WETLAND AND WATER RESOURCE MODELING AND ASSESSMENT: A Watershed Perspective - Chapter 14 pptx

Rapid assessment tools can be used as a warning sign to give a quick idea of wetland condition and determine sites in need of further assessment or immediate protection.. In particular,

Assessment Techniques for Establishing

Wetland Condition on

a Watershed Scale

Vanessa L Lougheed, Christian A Parker,

and R Jan Stevenson

14.1 INTRODUCTION

Recently, the U.S National Research Council (2001) recommended utilizing a watershed perspective together with science-based, rapid assessment procedures to track wetland mitigation and restoration Rapid assessment tools can be used as a warning sign to give a quick idea of wetland condition and determine sites in need of further assessment or immediate protection Many U.S states have or are developing three-tiered assessment procedures that include an initial landscape-scale assess-ment using aerial imagery (tier 1), followed by a rapid condition assessassess-ment (tier 2), and a more intensive monitoring program (tier 3) (e.g., Miller and Gunsalus 1999, Mack 2001, Fennessy et al 2004)

Wetlands can be significantly impacted by a variety of physical, chemical, and biological factors, and although a single environmental factor can sometimes be implicated as the primary stressor to a wetland ecosystem (King and Richardson 2003), it is more likely that a combination of factors result in wetland degradation on

a landscape scale (Danielson 2001)

Furthermore, spatial and temporal variability in chemical stressor levels can make it difficult to diagnose one specific nutrient causing impairment, especially for sites sampled just once in landscape-scale assessments In such cases, multistressor axes can be used to ensure assessments reflect a greater number of stressors (e.g., Mack 2001, Lougheed et al 2001) In particular, one encounters a variety of wetland classes in a single watershed (e.g., lacustrine, riverine, and isolated wetlands) and these different classes may respond differently to a variety of stressors (Fennessy et

al 2004) Multistressor axes may therefore have a greater utility for a suite of wet-lands in a wet-landscape setting than does any one individual measure

Existing rapid assessment methods generally combine various measures of hydrology, water quality, soils, landscape setting, and vegetation (Fennessy et al

2004) Fennessy et al (2004) reviewed 16 different rapid assessment methods that met 4 criteria they deemed to be important for successful rapid assessment They concluded that the best methods should:

1 describe the condition along a single continuum ranging from least to most impacted

2 provide an accurate assessment of conditions in a relatively short time period (e.g., 1 day total for both field and lab components)

3 include an onsite assessment

4 be capable of onsite verification using more comprehensive ecological assessment data (tier 3)

Using these guidelines, the goal of this study was to develop a suite of rapid assessment techniques and examine their utility in evaluating wetland condition in a single large watershed in Michigan In particular:

We compare a field-based estimate of riparian land use to actual land use val-ues determined from GIS (geographic information system) maps in a 1-km buffer around each wetland

We create a multimetric wetland disturbance axis (WDA) that incorporates rapid measures of hydrology, water quality, and land use

As a rapid assessment of biological condition, we compare an estimate of epi-phytic algal thickness against epiepi-phytic chlorophyll biomass values and percent cover of epiphytic macroalgae

To verify the utility of the WDA in reflecting biological condition, we determine whether plant community composition responds along the WDA

14.2 METHODS

on its eastern shore and is dominated by forested land in the upstream regions and agricultural and small urban areas (e.g., Muskegon, population 40,000) in the down-stream region We visited 85 wetlands in the Muskegon River watershed (MRW in Michigan) during the summers of 2001 through 2003 This included 35 isolated depressions, 25 lacustrine and 25 riverine wetlands Fifty-two (52) sites were selected randomly based on a numbered grid overlaid on GIS-based wetland maps, while the remaining 33 sites were purposely selected to represent a gradient of disturbance Approximately half (18) of the randomly selected wetlands were outside the MRW and in the upstream reaches of immediately adjacent watersheds (e.g., Chippewa River, Grand River, Pere Marquette River)

For determination of water chemistry, water was collected from an open water area in each wetland in 250-mL, acid-washed bottles Total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx), ammonia (NH3), silica (Si), soluble reac-tive phosphorus (SRP), and chloride (Cl) were determined using standard methods (American Public Health Association [APHA] 1998) on a Skalar auto-analyzer Conductivity was measured in the field using a YSI 556 multiprobe Sediment was collected from 3 random locations in the wetland using a 5-cm corer; the 3 samples

were combined and frozen until analysis C:N was determined using a Perkin-Elmer

2400 Series II CHN analyzer, while percent organic matter was determined follow-ing loss-on-ignition at 500°C We did not measure contaminant levels in this study; however, local public health departments had identified several areas with contami-nated sediments at a level of concern and these were noted

