Grantee Research Project Results
Final Report: Assessing the Interactive Effects of Landscape, Climate, and UV Radiation on River Ecosystems: Modeling Transparency to UVR and the Response of Biota
EPA Grant Number: R829642Title: Assessing the Interactive Effects of Landscape, Climate, and UV Radiation on River Ecosystems: Modeling Transparency to UVR and the Response of Biota
Investigators: Morris, Donald P. , Williamson, Craig E. , Pazzaglia, Frank J. , Weisman, Richard N. , Hargreaves, Bruce R.
Institution: Lehigh University
EPA Project Officer: Packard, Benjamin H
Project Period: July 30, 2002 through July 29, 2006
Project Amount: $825,850
RFA: Assessing the Consequences of Global Change for Aquatic Ecosystems: Climate, Land Use, and UV Radiation (2001) RFA Text | Recipients Lists
Research Category: Climate Change , Ecological Indicators/Assessment/Restoration , Water , Aquatic Ecosystems
Objective:
Progress on this project has been made in several different areas represented by the specialties of the participating scientists. These areas include: (1) fluvial changes in streams resulting from anthropogenic processes; (2) chromophoric dissolved organic matter (CDOM) transport from the watershed; (3) transmission of UV radiation (UVR) through the riparian canopy of streams; (4) attenuation of UVR through the water column of streams; (5) the role of solar photobleaching in modifying the optical properties of CDOM; and (6) the impact of UVR on stream macroinvertebrate communities. Progress in each of these areas is summarized below. The large volumes of data collected in this project are still being analyzed and interpreted by the principal investigators (PIs) and their graduate students. We expect a number of other submissions will be made in the next 12 months.
The theses, dissertations, abstracts, and reviewed publications supported by this grant are listed at the end of the report. The PIs wish to thank the many graduate students and undergraduate interns who participated in this project.
Geomorphology Project
Geomorphic research in the U.S. Environmental Protection Agency (EPA) Science To Achieve Results (STAR) grant is dedicated to describing and quantifying those physical and hydrological characteristics of watersheds that contribute to river discharge characteristics and water quality. The core hypothesis that was tested is that land use plays an important, if not the most important, role in stream discharge and water quality. Our research is ever mindful of trying to construct the physical framework for understanding UV transparency in the fluvial environment and its effect on stream ecology, as a function of CDOM. A picture of channel forms within the Lehigh watershed emerged from geomorphic research in 2002-2003. For 2003-2004, research migrated towards quantifying stream response to variable discharge as a means of understanding how the various channel forms were shaped. The final year of geomorphic research (2004-2005) was dedicated to tracking changes in channel forms through time and through variable land use practice using historic aerial photography.
Ecologic Contex and Motivation. The boundary conditions on the flow of energy and mass through a stream ecosystem are defined by the physical and hydrological characteristics of the channel. Simple well-established hydrologic metrics, like the average spacing of pools and riffles, or the average width-depth ratio of the channel, define the depth and flow velocity of the water column. Pools provide refuge for mobile organisms from UV penetration during base flow conditions when both the water column is shallow and the CDOM concentration is low. Changes in channel configuration, like the width to depth ratio or pool and riffle spacing, perhaps driven by anthropogenic activities, represent first-order stressors on the aquatic ecology.
Watershed DOC Modeling
The concentration and optical characteristics of dissolved organic carbon (DOC) play a critical role in determining water column transparency, especially to UVR. One objective of our project was to refine and test models for variation in DOC concentration and quality across the watershed to see if specific conductance, land cover, and other terrain features could predict variations in UV attenuation underwater. The approach used was to construct a pair of automated sampling systems that could collect baseline samples daily and also be triggered automatically to collect frequent samples during storm events. A pair of nearby headwater streams were selected to compare a primarily forested area with one in which the land cover was primarily farmland. Isco 6712C samplers were equipped with batteries, rain gages, and stream stage sensors in weather-shielding enclosures and deployed along with YSI datasondes that measured stream water quality parameters at the same interval (15 minutes) as the stream stage sensors. Funnels and plastic bottles were deployed at open and forested regions at each site to collect rain and canopy throughfall to measure the relative contribution of the canopy to stream DOC. By comparing baseflow and storm events and by analyzing DOC, CDOM, and ionic composition, in addition to routine parameters (pH, temperature, specific conductance, turbidity), we anticipate that we can validate a multi-compartment model to explain changes in water quality based on hydrologic flowpath and differences in land cover.
