Effects of Levels of Human Disturbance on the ...

American Fisheries Society Symposium 48:199–219, 2006 © 2006 by the American Fisheries Society

Effects of Levels of Human Disturbance on the Influence of Catchment, Riparian, and Reach-Scale Factors on Fish Assemblages Lizhu Wang*, Paul W. Seelbach Institute for Fisheries Research Michigan Department of Natural Resources and University of Michigan 212 Museums Annex, Ann Arbor, Michigan 48109, USA

John Lyons Fisheries and Habitat Research, Wisconsin Department of Natural Resources, 1350 Femrite Drive, Monona, Wisconsin 53716, USA

Abstract.—We analyzed data from 287 streams in Wisconsin and northern Michigan to evaluate the relative effects of human disturbance levels on the influence of catchment, network riparian, reach riparian, and instream variables on fish assemblages. The streams were divided into high, medium, and low human disturbance groups based on catchment and network riparian urban and agricultural land uses. We used canonical correspondence analyses to evaluate relations among variables at the four spatial scales and fish assemblage composition, abundance, and presence/absence and to partition the relative importance of spatial scales. Catchment and network riparian land uses were among the dominant variables correlated with fish for high disturbance catchments but not for low disturbance catchments. The variations in fish assemblage composition, abundance, and presence/absence explained by catchment factors were substantially higher for high than for low disturbance catchments, although the variations explained by network riparian factors and reach riparian land uses were similar among disturbance levels. In contrast, the variations in fish variables explained by instream factors and the interaction of the four spatial scale environmental factors were considerably lower for high disturbance than for low disturbance catchments. We concluded that in largely undisturbed catchments, fish assemblages were predominantly influenced by local factors, but as disturbance increased in catchments and riparian areas, the relative importance of local factors declined and that of catchment increased. Hence, instream and riparian habitat improvements would be most effective in catchments that are largely undisturbed and catchment scale land-use management would be more effective for improving stream quality in degraded catchments.

INTRODUCTION To maintain and improve stream ecosystem health, managers must understand how streams and their biological assemblages are shaped by both natural and human-induced environmental factors that operate at a variety of spatial and *Corresponding author: [email protected]

temporal scales. Understanding which environmental factors are most influential, and the spatial scales at which this influence is manifest, is essential for directing conservation and rehabilitation efforts to the factors and scales where management activities are most effective. Traditionally, stream rehabilitation and conservation efforts have mainly focused on riparian areas and instream physical and chemical

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Wang, Seelbach, and Lyons

habitats. This is partly because the structure of fish assemblages is strongly influenced by local factors such as water depth, current velocity, size of substrate particles, cover, bank condition, canopy shading, food sources, temperature, and other physicochemical variables (Rabeni and Jacobson 1993; Wang et al. 1998). While some rehabilitation projects have been successful, others focused on improving local habitat conditions have failed to improve fish assemblages. For example, streambank fencing improved fish assemblages and trout standing crops in a Colorado stream where the catchment was largely rangeland (Stuber 1985). And streambank and channel habitat improvements significantly increased brook trout abundance in a Wisconsin stream draining a largely forested catchment (Hunt 1976). On the other hand, after a 6-year implementation of stream bank fencing to exclude livestock significantly improved instream habitat, the fish assemblage remained degraded in a Wisconsin stream because the catchment was heavily altered by agriculture (Wang et al. 2002). Moerke and Lamberti (2003) also reported that stream rehabilitation, by reconnecting historical meanders to the channelized Potato Creek, Indiana, resulted in low fish densities, altered assemblage structure, and domination by slow-water, silt-tolerant fish species. These successes and failures of local management practices have been attributed to variation in the spatial scale of the dominant influential factors (e.g., Wang et al. 2002, 2003a; Moerke and Lamberti 2003). The more dominant local factors are in influencing biological communities, the more successful local management practices can be. Hence, identifying dominant factors and scales that are most influential to fish assemblages is critical to the success of stream improvement. Both catchment and local characteristics explained considerable amounts of variation in fish assemblages (e.g., Marsh-Matthews and Matthews 2000; Stauffer et al. 2000; Zorn 2003). Although an increasing number of studies have evaluated the relative importance of different spatial scale factors in influencing biological as-