We constructed a multimetric stressor axis designed to integrate and give equal weight to measurements in 3 primary stressor categories: land use, hydrological modification, and water quality Unlike many other rapid assessment methods (see

Fennessy et al 2004), we did not include plant habitat variables, as we felt that this would create circular relationships with our plant community metrics This wetland disturbance axis (WDA) included 3 metrics indicative of land use and land cover change (riparian land use, buffer width, distance to nearest wetland), 2 metrics indicative of hydrology (hydrological modification, water source), as well

as 2 water quality metrics (conductivity, contaminants) (Table 14.1) Some of these metrics were loosely based on those used in the Ohio Rapid Assessment Method (ORAM) (Mack 2001), while new metrics were also included to reflect different data collection methods in this study We assigned scores to some of the metrics

by placing the “answers” to assessment questions into different categories and then assigning a score by category (Fennessy et al 2004) For example, hydrological modification was categorized using questions such as: Are there roads along the wetland edge? Is there evidence of dams, dredging, or ditching? Then, each hydro-logical stressor answer was assigned a score, which was summed to achieve a metric indicative of all hydrological modifications Most metrics were scaled using a 1-3-5 scaling system, where a value of 0 or 1 was given to the least impacted wetlands and a value of 5 was given to the most degraded sites For example, average buffer width around wetlands was categorized in the field in 4 categories (0 = >50 m; 1 = 25–50 m; 3 = 10–25 m; 5 = <10 m) Similarly, water source was characterized as year round (0), intermittent (3), or none visible (5), and contaminants were classi-fied as none (0), low levels (3), or level of concern (5) In the field, riparian land use was categorized as either agricultural, fallow pasture, urban, suburban, parkland,

or forested on a scale from 0 to 4 (sum total of all categories = 4) For inclusion in the WDA, the proportion (out of 4) for each of these land use categories was mul-tiplied by 5 (for high-impact land categories such as urban and agricultural land),

by 3 (for moderate land use impacts such as fallow pasture, park, and suburban residential), whereas forested land was multiplied by zero Two metrics (nearest neighbor, conductivity) were scaled based on the frequency distribution of values observed for all wetlands in this study One of these, conductivity, was scaled from

0 to 10 in order to increase the weight of this metric in the overall WDA calcula-tion Finally, all individual scores from each metric were added together Although the maximum WDA in this study was 75, the WDA was scaled from 0 to 100, to allow its use in more degraded watersheds in the region Low value of the WDA indicate higher-quality wetlands

Land use and distance between wetlands were determined in ESRI ArcMap (ver-sion 9.0) using land use maps current to 1998 Using these data, we determined lin-ear distance to the nlin-earest wetland (nlin-earest neighbor), as well as riparian land use in

a 1-km buffer around each wetland Nearest neighbor is the only metric included in

TABLE 14.1

Description of metrics used in the wetland disturbance axis (WDA).

Sum of all metrics is 45, but is scaled out of 100 to get final WDA.

Score and range of values MAX

Land use and habitat fragmentation (MAX: 15)

Average buffer width

(score 1 value only)

0: >50 m 1: 25–50 m 3: 10–25 m 5: <10 m

5

Surrounding land use

(calculate and add)

0: multiply 0x proportion forested land 3: multiply 3x sum of proportion park, fallow pasture, and suburban residential land

5: multiply 5x sum of proportion urban, industrial, and agricultural land

5

Nearest neighbor a

(score 1 value only)

0: <0.13 km 1: 0.13–0.33 km 2: 0.33–0.66 km 3: 0.66–0.92 km 4: 0.92–1.64 km 5: >1.64 km

5

Hydrology (MAX: 15)

Water source

(score 1 value only)

0: year-round inputs (river, lake, groundwater) 3: seasonally intermittent

5: no visible inputs

5

Hydrological modification

(add all visible modifications

together to maximum of 10)

0: none 1: road along less than 1/4 of wetland edge 1: human dam (pre-1980)

3: human dams (post-1980) or natural dams (beaver, clogged culvert)

3: road along >1/4 of wetland edge 5: high impact (ditching, dredging, culverts)

10

Water quality (MAX: 15)

Conductivity a

(score 1 value only)

0: <85 μS/cm 2: 85–159 μS/cm 4: 159–289 μS/cm 6: 289–386 μS/cm 8: 386–498 μS/cm 10: >498 μS/cm

10

Contaminants

(score 1 value only)

0: None 3: Present at low levels 5: Level of concern

5

a Ranges included in metric based on frequency distribution.

the WDA that was not estimated in the field; however, it may be possible to estimate this variable more rapidly using aerial photos or topographic maps if GIS is not available

