Urban Ecosyst DOI 10.1007/s11252-015-0464-6
Influence of a forest preserve on aquatic macroinvertebrates, habitat quality, and water quality in an urban stream Patrick M. Wilkins 1 & Yong Cao 1 & Edward J. Heske 1 & Jeffrey M. Levengood 1
# Springer Science+Business Media New York 2015
Abstract The use of nature areas as an effective tool for conservation of streams and their biota is a relatively unexplored option in urban stream management. Benthic macroinvertebrates and water quality in an urban stream, Poplar Creek, were monitored along a continuum within and downstream of a forest preserve in the western Chicago metropolitan area, USA. Taxa richness and a benthic macroinvertebrate index of biological integrity (MIBI) increased as the stream progressed within the preserve, but percentage of Ephemeroptera, Trichoptera, and Plecoptera (%EPT) showed no improvement. Reductions in the amount of silt substrates and increases in gravel-dominated substrate were evident at sites within the preserve. There were no improvements in water-quality measures tested although trends may have been obscured by precipitation events between sampling occasions. The benefits in stream quality attained within the forest preserve extended beyond the downstream border of the preserve and were not diminished by the presence of a railway, which bisected the stream. The nature preserve provided an opportunity for increased macroinvertebrate biodiversity, likely through local reductions in impervious surfaces and improvements in stream substrate, despite lack of evidence that it improved water quality. Keywords Macroinvertebrates . Biodiversity conservation . Forest preserve . Urban stream . Water quality . Landuse
Introduction Effects of watershed land cover and local habitat on stream ecosystems have long been a focus of stream ecology (Hynes 1975; Vannote et al. 1980; Weins 1989; Ward 1998). Transition
* Yong Cao
[email protected] 1
Illinois Natural History Survey, Prairie Research Institute, University of Illinois at UrbanaChampaign, 1816 S Oak Street, Champaign, IL 61820, USA
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from natural to human-dominated landscapes typically degrades stream ecosystems (Paul and Meyer 2001; Allan 2004). Streams draining urban areas show a flashier hydrograph, elevated nutrient and contaminant levels, homogenized channel morphology, and reduced biodiversity with increased dominance of pollutant-tolerant taxa (Booth and Jackson 1997; Wang et al. 1997; Meyer et al. 2005), a pattern of degradation referred to as the Burban stream syndrome^ (Walsh et al. 2004). For example, changes in the hydrological regime occur as a result of more efficient transport of runoff due to increases in impervious surfaces and from piped stormwater drainage systems (Dunne and Leopold 1978). Stormwater management, bank stabilization, channel reconfiguration, and riparian buffer replanting are common strategies for improving the physical and ecological conditions of degraded urban streams (Bernhardt and Palmer 2007). However, dense human infrastructure can limit the spatial extent of restoration options. Macroinvertebrate assemblages reflect a multitude of physical, chemical, and biological stream features allowing them to be excellent indicators of stream health (Allan 2004). Sensitive species were absent or less abundant in streams draining urban areas, yet the specific environmental variables impacting invertebrates varied widely among metropolitan areas (Cuffney et al. 2007). King et al. (2000) found sharp declines in abundance of numerous taxa in watersheds with impervious surface cover as low as 2 %, suggesting biological responses to urbanization may occur at low levels of perturbation. Moore and Palmer (2005) also found that invertebrate diversity of headwater streams in suburban Maryland decreased with greater impervious surface cover in the watershed, but macroinvertebrate diversity was positively correlated with the amount of intact riparian vegetation around urban streams. Sudduth and Meyer (2006) found that macroinvertebrate richness was strongly correlated with the percent of stream banks covered with roots or wood in urban and urban-restored streams. However, meta-analysis of 78 independent stream or river restoration projects found that only two showed statistically significant increases in biodiversity after restoring habitat heterogeneity (Palmer et al. 2010). Walsh et al. (2004) reasoned that restoration projects completed at the reach scale may not consistently improve aquatic biodiversity because their beneficial impacts are not maintained unless effective measures are taken at larger spatial scales. As urbanization and associated landscape fragmentation continue, the use of natural areas to help preserve stream biodiversity is of increasing interest. Studies in New Zealand evaluated shifts in macroinvertebrate composition and diversity in streams in forest fragments within agriculturally dominated landscapes (Storey and Cowley 1997; Scarsbrook and Halliday 1999; Harding et al. 2006). They found that water quality was variable, and did not recover quickly to levels in Bcontrol^ forest streams even as far as 350 m into forest fragments. Contrarily, diversity and density of benthic communities recovered to levels in control streams without corresponding improvements in water quality. Houghton et al. (2011) monitored benthic macroinvertebrates and adult caddisflies along an agricultural stream upstream, within, and downstream of a small forested preserve in Michigan. They found that the diversity of adult caddisflies was significantly higher within the preserve, with a three-fold increase in species diversity despite no clear improvements in water quality. Few studies, if any, have examined the effects of terrestrial nature preserves on streams in an urbanized landscape. Evaluating the role of small, isolated natural habitats such as forest preserves in urban stream systems is particularly important because such preserves offer the most common options for conservation in urbanized watersheds. A terrestrial preserve can provide allochthonous inputs, filter pollutants, reduce flow variation, and increase morphological stability, thus retaining greater aquatic biodiversity (Allan 2004). Further, managers of urban nature preserves need to know the spatial scales at which management is most effective.