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semblages, conclusions have been inconsistent. Several studies have indicated that catchment factors accounted for more variation in stream biotic integrity than did local conditions (e.g., Roth et al. 1996; Allan et al. 1997; Wang et al. 1997, 2001), whereas others have found just the opposite (e.g., Lammert and Allan 1999; Wang et al. 2003a). The less the catchment is disturbed, the more local factors are believed to determine biological assemblages, and vice versa (DeBano and Schmidt 1989; Wang et al. 2002, 2003a). This hypothesis has not been tested directly (Wang et al. 2003a). In this study, we tested the hypothesis that in largely undisturbed catchments, fish assemblages are predominantly influenced by local factors (instream habitat and riparian conditions), but as the level of disturbance increases in the catchments, the relative importance of local factors declines and that of catchment increases. We also determined whether the dominant influential factors differed among catchments with different human disturbance levels.

METHODS Study Areas Data were collected from 287 sites on 1st- to 4thorder streams across Wisconsin and northern Michigan (Figure 1). Sites were selected to be easily accessible, represent a range of anthropogenic influence, and cover a range of natural variation in stream and catchment characteristics. The study sites represent the range of catchment surficial geology types, land relief, and stream thermal regimes found across Wisconsin and northern Michigan. In northern Wisconsin and the upper peninsula of Michigan, the landscape characteristics are dominated by undulating till plains, morainal hills, broad lacustrine basins, and extensive sandy outwash plains with low relief. Central Wisconsin and northern lower Michigan are typified by flat to rolling glacial till plains, lacustrine basins, outwash plains, and rolling to hilly moraines and beach ridges. Hilly

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Figure 1. Sampling locations in Michigan and Wisconsin. Open circles are high disturbance sites, filled triangles are medium disturbance sites, and filled circles are low disturbance sits.

uplands dominated by a loess-capped plateau that is deeply dissected by stream valleys are characteristics of southwestern Wisconsin, and outwash plains, lacustrine basins, and flat to rolling till plains were the characteristics of southeastern Wisconsin. The study streams are dominated by cool- and coldwater systems in the north and a mixture of cool-, cold-, and warmwater systems in the southern part of the study area. The study sites also have a range of catchment sizes, land-cover types, and local instream conditions. Catchment size varies from less than 5 to 1,006 km2 (mean = 136). The combination of woodland, wetland, and water ranges from 2% to 100% of the catchment. Agriculture land ranges from 0% to 93% (mean = 36%) and urban land varies from 0% to 52% (mean = 3%). Stream wetted width varies from 2 to 56 m (mean = 9).

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Catchment and Buffer Data Catchment boundaries upstream of each sampling site were delineated using Arc View WATERSHED Avenue Command Procedures (ESRI 2002) and a Digital Elevation model with a 30-m resolution. Catchment surficial geology, soil permeability, bedrock depth and geology, growing degree-days, annual precipitation, land use/-cover, and groundwater delivery potential (Baker et al. 2003) within each catchment were quantified using ARC/INFO software to overlay catchment boundaries on these readily available database layers assembled by an ongoing stream segment modeling and classification project (Brenden et al. 2006, this volume). Stream riparian characteristics within 150 m centered at the stream line for the channel network were also gathered using the same landscape database layers.

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Instream Habitat and Fish Sampling Stream physical habitat, dissolved oxygen, discharge, and fish data were collected once between 1997 and 2002. We assessed physical habitat at a site length of 35 times the mean stream width or a minimum of 100 m. This length was sufficient to encompass about three meander sequences (Simonson et al. 1994; Wang et al. 1996). We sampled physical habitat and fish between late May and late August when low stream flows facilitated effective sampling and large-scale seasonal fish movements were unlikely to occur (Lyons and Kanehl 1993). At each site, 30 habitat variables, including channel morphology, bottom substrates, cover, bank conditions, and riparian vegetation and land cover, were measured or visually estimated along 12 transects using standardized procedures (Simonson et al. 1994). Dissolved oxygen was measured using a YSI oxygen-conductivity meter (model 85) and discharge was measured with a Flow-Mate portable flowmeter (model 2000) at the downstream end of each site before sampling physical habitat. Continuous water temperatures were recorded using Onset Stow-Away temperature loggers between mid-May and late September. The entire length of each site was electrofished once with either two backpack units in tandem or a single tow-barge unit with three anodes (Lyons and Kanehl 1993; Simonson and Lyons 1995). Efforts were made to collect all fish observed, and all captured fish were identified and counted.