Macrophyte and epiphytic algae communities were surveyed using a stratified random design We established 3 regularly spaced parallel transects, perpendicular

to the shore, and randomly placed 1-m2 rectangular quadrats along each transect according to a random numbers table In each quadrat, we recorded relative cover

of each plant species using a modified Braun-Blanquet scale, estimated the percent cover of filamentous macroalgae, and classified epiphyte thickness on a semiquan-titative scale (rapid epiphyton survey [RES]: 0 = no growth; 1 = thin film, tracks can be drawn with your fingernail; 2 = 1 to 5 mm; 3 = >5 mm) These were visual estimations of epiphytic thickness, and did not represent precise measurements Epi-phytic algae were collected from cuttings of the dominant vegetation type in each wetland selected from random locations along each transect; we avoided collecting plants with macroalgal growth Algae was removed from the plants with a com-bination of gentle rubbing from emergent stems and shaking of submerged plant stems in distilled water Cleaned plants were placed in zipper bags and refrigerated

so that surface area could be determined using image analysis software (ImageJ, NIH) Subsamples of the resulting algal suspension were frozen and analyzed for chlorophyll-a within 2 months of collection Chlorophyll-a was extracted with 90% ethanol for 24 hours in the dark at 4°C; samples were then sonicated for 15 minutes and chlorophyll fluorescence determined on a Turner Designs fluorometer Chloro-phyll concentration was expressed per surface area of plant Results presented are not corrected for phaeophytin because our RES could not distinguish between live and dead epiphytes

We selected the Floristic Quality Assessment Index (FQAI) for Michigan (Her-man et al 2001) and its related coefficient of conservatism (CofC) to describe the wetland condition represented by the plant communities The FQAI indicates the extent to which the community is dominated by sensitive wetland plants The CofC

is the sensitivity value given to each plant and we used the average CofC calculated for all plant species in each wetland To explain structure in the biological communi-ties of the wetlands, independent of any preconceived environmental preferences or gradients, we used nonmetric multidimensional scaling (NMDS) NMDS analysis identifies axes that describe biologically meaningful, multivariate gradients in the community data (McCune and Grace 2002) We selected the Bray-Curtis distance measure and used the first NMDS axis identified by PC-ORD (version 4.10) as an indicator of plant community structure The NMDS, FQAI, and CofC were deter-mined from previous analyses (Lougheed et al 2007) to respond strongly to envi-ronmental gradients in the MRW

Relationships between the rapid assessment variables and more detailed mea-surements of land use and epiphytic chlorophyll-a were studied in the large dataset

of 85 wetlands, regardless of wetland class In studying the responses of the plant communities, we divided the data into wetland classes (depressions, lacustrine, riv-erine) because biological communities in differing classes may respond uniquely to differing stressors

14.3 RESULTS

Actual land use in a 1-km buffer around each wetland was well represented by the estimated land use categories (Figure 14.1); however, our estimates of land use more accurately reflected urban and agricultural land use For both these land use types,

we were able to distinguish among 3 separate categories and the 0 category had an average of 4% developed land in both cases Our measurements of forested land dif-fered between the lowest (0 and 1) and highest categories (3 and 4); however, the 0

Agriculture Category 0

0.0 0.2 0.4 0.6 0.8

0.0 0.2 0.4 0.6 0.8

Forest Category

Urban Category

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

A

A

AB

AB

B

B

B

C C

C

BC

FIGURE 14.1 Comparison of GIS-calculated percent land use values determined in a 1-km buffer around each wetland in 4 to 5 land use categories estimated in the field Letters indi-cate statistical similarities (Tukey multiple comparisons; p < 0.05)

category had an average of 26% forested land, which was not significantly different from category 1 at 33% forested land

We took the average rapid epiphyton survey (RES) values from each wetland and rounded the value up to the nearest 0.5 Epiphytic chlorophyll-a was signifi-cantly different between sites with a “thin film” (category 1) of algae, relative to sites with approximately 1 to 5 mm of growth (category 2) (Figure 14.2) There was

no significant increase in category 3, likely because it included sites with increased macroalgal cover, which we excluded from our epiphyte samples This is supported

by comparisons of macroalgal cover, expressed as relative dominance of macroalgal cover (per m2) relative to total plant species cover (per m2), which was significantly higher in sites with an average RES value of 3

We used principal components analysis to determine which rapid assessment metrics explained the greatest amount of variation in the dataset The first 3 PCA axes together accounted for 68% of the variation among sites The first principal component (PC1) explained 34% of the variation in the dataset, and was most highly