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If stream biodiversity is affected primarily by factors at the watershed scale, management options may be limited. If stream habitat quality and local riparian vegetation strongly affect stream biodiversity, then local management can be an effective conservation tool. Finally, the effect of water quality on stream biodiversity relative to effects of habitat and landscape-level factors should be evaluated in urban streams. A longitudinal study of changes in stream biodiversity as a stream moves through a preserve can indicate distances over which positive changes occur, and thus thresholds in preserve size for effective conservation. In this study, we examined changes in macroinvertebrate assemblages, habitat quality, and water quality along an urban stream as it traversed a forest preserve in an otherwise urban landscape. We modeled relationships among three macroinvertebrate metrics and environmental variables in the study stream to evaluate the relative effects of water quality, habitat quality, and land cover on macroinvertebrate assemblages. Our results demonstrate the potential value of urban forest preserves for aquatic conservation.
Methods Study area and design The Chicago Wilderness is a regional nature preserve system with >910 km2 of protected natural areas that includes state parks, federal reserves, and county preserves in seven counties in and around Chicago, Illinois, USA. Many preserves are along or around rivers or creeks, and we selected one of these (Poplar Creek Forest Preserve in Cook County) for the present study. Poplar Creek is a tributary of the Fox River, which flows from southern Wisconsin through northeastern Illinois before joining the Illinois River. The watershed for Poplar Creek is primarily urban, although the creek travels for 9.4 km through an established terrestrial preserve (Poplar Creek Forest Preserve, hereafter PCFP). While called a Bforest preserve^, PCFP is a 1700-ha complex of prairie, old field, wetlands, and oak woodland, with white oak (Quercus alba) and bur oak (Quercus macrocarpa) being the dominant mature woody vegetation. An ideal design for capturing the potential recovery gradient of a stream would be to select multiple sampling locations upstream, within, and downstream of an established preserve. However, constraints on access to sampling locations in urban areas, particularly difficulties with access to private property and stream modifications that made sampling difficult, restricted our choices. As a result, we selected seven sampling locations on Poplar Creek with four locations regularly spaced along the stream within the preserve and three locations downstream of the preserve (Fig. 1). The sampling locations are more or less equally spaced to quantify changes in stream conditions throughout the preserve. Poplar Creek enters the preserve on the eastern boundary and traverses the length of the preserve before approaching, travelling parallel to, and then passing under railroad tracks on the western boundary.