Stream and Canopy Modeling
Our objective was to refine measurements of UV transmittance and riparian canopy properties along small streams in the watershed in order to model UV penetration of the canopy from GIS and remote sensing data. We evaluated the previous year’s data and determined several sources of error in the photographic images, (e.g., narrow field of view instead of hemispherical in canopy photographs, and automatic exposure left important parts of the higher canopy overexposed so these parts were counted as sky). We also determined that diffuse versus direct irradiance could be measured by a simple shading technique and the added information would allow better modeling of our results. We modified our digital camera system by building a self-powered (rechargeable battery + interface circuit) USB extension cable to allow the camera to be used mid-stream while the recording notebook computer stayed on the stream bank. We revisited a number of sites used the previous year and developed several sites on campus for testing our protocols. We also calibrated a UV sensor that is permanently mounted on campus to provide a local resource when our portable UV monitors are not making measurements. As we continue this project, we will characterize seasonal changes in canopy during leaf drop and leaf growth and also variations caused by differences in tree species and age and by variations correlated with stream order.
Measuring UVR Attenuation in Lotic Environments
Direct measurement of UVR attenuation (Kd) in streams is difficult and in many places impossible. Logistical complications, including expense and bulkiness of the instruments, necessary depth for profile measurements (~ 0.5 meters even with the smallest radiometers), and sensitivity of the instrument to verticality and surface smoothness make in situ measurements of Kd impractical for most ecological applications. Yet, to study the ecological implications of UVR exposure it is imperative to know, within reasonable error, the penetration of UVR through the water column. We have developed two methods to allow routine and accurate estimation of UVR attenuation in streams. We have adapted and tested a spectrophotometer-based quantitative filter pad (QFT) method to determine the contribution of suspended particulate material to Kd. Used together with spectrophotometric analysis of filtered samples, our model makes precise estimates of in situ Kd possible. We have also developed and tested a field-based microcosm method for estimating Kd in streams. Both methods have been compared to direct measurement of Kd in streams at 19 locations across the Lehigh River watershed.
Photobleaching Potential for CDOM of the Lehigh River
CDOM accounts for a large proportion of the absorption of UVR in the water column of both lakes and rivers. Photobleaching alters the optical properties of CDOM and may be an influential in-stream process regulating UVR transparency. The main purpose of this study is to: (1) show how river conditions and environmental factors such as rainfall and discharge affect the photobleaching potential; and (2) demonstrate potential optical changes in the river due to photobleaching. Photobleaching specifically refers to the degradation of the chromophoric structural groups in the DOC that are responsible for the characteristic color as well as the absorption of UVR. The degree of photobleaching is typically assessed by evaluating optical changes in the DOC that reduce the color and UVR absorbance of a particular sample of water. For the purposes of this study, photobleaching is determined by the loss of absorbance of a sample. Photobleaching is also reflected in the alteration in other optical parameters such as spectral slope and molar absorptivity.
Water samples were placed in triplicate 1-cm quartz test tubes. The samples were analyzed before and after a 48-hour exposure to a UV lamp system (Q Panel 340). Initial and final measurements included: temperature, dissolved oxygen (DO), pH, DOC concentration ([DOC]), fluorescence (total and a ratio of emission at 450 nm and 500 nm), and absorbance (200-800 nm).
Influence of UVR on Macroinvertebrates
The subproject of the EPA STAR grant on the effects of UVR on lotic invertebrates has had two components over the past year. The first component examined in situ attenuation of UVR with a BIC submersible profiling radiometer. The idea was to both characterize the underwater light regimes to which macroinvertebrates are exposed, and to simultaneously examine particulate and dissolved absorbance to test optical models being used by others in the project. Profiles of UVR and photosynthetically available radiation (PAR) were taken in all of the standard sampling sites on the tributaries and main stem of the upper and lower reaches of the Lehigh River. Water samples were also collected and brought back to the laboratory for processing by Bruce Hargreaves. The UVR profile data were archived on the server.
The second component of the project focused on the behavioral response of stream macroinvertebrates to changes in exposure to UVR and visible light. This subproject involved a series of in situ experiments looking at the behavioral response of macroinvertebrates to stream reaches with different riparian vegetation and hence solar shading regimes. This work is being done in the context of land-use changes and the possible effects these changes may have on stream macroinvertebrate communities.