Data Summary The catchment, network riparian, reach riparian land-use, and instream habitat variables were organized into four data sets and summarized before statistical analysis. From the catchment and network riparian data, we summarized surficial geology, bedrock geology, bedrock depth, and land use/cover as percentages of the total surface area of each catchment or riparian zone upstream of the sampling reach. The an-

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nual precipitation, number of growing degreedays, soil permeability, bedrock depth, and potential groundwater delivery rates were averaged across each catchment. From the reach riparian land-use and instream habitat data, we calculated the frequency of occurrence for algae, macrophytes, shading, and fish cover; the percent composition of substrate, embeddedness, riffle/run/pool, bank condition, riparian land-use categories; and the means of thalweg depth, wetted width, and sediment depth. We combined some of these individual variables into additional summary variables. We also calculated the ratio of stream width to depth and coefficients of variation for sediment depth, embeddedness, and water depth. From the continuous water temperature data, we determined the maxima for 7-d means of daily means, daily maxima, and daily ranges; maxima for 21-d means of daily means, maxima, and ranges; maxima for July means of daily means, maxima, and ranges; and maxima for daily means, daily maxima, and daily ranges for June through August. We created three fish data sets: fish assemblage characteristics, fish abundance (individuals/100 m by species), and species presence/absence. For the abundance and presence/absence data sets, we included only species that occurred at more than 14 sites (ⱖ 5% of sites) and had at least one site with five or more individuals per 100 m. This criterion reduced the number of fish variables and minimized the influence of rare species on results. The fish assemblage data set included 14 variables (Table 1). Thermal, feeding, tolerant, and reproduction classifications were based on Lyons (1992) and Lyons et al. (1996), and the Shannon diversity index was calculated based on Magurran (1988). The index of biotic integrity (IBI) score was calculated using the coldwater version for streams that had maximum daily mean water temperatures less than 22°C (Lyons et al. 1996) and the warmwater version for streams with temperatures greater than 24°C (Lyons 1992). For streams with intermediate temperatures, we calculated both versions and

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Table 1. Fish assemblage characteristics variables, with variable abbreviation and their summary statistics for the study sites. Variable

Abbreviation

Mean

Standard deviation

Minimum

Fish abundance (individuals/100 m) Number of fish species % of top carnivore individuals % of cool- and coldwater individuals % of cool- and coldwater species % of invertivore individuals % of intolerant individuals % of omnivore individuals % of salmonid individuals % of salmonid species % of simple lithophil individuals % of tolerant individuals IBI score Shannon diversity index

Abundance Fishsp Pcarniv Pclcdiv Pclcdsp Pinviv Pintoiv Pomniv Psaiv Psasp Plithiv Ptoliv IBI Shan

368.4 11.6 12.7 25.9 24.7 49.5 18.5 16.1 10.4 7.6 30.5 42.6 42.1 1.7

442.6 5.7 20.6 32.1 23.3 23.8 25.4 16.9 20.6 12.3 22.5 28.8 22.1 0.5

20.0 2.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2

used the higher of the two scores; a coolwater IBI does not yet exist for the study region. Although our data originated from a 7-year period, the majority of habitat and fish data were collected between 1998 and 2000. A small portion of the study sites were sampled in multiple years, and we used the mean of each variable for each site. Our preliminary analysis on sites with multiple-year data and previous studies (Wang et al. 1996; Hughes et al. 1998; McCormick et al. 2001) indicated minor temporal changes in fish assemblage and habitat measures. Our exclusion of fish that occurred in less than 5% of sites and species that had maximum catch of less than 5 individuals per 100 m minimized the influence of temporal variation in fish abundance and presence/absence. Additionally, no substantial land-use change occurred in the study catchments during the study period (Wang et al. 2002, 2003a, 2003b; Baker et al. 2005).