Macroinvertebrate sampling We used a D-net (23×43 cm) to collect a 20-jab sample from each location, following the Illinois Environmental Protection Agency standard field protocol (IEPA 2004) on June 6, 2010. Sampling for Year 2011 was postponed to June 14 because of high stream flow. The 20 dips were allocated proportionally to major types of habitats (e.g., pools, riffles, and runs)
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Fig. 1 Poplar Creek watershed, the boundary of the forest preserve, and the seven sampling locations
available at each sampling location. Samples were fixed with 90 % ethanol (approximately 70 % after mixing) and shipped to a taxonomy lab. Samples collected in 2010 were processed by Macro-ID Services (Elmhurst, Illinois), which has provided taxonomic services to IEPA. We were required to employ a different lab, Eco-analysts, Inc. (Moscow, Idaho), to process samples collected in 2011. This lab also has provided services to the United States Environmental Protection Agency and many state environmental agencies. Both labs conducted standard random picking and identification of >300 individuals per sample or complete sorting if 80 %) were identified to the genus level, those individuals in the sample that could be identified only to the family level were discarded and OTUs based on the genera were established. In contrast, if most specimens were only identified to the family level, one OTU was established for that family and all individuals were kept in the sample. This standardization maximized the amount of information retained in the data. We standardized all taxon names in the two sets of macroinvertebrate data to make certain data from the two labs were comparable. We further modified the list of
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OTUs by pooling chironomid taxa (usually genera) to the Btribe^ level (a taxonomic level between genus and family) to further improve the comparability of samples processed by the two taxonomic labs. This modification slightly reduced the estimation of taxa richness per 300individual sample, but affected calculation of the macroinvertebrate-based Index of Biological Integrity (MIBI; IEPA 2004) to a much lesser extent and did not affect calculation of the percent of the sample comprised of Ephemeroptera, Plecoptera, and Tricoptera (%EPT) individuals.
Habitat quality and substrate sampling We assessed habitat quality for all sampling locations in 2011 based on a Qualitative Habitat Evaluation Index (QHEI) (Rankin 1989). The QHEI is composed of six metrics that take into account six major habitat variables, and provides a standard, qualitative evaluation of the physical characteristics of a given stream reach. We defined the evaluation area for each site as 20 times the bankfull width of the stream. The six metrics included substrate, in-stream cover, channel morphology, riparian zone and bank erosion quality, pool and rifle quality, and stream gradient. Gradient was measured as the drop in elevation on each site through the sampling area obtained from a GIS database developed by Great Lakes Regional Aquatic Gap Analysis Project (Brenden et al. 2006). Qualitative assessments for the other metrics were made following the Ohio EPA QHEI data sheet (Rankin 1989) to create a score for that variable. Scores are weighted based on the importance of each variable to stream health, then summed for an overall index of habitat quality. Maximum score for the QHEI is 100. The higher the score value is the better habitat quality. In addition to the qualitative assessments provided by the QHEI, we conducted detailed substrate surveys at each site based on the IEPA wadeable streams transect approach (IEPA 2003). For substrate surveys, we defined each sampling area as 10 times the channel width of the stream. We then spaced 11 transects evenly throughout the sampling area Along each transect, 10 physical grabs of the stream substrate and the depth at the given grab were recorded. Dominant substrate type was recorded as silt/mud (particles < 0.062 mm diameter), sand (0.062–2 mm), fine gravel (2.032–7.62 mm), medium gravel (7.63–15.24 mm), coarse gravel (15.25–63.5 mm), small cobble (6.36–12.7 cm), large cobble (12.8–25.4 cm), or boulder (>25.4 cm). The habitat transects allowed calculation of mean depth and width and determination of the dominant substrate type for each sampling location.
Water quality sampling We measured water quality variables monthly between June and September in both 2010 and 2011. At each sampling location, we measured five water-quality variables on site using a Quanta Water Quality Monitoring System (Hach Environmental Inc., Loveland, Colorado), including dissolved oxygen (DO; mg L-1), specific conductivity (SpC; mS cm-1), pH, turbidity (Turb; NTUs), and salinity (Sal; PSS). Water chemistry samples were collected following a standard protocol for biological water quality assessments (Hawkins et al. 2003). Two samples were collected on each sampling date. One 60-ml polyethylene bottle was filtered using a 47-mm, 0.45-micron nitrocellulose filter, stored at 4 °C, and analyzed for PO4 and NO3. A second 250-ml polyethylene bottle was left unfiltered, preserved with 0.02 % sulfuric acid, stored at 4 ° C, and analyzed for total Kjehldahl nitrogen (TKN) and total phosphorus (TP). All analyses were performed following methods approved by the USEPA (APHA 1998) at the IL State Water Survey (Champaign, Illinois).