Summary/Accomplishments (Outputs/Outcomes):
Geomorphology Project
This Ph.D. research of Josh Galster explored the interactions between a watershed and its trunk stream by examining how watersheds influence river discharge at a variety of spatial scales. The discharge of a river is a fundamental process operating within a watershed and strongly influences sediment transport, human use of water, aquatic habitat, and, at longer time scales, landscape evolution. Discharge is then a logical place to begin when investigating the interactions of rivers and the watersheds that contain them.
Examined first was the discharge characteristics of two similar watersheds in eastern Pennsylvania, the Little Lehigh Creek and Sacony Creek. These two neighboring watersheds share many characteristics except for their current land use, with the Little Lehigh Creek watershed having more than three times the amount of urbanized land within it. The larger urbanized area within the Little Lehigh Creek watershed accelerates the generation of surface runoff from precipitation and, consequently, river discharge. The peak discharges in the Little Lehigh Creek increase downstream at almost twice the rate of that of the more rural Sacony Creek when scaled to drainage area (Figure 1). An increase in peak discharges from urbanization is well documented, but this research is unique for quantifying the rate of that increase in discharge with drainage area.
Figure 1. Discharges in the Little Lehigh Creek. Discharge in a river channel grows as drainage basin area increases following the general equation: Q = kAc, where Q is river discharge, k is a measure of river base flow, A is upstream drainage area, and c is the scaling power dependency. The regression values of each peak flow event (Logarithm A vs. Logarithm Q, using a minimum of three data points for each regression) as well as the compiled linear regression (c, dotted line) using dummy variables for all peak flow events for Sacony Creek (black triangles) and Little Lehigh Creek (white triangles). The discharge increases at a faster rate for the Little Lehigh Creek (c = 1.81 + 0.28) than for Sacony Creek (c = 0.83 ± 0.25), represented by the larger value for the Little Lehigh Creek regression.
The second part of this work examined the long-term effects of those higher discharges on the channel widths of these two streams. High-resolution surveys comparing the modern channel morphologies and direct measurement of the channel widths before (1946/1947) and after (1999) urbanization were used. The surveys demonstrated that the complexity of the two rivers, with large variations in channel metrics over measured reaches (~100 m in length), was too great to reliably discern any differences between the two rivers. However, the large number of width measurements completed on the aerial photographs enabled the influence of increased urban areas on channel widths to be observed. The earliest aerial photographs were taken before the current levels of urbanization, and the Sacony Creek watershed remained predominantly rural. Its widths did not systematically change during the 52-year time period, while the Little Lehigh Creek, with its watershed becoming more urbanized, had more than three-quarters of its widths increase an average of over 3 m (Figure 2).
Distance upstream from mouth (km)
Distance upstream from mouth (km)
Figure 2. The Change in Widths of Little Lehigh Creek (top) From 1947 to 1999 and Sacony Creek (bottom) From 1946 to 1999. The error bars are the 95% confidence interval of the difference in means calculated from a t-test of the replicate width measurements.
The third and final chapter stems from the concept of the scaling of discharge and drainage area introduced in the first chapter. The scaling of the two variables is examined over a much larger scale by investigating how peak and mean annual discharges increase downstream in five undammed rivers from the continental United States: the John Day River (Oregon), Salmon River (Idaho), Yellowstone River (Wyoming/Montana), Wabash River (Indiana), and Greenbrier River (West Virginia). These rivers encompass a range of climatic and geologic settings, but their lack of dams on the main stems of the rivers enables the discharges to be examined in as natural a state as possible. The Colorado River, with its extensive system of dams, represents a case study in how discharge scales with area for a watershed heavily impacted by human management strategies. The assumption is often made that discharge and drainage area scale at a value close to 1, but this has not been extensively tested.
Four of the rivers do scale at values that approximate 1: the John Day River, Salmon River, Wabash River, and Greenbrier River. The scaling value is 0.8, suggesting that variables such as precipitation, relief, and slope decrease the scaling factor to 0.8. The Yellowstone River’s peak annual discharges scale at values approximating 0.5 and steadily decrease over the length of the discharge record. This unique scaling of the Yellowstone is possibly due to the strong west/east gradient in precipitation and the large contribution of snow melt to the peak annual discharge. The change in the scaling factor over time is most likely reflective of a change in climate, and it is proposed that the warmer temperatures produce higher, more concentrated peak discharges at higher elevations. The Colorado River discharge also scales at values close to 0.5, and the construction of the large dams and resulting reservoirs on the main stem are visible in the discharge record.