Data Analysis We divided the 287 sites into three groups according to urban and agricultural land uses based on previous findings of relationships between fish assemblages, and urban or agricultural land uses in catchments and in riparian areas (Wang

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Maximum 3,006.0 33.0 95.9 100.0 100.0 100.0 100.0 81.1 95.9 66.7 88.9 100.0 100.0 2.7

et al. 1997, 2001, 2003b). High disturbance sites (87 sites) had either catchment urban land use greater than 20% or agriculture greater than 70%; or network riparian urban land use greater than 10% or agriculture greater than 50%. Low disturbance sites (72 sites) had either catchment urban land use less than 2% or agriculture land use less than 10%; or network riparian urban land use less than 1% and agriculture land use less than 5%. Although agricultural and urban land uses were minimal in the catchments of this data set, some legacy effects from previous logging or burning might remain (Richards 1976; Harding et al. 1998). Medium disturbance sites (128 sites) had intermediate levels of urban and agricultural land uses. We conducted two multivariate analyses on the three data sets of different disturbance levels. The first analysis was to test the hypothesis that in largely undisturbed catchments, fish assemblages are predominantly influenced by local factors (instream habitat and reach riparian land uses), but as level of disturbance increases in the catchments, the relative importance of local factors declines and that of catchment increases. We performed a canonical correspondence analysis (CCA) forward selection procedure (ter Braak and Smilauer 1998) to select the environmental

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variables that were significantly (p < 0.05) correlated with CCA axes in each of the 36 data pairs—fish assemblage characteristics, abundance, and presence/absence data sets paired with each of the high, medium, and low disturbance data sets; and paired with each of the catchment, network riparian, reach riparian land-use, and instream habitat using CANOCO software (ter Braak and Smilauer 1998). We then conducted a CCA partition procedure (Borcard et al. 1992) to estimate the relative importance of catchment, network riparian, reach riparian land-use, or instream habitat in explaining the fish variables at the three-level disturbance data sets using the selected environmental variables. The second analysis was to evaluate whether the influential factors at the four spatial scales were different among the three data sets that had different disturbance levels. Using CANOCO software (ter Braak and Smilauer 1998), we first performed a CCA forward selection procedure to select the environmental variables that were significantly (p < 0.05) correlated with CCA axes in each of the nine data pairs—fish assemblage characteristics, abundance, presence/absence data sets paired with each of the high, medium, and low disturbance data sets. We again used CANOCO software to conducted CCA on each data-set pair using only the retained environmental variables to examine the loadings of both the fish and environmental variables on the resultant CCA axes for each data set pair.

RESULTS Fish Assemblage Characteristics We collected 109 fish species during the study period (Appendix 1). Species richness per reach ranged from 2 to 33 with a mean of 12. The most frequently occurring fishes were white sucker Catostomus commersonii (81% sites), creek chub Semotilus atromaculatus (73% sites), central mudminnow Umbra limi (56% sites), Johnny darter Etheostoma nigrum (54% sites), and common shiner Luxilus cornutus (53% sites). The

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catch at sampling sites ranged from 20 to 3,006 fish per 100 m, with a mean of 375 individuals per 100 m. About 78% of the sites supported cool-/coldwater fishes, and 42% of the sites contained salmonids. Species intolerant of environmental degradation occurred at 76% of the study sites and comprised more than 50% of the individuals at 14% of the sites. Forty-two percent of the study sites had IBI scores greater than 50 and 26% had scores less than 30.

Disturbance Level Effect on Scales Influencing Fishes Fish assemblage characteristics.—For the fish assemblage characteristics-high disturbance data pair, the selected 33 different-scale environmental factors (Appendix 2) explained 77% of the variance in fish variables. Interactions among factors at the four spatial scales explained the most variation (38%); catchment and instream habitat explained moderate amounts (26% and 28%), and network riparian and reach riparian land-use explained the least (each 40 ft (%) BDEPMENW Mean bedrock depth (feet) BIGNMETW Metamorphic/igneous bedrock geology (%) BSANDSTW Sandstone bedrock geology (%) Catchment soil permeability Q25P150W Soil permeability < 150 in 100 h–1 (%) Q50P265W Soil permeability < 265 in 100 h–1 (%) Q75P500W Soil permeability < 500 in 100 h–1 (%) SOILPERW Mean soil permeability (in 100 h–1) Network riparian variables Agricultural land use (%) AGRICB BARRENB Barren land (%) FORESTB Forest land (%) GRASSB Grass land (%) URBANB Urban land (%)

Assemblage characteristics

Abundance

Mean ± (1 SE)

H

M

L

H

M

L

H

M

L

136.4 + 14.4 35.5 + 1.7 1.5 + 0.1 34.7 + 1.6 12.1 + 0.6 2.9 + 0.5 1.5 + 0.2 11.8 + 0.7 9.4 + 0.5