Rapid Epiphyton Survey

0 200 400 600 800 1000 1200 1400

Rapid Epiphyton Survey

0.00 0.05 0.10 0.15 0.20 0.25

A

AB

AB

B

FIGURE 14.2 Comparison of epiphytic chlorophyll-a biomass (top) and macroalgal domi-nance (bottom) in 5 rapid epiphyton survey (RES) categories estimated in the field Letters indicate statistical similarities (Tukey multiple comparisons; p < 0.05)

correlated with land use and fragmentation variables (buffer width, r = 0.82; ripar-ian land use, r = 0.83, nearest neighbor, r = 0.53) and water conductivity (r = 0.64); all other metrics were also significantly correlated with this axis, but at much lower levels (r = 0.24–0.36) The second axis (PC2; 18% of variation in dataset) was most highly correlated with hydrological variables (modification, r = 0.73; water source,

r = 0.60), as well as a negative correlation with nearest neighbor (r = −0.59) The third axis (PC3; 16% of variation in dataset) was most highly correlated with con-taminants (r = 0.80) There was no significant difference in the location of different wetland classes along PC1; however, PC2 values were significantly higher in depres-sional wetlands, likely because fewer of these had year-round inputs of water (water source) as opposed to all riverine and lacustrine sites

As an indicator of disturbance, the WDA correlated strongly with many mea-sured land use and water chemistry variables (p < 0.05) In particular, it was highly correlated with land use variables (r = 0.54–0.60), water chemistry measures (r = 0.3–0.36), and sediment characteristics (r = 0.23–0.25) (Table 14.2)

For subsequent analyses, we separated the data into 3 sections, representing the different wetland classes (depressions, lacustrine, riverine) A significant amount of the variation in a measure of plant community structure (NMDS) and the extent to which the community was dominated by native, sensitive taxa (FQAI and CofC)

with the WDA for riverine sites, whereas the CofC was a better metric for sions and lacustrine wetlands Overall, these relationships were strongest for depres-sional and lacustrine wetlands, and lower for riverine sites In many cases, the WDA explained more variation in the biological metrics than did any individual environ-mental variable (Table 14.3); however, forested land explained slightly more of the variation in the NMDS values for depressional wetlands, and variation in riverine plant communities was explained slightly better by TP and conductivity It is inter-esting to note, however, that using a suite of 120 plant metrics calculated for all

TABLE 14.2 Significant correlations between WDA and environmental variables (p < 0.10; Bonferoni corrected).

% Agriculture 0.43 0.0000

Sediment: %organic –0.23 0.0477 Sediment C:N 0.25 0.0356

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

WDA

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80

1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55

Depressional

r 2 = 0.46 p<0.001

Lacustrine

r 2 = 0.51 p<0.001

Riverine

r2= 0.21 p = 0.02

Depressional

r 2 = 0.38 p<0.001

Lacustrine

r2 = 0.30 p<0.00 1

Riverine

r 2 = 0.20 p = 0.03

FIGURE 14.3 Relationship between plant community metrics and the WDA for depressional (left), lacustrine (middle), and riverine (right) wetlands in the MRW

© 2008 by Taylor & Francis Group, LLC

site types, including measures of species richness and plant community composition (Lougheed, unpublished data), more of these metrics were correlated to the WDA (26 metrics; Bonferoni corrected; p<0.05), than the next most commonly correlated environmental variables: developed land (21 metrics), TP (12 metrics), and Cl (5 metrics)

14.4 DISCUSSION

This study provides evidence that field-based estimates of algal cover and land use can accurately reflect more detailed measures requiring increased lab processing time and technical skills In addition, we present the development and verification of

a multimetric wetland disturbance axis (WDA) that successfully integrates stressors from 3 categories: land use, hydrological modification, and water quality The WDA

is highly correlated with a variety of land use and water chemistry measures, as well

as several measures of plant community composition

Rapid epiphyton assessment can be highly useful because it enables the determi-nation of algal biomass over larger spatial scales than sampling algae off individual substrates followed by lab analysis (Stevenson and Bahls 1999) We provide evidence that an estimate of epiphyte cover using a rapid epiphyton survey can be a good sur-rogate for more detailed measures of epiphytic and macro-algal biomass Despite its accuracy, both the rapid and more detailed measurements of algal biomass were not correlated to any rapid or detailed measures of wetland condition, including the WDA or nutrient levels Wetlands are complex environments, where both vascular plants and algae compete for nutrients and light Measures of diatom community composition (Lougheed et al 2007) or trophic state indices (e.g., Van Dam et al 1994) may be more sensitive indicators of algal responses to nutrient enrichment in wetlands than more simple measures of algal biomass In particular, Lougheed et al (2007) found that diatom community composition (as indicated by NMDS) was a

TABLE 14.3

Significant correlations between biological metrics & environmental

variables (p < 0.10; Bonferoni corrected).

Depressions Lacustrine Riverine

a Not significant when Bonferoni corrected at p < 0.05.