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Land-cover data compiling Landuse can strongly affect flow, water quality, and in turn stream macroinvertebrates (Allan 2004). We extracted urban, grassland, and forest land cover variables from the GIS database of the Illinois Department of Natural Resources (Brenden et al. 2006; Cao et al. 2015). We quantified urban influence as the percent of land area covered by the urban category (%Urban), which included all impervious surfaces. We pooled grassland and forest cover to create a single vegetative cover variable (%Veg). We then summarized land cover by these two categories at each sampling location at four spatial scales. First, we defined reach and total watershed scales for each site. A stream reach was defined as a confluence-to-confluence segment (Fig. 2), and analyses at this scale were intended to evaluate effects of local landscapes. Dams, ponds, or abrupt change points for slope or geology also can act as the boundary of a reach. Both habitats and fish samples were more similar within a reach than among reaches in other studies, and a reach is thus an appropriate unit of stream network (Warrner et al. 2010; Wang et al. 2011). The total watershed (WT) was defined as the total upstream land area draining into the most downstream point of the reach sampled, and analyses at this scale were intended to evaluate effects of inputs from the entire drainage area on a sampling location. Second, within each reach and total watershed, we quantified land cover variables at riparian buffer and local watershed scales. A riparian buffer (R) was defined as the terrestrial landscape within 30 m of a stream bank, and this scale was intended to
Fig. 2 Representation of the land cover metrics used in this study. (a) R: riparian buffer, reach scale; (b) RT: riparian buffer, total watershed scale; (c) W: local watershed, reach scale; (d) WT: total watershed (after Brenden et al. 2006)
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evaluate the effects of the land cover immediately adjacent to a stream. A local watershed (W) was defined as the area within a reach that directly drains into stream, and was intended to evaluate effects of inputs from local drainage on a sampling location. Thus, for each site (using %Veg as an example), R%Veg represents the percent cover by vegetation in the buffer zone in a reach, RT%Veg represents the percent cover by vegetation in the buffer zone in the total upstream watershed, W%Veg represents the percent cover by vegetation in the local watershed, and WT%Veg represents the percent cover by vegetation in the total upstream watershed. The first four upstream locations in Poplar Creek occurred in separate local watersheds, but the last three sampling locations occurred in the same local watershed. Poplar Creek watershed was dominated by >48 % urban land, with approximately 56 % urban land for the first two sampling reaches (P1 and P2) (Table 1). Urban lands typically include 20–86 % of impermeable surface (USDA 1986), which strongly affect streams (Wang et al. 2001; Walsh et al. 2004). The exactly % of impermeable surface for the Poplar Creek watershed was not available, but was presumably much higher than 2 %, a threshold for significant ecological degradation reported previously (King et al. 2000). As a result, our sampling sites most likely captured part of the urbanization gradient. Nevertheless, land-use did vary along the stream segment sampled, particularly at the local scales. WT%Urban slightly decreased and WT%Veg slightly increased as the stream travelled through the preserve. In comparison, landuse in the local watershed differed more among sampling sites. W%Urban quickly decreased after the stream enters the preserve, but significantly increased for the last three sites. W%Veg showed an opposite trend, with P1 through P4 having high values and the last three sites outside of the preserve (NP5-NP7) having low values. Total riparian cover varied little among the sampling locations, with a slight increase in RT%Veg from P1 to P4 while RT%Urban showed an opposite trend. Both RT%Veg and RT%Urban for sites downstream of the preserve were intermediate high. R%Veg was much higher and R%Urban lower at sites P1 through P4 than at sites outside the preserve.