X X – X – X X – X

X X – – – X X X X

X – – – X – X – X

X X X – – X X X X

X X X – X X X X X

X – – – – – X – X

X X X X X X – – X

X – X – X – X X –

X – – – – – X – X

2,290.2 + 23.3

X

X

X

X

X

X

–

–

X

736.0 + 14.4

–

X

X

–

–

X

–

–

–

63.8 + 2.5 13.2 + 1.8 5.8 + 1.2 16.1 + 2.1 1.2 + 0.3

– – – X –

– – – X –

– X – – X

X – X X –

X – X X –

– – – – X

– – – – –

– – – – –

– – X – –

44.1 + 2.5 40.9 + 2.4 24.1 + 1.7 24.2 + 1.9 7.7 + 1.2 3.1 + 0.9 102.8 + 5.6

X – – X – – X

X – – – – – X

– – – X – X –

– X – – – – X

– – X – – – X

– – – X – – X

X – – X – – X

– – X – X – X

– – – – – – –

25.6 + 2.5 24.5 + 2.0

– –

– –

– X

– X

– –

– X

– –

– –

X –

23.1 + 2.1

–

X

–

–

X

–

–

–

–

48.8 + 2.5

–

–

–

X

–

–

–

X

–

62.1 + 2.4

X

–

–

X

–

–

–

–

–

381.8 + 17.4

–

X

–

–

X

X

–

X

–

27.6 + 1.5 0.9 + 0.1 28.5 + 1.3 10.5 + 0.6 2.5 + 0.5

X – X X X

– X X – X

– – – X X

X X X – X

X – – – X

– – – X X

X – X – X

– X – – –

– – – – –

(Appendix continues)

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Appendix 2 (continued) Presence/ absence Variable WETLANDB BDEP05B BDEP51B BDEP12B BDEP24B BDEP46B BDEPMENB Q25P150B Q50P265B Q75P500B SOILPERB BCARBONB BIGNMETB BSANDSTB BSHALEB DARCYB GCO ARSEB GCOARSEB GFINEB GMEDIUMB GNOTEXTB GPEATMUB

Description

Mean ± (1 SE)

H

M

L

H

M

L

H

M

L

Wetland (%) 30.0 + 1.3 Bedrock depth < 5 ft (%) 14.0 + 1.3 Berock depth 5–10 ft (%) 18.6 + 1.3 Bedrock depth 10–20 ft (%) 36.6 + 2.9 Bedrock depth 20–40 ft (%) 22.2 + 3.6 Bedrock depth > 40 ft (%) 3.7 + 1.3 Mean bedrock depth (feet) 102.2 + 5.5 Soil permeability < 150 in 100 h–1 (%) 24.4 + 2.2 Soil permeability < 265 in 100 h–1 (%) 47.9 + 2.5 Soil permeability < 500 in 59.1 + 2.5 100 h–1 (%) Mean soil permeability 382.6 + 17.8 (in 100 h–1) Carbonate bedrock geology (%) 33.3 + 2.6 Metamorphic/Igneous bedrock geology (%) 25.6 + 2.5 Sandstone bedrock geology (%) 24.5 + 2.0 Shale bedrock geology (%) 14.9 + 1.6 Ground water velocity (m/day) 69.5 + 5.1 Coarse textured (%) 62.5 + 2.6 Fine texture (%) 14.1 + 1.8 Medium texture (%) 5.5 + 1.2 No texture (%) 16.7 + 2.1 Peat and muck (%) 1.3 + 0.3