Watershed DOC Modeling
We established the importance of wetland area and forest cover in controlling [DOC]. We were surprised to find that stream DOC was largely derived from algal production in regions where carbonates were more concentrated, but cannot resolve the causal factor (stream shading versus chemistry). Specific conductance was a good predictor of DOC concentration at any given site because rain and groundwater contribute to different degrees to streamflow over time and also different in typical DOC concentration. Specific conductance was also highly correlated with variations in the source of DOC (soil versus algal) for reasons that are still unclear.
Response to rain storms for different land cover was investigated with high-frequency automated sampling. Four sites were evaluated: one August storm on Tobyhanna Creek (Pocono Plateau, draining extensive wetlands); multiple storms from May to October along Cranberry Creek (as it passes through Tannersville Bog, Pocono Plateau); multiple storms April-October along a tributary of Jordan Creek (forested Pennsylvania State Game Lands, near Schnecksville, PA); and multiple storms April-October along a tributary of Switzer Creek (agricultural lands near the forested Pennsylvania State Game Lands near Schnecksville, PA). Patterns that were the same across sites included an inverse correlation between specific conductance and [DOC] during rain events and a tendency for CDOM to become more soil-like during storm runoff and more algal-like during baseflow. The influence of the type of vegetation was similar across sites: higher [DOC] when wetlands were more abundant. Within the stream that drains 100 percent wetland, there was still variation between soil-like CDOM during storm runoff and algal-like CDOM during baseflow. A difference between agricultural land cover and forested land cover is that DOC from canopy throughfall contributes substantially to the DOC discharged by forested streams during storm runoff. In most cases, [DOC] increased during storm runoff, presumably mobilized from soil by the runoff; in the largest storms, the effect of rain and overland flow was to briefly lower [DOC] by dilution before causing the typical rise in [DOC]. A difference in the timing of discharge between streams with extensive wetlands compared to other areas was the capacity to rapidly store water in the wetland during a storm and to slowly discharge it during baseflow periods (see Figures 3 and 4).
Figure 3.
Figure 4.
We succeeded in recording stream data for a number of small storms and baseflow conditions with our paired automated stream samplers. With hard work and a bit of luck, we also recorded the complete record as the remnants of Hurricane Ivan passed through our region and caused major flooding. High water moved one of our stations slightly and left it muddy, but nothing at these sites was damaged and all samples and data were safe. At another stream monitoring site where we were logging stream stage, we were not so fortunate and lost a small data logger to the 50-year flood. Comparing storm flow over a range of precipitation, we can confirm the general impression from last year that DOC and CDOM vary inversely with specific conductance, but we can now add that throughfall DOC from the canopy makes a major contribution during rain events (see Figures 5, 6, and 7).
Figure 5.
Figure 6.
Figure 7.
Stream Canopy Modeling
Canopy photographs in the first year using a less-than-hemispherical field of view were correlated with UV measurements made at the same time (Figure 8). Diffuse light appeared to be important in regulating UV transmittance. Small order streams appeared to have lower UV transmittance through the canopy but the results appeared to be site-specific and not easily extrapolated to other areas.
Figure 8. Digital Photography Technique, Tree Lined Pathway in Front of Williams Hall, October 26, 2004, 11:00 a.m. Sky overcast. Default (brightness ~ 7.8; contrast ~ 8.2); gap threshold: 100%; gap fraction: 78.20%; sky view: 79.44%.
Our canopy optics investigation confirmed the need to manually adjust hemispherical photographs because the highest part of the canopy is usually much brighter than the average canopy and may be lumped with sky when computing transmittance (Figure 9). Our preliminary analysis suggests that this brightness is not a source of UV and thus should be excluded from the sky view fraction that we correlate with UV transmittance. Also, we have confirmed by measurements of diffuse and direct irradiance in the open and under canopy that diffuse light is the dominant contributor to UV penetration and must be measured explicitly. Typically, the UV-B wavelengths under a clear sky are 60-80 percent diffuse while the UV-A wavelengths are closer to the visible waveband (PAR) and range from 50-20 percent diffuse. Preliminary work with remote images (air photos and satellite multispectral images) showed promise for estimating canopy openness and UV-B attenuation as well as stream canopy gap for streams of higher order. As expected, canopy gap over streams increased in higher stream order, but age of the trees and prior forestry practices clearly influence the relationship between canopy openness and stream order.