Longitudinal changes in macroinvertebrates, habitat quality, and water quality We first examined changes in the composition of macroinvertebrate assemblages among sampling locations using non-metric multidimensional scaling (NMDS) based on a BrayCurtis similarity index and log(x+1) transformed data (Clarke and Gorely 2006). NMDS is an indirect gradient analysis that shows relationships of samples based on the rank-ordered similarity, i.e., more similar samples are closer in the ordination plot. Table 1 Summary of water-quality data collected from seven sampling sites during 2010-2011 Sampling location pH
Sal. (PSS)
Turb. (NTUs)
DO. (mg/L)
SpC. (mS/cm)
PO4 (μg/L)
TP (μg/L)
TKN (μg/L)
P1
7.9 (0.23)
0.48 (0.06)
14.4 (7.7)
7.7 (1.2)
0.95 (0.12)
20.4 (12.7)
95.0 (24.6) 935.6 (180.5)
P2
7.8 (0.33)
0.51 (0.04)
22.9 (7.6)
7.5 (0.9)
1.03 (0.09)
11.9 (6.9)
79.4 (35.6) 1004.4 (139.9)
P3
7.9 (0.28)
0.50 (0.08)
15.4 (2.5)
8.0 (0.8)
1.02 (0.14)
11.0 (4.9)
76.7 (25.3) 1022.8 (152.7)
P4
7.8 (0.32)
0.51 (0.07)
15.6 (9.2)
7.8 (0.8)
1.03 (0.14)
15.6 (11.0)
67.0 (31.2) 907.1 (141.9)
NP5
7.7 (0.21)
0.51 (0.06)
18.5 (7.2)
7.7 (0.7)
1.05 (0.11)
16.6 (9.7)
70.4 (31.2) 825.3 (277.9)
NP6
7.8 (0.21)
0.47 (0.19)
20.2 (7.5)
7.4 (1.0)
1.04 (0.15)
17.9 (8.7)
75.2 (33.9) 922.3 (409.7)
NP7
8.0 (0.25)
0.51 (0.10)
14.0 (7.8)
8.1 (0.8)
1.03 (0.17)
17.2 (14.9)
51.3 (15.9) 809.0 (207.0)
Values for selected physiochemical parameters are averages followed by coefficient of variation (%)
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Because water quality variables often co-vary, data on these variables were summarized with principal components analysis (PCA). PCA scores in the first two dimensions, as well as QHEI, were then related to the position of the sampling location along the stream to examine whether water quality and habitat quality changed in a predictable manner as the stream travelled through each forest preserve.
Modeling changes in macroinvertebrate metrics We used generalized linear models (GLMs) to evaluate the relative importance of different types of environmental variables for taxa richness, MIBI score, and %EPT taxa. First, sets of competing models were developed to identify the best models representing each of three competing but not mutually exclusive hypotheses: macroinvertebrate assemblages are affected by 1) water quality, 2) habitat quality, and 3) land cover. Second, the top-ranked models representing each hypothesis and all additive combinations of those models plus a model representing distance downstream were then compared to evaluate which types of environmental variables (i.e., water quality, habitat quality, land cover, distance downstream) were the best predictors of each macroinvertebrate metric. Distance downstream was defined in kilometers from the most upstream sampling site. For water-quality-based modeling, we used the first two axes of the PCA on the collected water quality variables, singly and in an additive model, as competing water quality models. Before constructing models for habitat quality and land cover, colinearity among variables was assessed by constructing a correlation matrix; variables that were correlated at r>0.6 were not included in the same model. Competing models for habitat quality included the QHEI, plus the quantitative measurements of mean wetted width, mean channel width, depth, %Sand (representing finer substrates), and %Coarse (sum of %Gravel and %Cobble, representing coarser substrates) alone and in all possible additive combinations. Competing models for land cover included only %Veg variables (i.e., R%Veg, W%Veg, RT%Veg, WT%Veg) because %Veg and %Urban variables were inversely related at all scales. Again, models included each variable alone and in all possible additive combinations. A Poisson family distribution was used for taxa richness, which are count data (Guisan et al. 2002). Percent EPT taxa were arcsine transformed (√[y/100]) prior to analysis. Individual model performance was evaluated using the coefficient of variation (R2), the parameter estimate (β) of the variable of interest, and the 95 % confidence interval of the parameter estimates. A model was considered significant if the confidence interval of the associated parameter estimate did not include zero. We used an information criterion approach to model selection (Burnham and Anderson 2002) to rank the most important variables affecting each macroinvertebrate metric for each hypothesis (i.e., water quality, habitat quality, land cover). Models were ranked via the Akaike Information Criterion for small sample size (AICc). The top-ranked models for each type of environmental variable were then compared in a balanced design (i.e., each model separately and in all possible additive combinations) and Akaike model weights (wi) were summed across all possible models to assess the relative importance of the four environmental variable categories (water quality, habitat quality, land cover, distance downstream) included in the models. If the null model, which contained only the intercept, was the top-ranked model for a given environmental variable type, we used the next-highest ranked model for the comparison of types of environmental variables. All data analyses were conducted within the R statistical software (R Development Core Team 2011). The package ‘vegan’ was used to perform the multivariate analyses (Oksanen
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et al. 2012). The package ‘MuMIn’ was used to conduct model selection procedures for competing generalized linear models (Barton 2012).