X X X X – – –

X – – – – – –

– X – – – X X

– X – – X – X

X – X – – – –

– X – X – – X

– – – – – – –

X – X – X – X

X X – – – – –

–

–

–

–

–

–

–

X

–

X

–

–

X

X

–

–

X

–

X

–

–

–

–

–

–

–

–

– –

X X

X –

X –

X –

X –

X X

X –

– –

– X – – – – – X –

– – X – – – – X X

– X – – – X – X X

– X – – X – – X –

– – – – X – X – –

– X – X – – – – X

– X X – – – – – –

– – – – – – – X –

X – – X – – – – –

0.7 + 0.2

–

–

–

X

–

–

X

–

–

5.3 + 0.8 27.8 + 1.9 4.8 + 1.1 15.4 + 1.3 6.4 + 1.2 38.9 + 2.1

X – X – – –

X X – – – –

– – – – X X

– – X – – X

X – – X X –

– – – – X X

– X X – – –

X – – X – –

– – – – – X

4.1 + 0.6

–

X

X

–

X

–

–

–

X

4.6 + 0.5

X

X

–

–

–

X

–

X

–

21.9 + 0.9

–

–

–

X

–

–

–

–

X

34.7 + 1.5 14.8 + 1.0 8.6 + 0.1

X X X

– X X

X – –

X – X

– X X

X – –

– – X

– X X

– – X

58.6 + 1.8 77.2 + 4.2 16.6 + 1.0 0.7 + 0.1 12.5 + 0.8

X – – X X

X X X – –

– X – – X

X X X – X

– – – – X

– X X – X

X – X – X

X X X – –

– – – – –

Reach riparian land use CROPLAND Reach scale crop land use (%) DEVELOP Reach scale developed land use (%) MEADOW Reach scale meadow land (%) PASTURE Reach scale pasture land (%) SHRUB Reach scale shrub land (%) WETLAND % riparian wetland WOODS Reach scale wood land (%) Instream habitat CLAY % stream bottom covered with clay DETRTS % stream bottom covered with detritus GRA VRUB GRAVRUB % stream bottom covered with gravel or rubble SAND % stream bottom covered with sand SIL T SILT % stream bottom covered with silt DO Dissolved oxygen (mg/L) EMB % rocky substrate covered by silt or sand EMBCV Coefficient of variation for EMB EROSION % stream banks are erodable FL OW FLOW Stream discharge (m/s) FSCOVER % stream with fish cover

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Appendix 2 (continued) Presence/ absence Variable GRADIENT HABSC POOL RIFFLE SEDEP SEDEPCV SHADE SINUOS TDEPTH WDEPCV WDRA TIO WDRATIO WIDTH DAYMEAN DAYMAX DAYRNG

D7MEAN D7MAX D7RNG D21MEAN D21MAX JULMEAN JULMAX JULRNG

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Description Stream gradient within sampling site (m/1,000 m) Habitat score % stream reach that is pool % stream reach that is riffle Sediment depth (cm) Coefficient of variation for sediment depth % stream is shaded by canopy Sinuosity of stream reach (ratio) Thalweg depth (m) Coefficient of variation for width to depth ration Width to depth ratio (ratio) Stream width (m) Maximum daily mean temperature (oC) Highest temperature reading during the season (oC) Maximum difference between daily maximum and daily minimum temperature (oC) Maximum 7-d mean of daily mean temperature (oC) Maximum 7-d mean of daily maximum temperature (oC) Maximum 7-d mean DAYRNG (oC) Maximum 21-d mean of daily mean temperature (oC) Maximum 21-d mean of daily maximum temperature (oC) Mean of July daily mean temperature (oC) Mean of July daily maximum temperature (oC) Mean July daily temperature range (oC)

219


Abundance

Mean ± (1 SE)

H

M

L

H

M

L

H

M

L

3.1 + 0.2 59.9 + 0.7 11.0 + 1.0 13.5 + 1.0 9.3 + 0.7

– – – – –

– – – – –

X – – X X

X – – – X

X – X X X

X – – X X

X – X – –

X – – – X

X X X – X

155.8 + 6.6 36.8 + 1.6 1.3 + 0.0 0.49 + 0.0

– – X X

– X – X

– – – X

– – X –

– – X –

– – – –

X X – –

– – – –

– – X X

43.7 + 0.8 17.6 + 0.7 8.7 + 0.5

– – X

– – X

– X X

– – X

X X X

– X –

X – X

– – X

X X X

23.0 + 0.2

X

X

–

X

X

X

X

X

X

26.0 + 0.2

X

–

X

–

–

X

–

–

X

8.0 + 0.2

–

X

X

–

X

X

–

X

–

22.1 + 0.2

X

–

X

–

–

X

–

–

X

24.7 + 0.2

–

–

X

–

–

X

–

–

–

8.0 + 0.2

–

–

X

X

X

X

–

–

–

21.1 + 0.2

–

–

X

X

X

X

–

–

X

23.6 + 0.2

–

–

X

X

–

X

X

–

–

19.7 + 0.2

X

X

X

X

X

X

–

–

–

22.0 + 0.2

–

–

–

X

X

–

X

X

–

4.3 + 0.1

X

–

X

X

X

X

–

–

–

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