Figure 9. Manual Setting To Improve Mid-day Canopy Image: Brightness Level 6, Contrast Level 5; Gap Threshold: 100% Gap Fraction: 38.80%; Sky View: 43.88%
Measuring UVR Attenuation in Lotic Environments
A plexiglass “microcosm” was constructed in such a way to accommodate the measurement of UVR attenuation across a 20 cm pathlength of water using a PUV-500 radiometer. This system allowed for the estimation of stream Kd values by removing water from the stream and making measurements under natural sunlight (thus eliminating canopy effects and issues with stream current and surface roughness). Side by side measurements of Kd using the microcosm and direct measurement of Kd in situ (Lake Nokomixon) showed that for most wavelengths of UVR there was no statistically significant difference in attenuation coefficients (Table 1).
Table 1. Mean Kd Values for Three Lake Profiles and Three Trials in the Microcosm
In Situ Kd |
2 σ SE |
Microcosm Kd |
2 σ SE |
|
PAR |
2.1 |
± 0.03 |
2.3 |
± 0.12 |
380 |
11.6 |
± 0.35 |
11.3 |
± 0.31 |
340 |
19.3 |
± 0.41 |
18.9 |
± 0.54 |
320 |
24.8 |
± 0.62 |
24.5 |
± 0.65 |
305* |
29.8 |
NA |
30.9 |
± 0.07 |
After demonstrating the effectiveness of the microcosm in measuring Kd, the system was then used as an experimental platform to develop a modified QFT method and test various models of predicting Kd based on laboratory spectrophotometric analysis of dissolved and particulate absorption of stream samples. Suspended sediment concentrations were manipulated in the microcosm, which was used to obtain direct measurements of diffuse attenuation. Dissolved and particulate absorption measurements of samples from the microcosm experiments were used to calibrate the laboratory method. The experiment covered a range of suspended sediment (0-50 mg L-1) and DOC concentrations (1-4 mg L-1). Four models for calculating the particulate absorption coefficient from the QFT data were evaluated for precision and reproducibility, and we used an empirical approach to estimate diffuse attenuation coefficients from total absorption coefficients. We then field-tested the laboratory method by comparing estimated diffuse attenuation coefficients to 7 sites on the main stem and 10 large tributaries to the Lehigh River, Pennsylvania (Figure 10). The laboratory-based method described here affords widespread application, which is imperative to further our understanding of how the optical environment influences ecological processes in streams.
Figure 10. Regression of Kd Values Calculated Using the QFT (Roesler 2.0 Equations and Associated μ Values) to Kd Measured In Situ at 18 Different Locations Along the Main Trunk and Major Tributaries of the Lehigh River. Regression slopes, forced through the origin, were 1.17, 1.31, and 0.96 for 380, 320, and 305 nm, respectively.
Photobleaching Potential for CDOM of the Lehigh River
Photobleaching caused significant changes in all the variables measured in this study (Table 2).
Table 2. A Summary of Chemical and Optical Changes After Photobleaching. Each variable had a sample size of n = 72.
Max. |
Min. |
Median |
Mean |
s.e. |
p |
% Change |
|
Δ Dissolved O2 (mg/L) |
− 3.72 |
0.17 |
− 0.76 |
− 0.83 |
0.06 |
< 0.05 |
− 8.1 |
Δ pH @ 25°C |
− 0.71 |
0.17 |
− 0.10 |
− 0.11 |
0.02 |
< 0.05 |
− 2.5 |
Δ DOC (mg/L) |
− 0.52 |
− 0.02 |
− 0.14 |
− 0.17 |
0.01 |
< 0.05 |
− 10.0 |
Δ Fluorescence ratio |
− 0.60 |
0.00 |
− 0.22 |
− 0.22 |
0.01 |
< 0.05 |
− 12.2 |
Total Fluorescence |
− 57901 |
− 791 |
23330 |
− 24186 |
1201 |
< 0.001 |
− 57.1 |
Δ Absorption coef. at 320 (m-1) |
− 6.74 |
− 0.87 |
− 2.30 |
− 2.57 |
0.15 |
< 0.001 |
− 37.0 |
Δ Specific abs coef. At 320 (m-1) |
− 1.96 |
− 0.45 |
− 1.15 |
− 1.21 |
0.04 |
< 0.001 |
− 30.4 |
Δ S UVB (nm-1) |
0.00942 |
0.00062 |
−0.00338 |
0.00382 |
0.00016 |
< 0.001 |
− 25.3 |
The greatest changes during the 48-hour incubations were observed in total fluorescence and absorbance (at 320 nm), with a 57 percent and 37 percent decrease, respectively. The DOC-specific absorbance (− 30%) and the UV-B spectral slope (+ 25%), indices of DOC quality, also changed substantially. A regression analysis indicates that the change in UV-B is highly correlated to photobleaching rate constant Kb (r = 0.69, p ≤ 0.001), suggesting that photobleaching does not decrease in absorbance uniformly across the UV-B spectrum. Small but statistically significant changes were also observed in pH (− 2%), dissolved oxygen (− 8%) and DOC (− 10%). The values of Kb (at 320 nm) varied substantially over the course of the study (Figure 11). The average value of Kb was 0.0108, but significant seasonal variations were observed.