Results Macroinvertebrate assemblages and metrics A total of 31 taxa based on revised OTUs were collected during the study; nine were members of Ephemeroptera, Trichoptera, or Plecoptera. Gradients in taxonomic composition among samples are shown via NMDS (Fig. 3). The best representation of the original data was found in two dimensions, with a stress value of 0.14 and a good relationship between original distance matrix and the two ordination axes (Axis 1 R2 = 0.55, Axis 2 R2 =0.56). Axis 1 uncovered differences in taxonomic composition between 2010 and 2011 for samples from the same locations. Axis 2 revealed a change in taxonomic composition along the longitudinal gradient of the sampling locations in 2010; the same gradient was present but less pronounced in 2011, likely due to effects of extremely high flow prior to the sampling. Taxa richness increased downstream, with the highest richness occurring at NP5 in both years (Fig. 4a). MIBI scores ranged from 20.5 to 54.4, a significant improvement in stream biological conditions. Location P1 had the lowest scores in both years. MIBI scores tended to increase with position downstream in both 2010 and 2011; NP7 had the highest score. The railroad tracks between P4 and NP5 did not interrupt this recovery trend, indicating it did not have a negative effect on macroinvertebrate communities (Fig. 4b). Percent EPT taxa ranged from 3.4 to 36.8. Location P4 had the highest percentage of EPT taxa in 2010 and NP7 had the highest percentage in 2011 (Fig. 4c).
Fig. 3 Ordination plot of non-metric multidimensional scaling (NMDS) results. Sampling locations are numbered starting with the furthest upstream. P – within-preserve locations; NP – outside-preserve locations. Dark gray – 2010 data; light gray – 2011 data (NP7 sampled only in 2011)
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Fig. 4 Macroinvertebrate metrics from seven sampling locations along poplar Creek in 2010 and 2011. Locations are ordered from the furthest upstream (P1) to furthest downstream (NP7, sampled only in 2011). See Fig. 1 for map of locations
Water quality and habitats All sampling locations had pH levels above 7 (Table 2). Salinity, turbidity, dissolved oxygen, and conductivity levels varied little among locations. Nutrient concentrations varied among sampling occasions as indicated by the large CVs in Table 4, but tended to decrease downstream. Principle components analysis yielded 2 axes (PCA1 and PCA2) that cumulatively explained 70 % of the total variance in the selected water quality parameters (Table 3). PCA1 explained 42 % of the total variance and had positive loadings from total phosphorus and TKN, whereas Sal and DO were negatively loaded. PCA2 explained 28 % of the total variance, with pH loading positively and turbidity and specific conductivity loading
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Table 2 Weights of individual water-quality variables on the first two axes of principal component analysis (PCA)
PCA1
PCA2
%Variance
42
28
pH Sal
−0.38 −0.81
0.82 −0.45 −0.75
Turb
0.42
DO
−0.83
0.38
SpC
−0.52
−0.75
PO4
0.40
0.33
TP
0.86
0.05
TKN
0.75
0.02
negatively. PCA1 (β=−0.082, CI=−0.21 to 0.049) and PCA2 (β=−0.075, CI=−0.21 to 0.058) were not related to distance downstream. Mean stream width and depth increased slightly downstream (Table 4). Substrate composition was dominated by silt at P1 but shifted to a gravel-dominated composition at P2 through NP6. Cobble was greater in the 3 locations downstream of the preserve, and NP7 was dominated by cobble. QHEI scores increased greatly from P1 to P2, then remained high at all other sites except NP6. Although QHEI increased with distance downstream within PCFP and continued to increase through the first sampling location downstream of the preserve, the overall relationship was not statistically significant (parameter estimate, β=1.15; 95 % confidence interval, CI=−0.56 to 2.87).
Modeling macroinvertebrate metrics based on water quality, habitat, and land-use Water-quality-based models The null model was the top-ranked model for all analyses based on water quality indicating that water quality was a poor predictor of macroinvertebrate metrics in our study. Models based on PCA1 of water-quality variables performed better for all three macroinvertebrate metrics (βrich =−0.10, CI=−0.20 to 0.02, R2 =0.18; βMIBI =−3.7, CI= −9.07 to 1.69, R2 =0.14; β%EPT =−0.03, CI=−0.11 to 0.04, R2 =0.07) than models based on PCA2, but were only competitive (ΔAICc
