for species-level sampling while providing similar information. Given the similarity of results and conclusions, particularly the capacity of functional group sample data to distinguish mean differences and rank order patterns of functional groups among habitat quality classes, there is good support for Mean FGD as a useful and rapid indicator of prairie habitat quality, but the noted trade-off between precision and efficiency should be recognized. In vegetation monitoring programs, it is suggested

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Fig. 4. Distribution of plant functional groups among prairie quality classes comparing results from functional group sampling, based on frequency and density of functional groups in 5-m-long segments of belt transects, and species-level sample data. Species sample data were based on quarter-meter square quadrats sampled along the same transect as functional group sample data and used percent cover values for each species. Results are presented in descending rank order of data from high-quality prairies. Woody species groups were removed since they are related more to time since last fire than habitat integrity. A = annual, A/B = annual/biennial, P = perennial, N = nitrogen, X = non-native.

to use functional group sampling for periodic monitoring to lessen the need for more intensive species sampling efforts. Applications include temporal comparisons as well as comparisons among sites of similar habitat types. With additional research, the method could be extended to other vegetation types. Acknowledgements Much thanks to Frank Hassler, Molly McNicoll, Brent Wachholder and Jason Zylka for field assistance and to Michael Murphy and Loy R. Phillippe for assistance in identification of some sterile material. We are grateful to Bill Kleiman for access and accommodations at Nachusa Grasslands, and to the Illinois Nature Preserves Commission and the Forest Preserve District of Vermilion County, Illinois for access to nature preserves. Thanks also to Ken Robertson, Carol Augspurger, and Tony Endress for insightful comments on a draft of this manuscript. Finally, we thank the Illinois Department of Natural Resources for support through a Wildlife Preservation Fund grant. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ecolind.2011.01.003.

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