Figure 11. Photobleaching Rate Constant (k +/- s.e.) With Date
Average values of Kb were lowest in the spring (0.0094) and highest in the fall (0.0122) (Table 3). The lower spring values could be related to snowmelt, whereas the reoccurring high fall values can be associated with leaf drop.
Table 3. Seasonal Variations in Kb
Season |
Kb avg |
SE |
Winter |
0.0110 |
2.78E-05 |
Spring |
0.0094 |
2.78E-05 |
Summer |
0.0099 |
1.68E-05 |
Fall |
0.0122 |
3.70E-05 |
Univariate regression analyses were performed between Kb and other parameters in order to determine which factors are important in regulating photobleaching in the Lehigh River. A significant suite of variables related to [DOC] and quality were each inversely correlated with Kb (p ≤ 0.001) and explained 17-51 percent of the variation in Kb (Table 4). The fluorescence ratio (51%), UV-B spectral slope (44%), and DOC-specific absorbance (30%), possible indicators of DOC source and prior photobleaching, explained the greatest amount of variance. The chemical parameters (conductivity, pH, and alkalinity) were each significantly correlated with Kb (p ≤ 0.001) and explained only 19-37 percent of the variation. Lastly, the environmental variables (discharge and precipitation) were inversely correlated with Kb and explained about 20 percent of the variation. These variables do not directly influence Kb, but are probably important in regulating the quantity and quality (source) of CDOM as well as the ionic composition of the river, which may directly influence photobleaching rates.
Table 4. A Summary of the Correlation Analysis Between Various Parameters and Kb. The r-values are reported as well as the significance, which is denoted as * = p < 0.05, ** p = < 0.001.
Discharge |
Precip5 |
pH |
Conductance |
Alkalinity |
DOC |
Total F |
Ratio F |
abs 320 |
a320/DOC |
S UV-B |
|
Kb |
−0.46 |
−0.46 |
0.53 |
0.61 |
0.44 |
−0.4 |
−0.36 |
0.71 |
−0.61 |
−0.54 |
0.66 |
In order to explain more of the variance in Kb, a multivariate regression was developed using four types of models: (1) models using only environmental variables (discharge and precipitation); (2) models using only chemical variables (pH, conductance, alkalinity, and DOC), (3) models using only optical variables (absorption coefficients, spectral slope, DOC-specific absorbance, and fluorescence ratio); or (4) models using both optical and chemical variables (Table 5). The best model based on only environmental variables explained 18 percent of the variance (p ≤ 0.001). Models using only chemical variables explained 36 percent of the variance (p ≤ 0.001). Models using only optical variables had better results, explaining 54 percent of the variance (p ≤ 0.001). However, the model that included both optical and chemical variables performed the best, explaining 75 percent of the variance.
Table 5. A Summary of Multivariate Models Run Between Kb and a Variety of Environmental Variables
Model/Variable (transform) |
n |
Coefficient |
s.e. |
r2 |
p |
Environmental Model: |
76 |
0.18 |
< 0.001 |
||
Discharge |
− 1.23E-05 |
0.000 |
< 0.001 |
||
Precipitation |
1.72E-04 |
0.000 |
0.024 |
||
Constant |
0.0113 |
0.000 |
|||
Chemical Model: |
58 |
0.36 |
< 0.001 |
||
pH |
1.64E-03 |
0.001 |
0.018 |
||
Conductance |
8.21E-06 |
0.000 |
0.035 |
||
Constant |
− 0.0041 |
0.008 |
0.063 |
||
Optical Model: |
58 |
||||
Fluorescence ratio |
0.0106 |
0.002 |
0.54 |
< 0.001 |
|
S UV-B |
0.0382 |
0.181 |
0.008 |
||
Constant |
− 0.0047 |
0.002 |
0.016 |
||
Optical and Chemical Model: |
59 |
||||
Conductance |
− 7.65E-06 |
0.0000 |
0.75 |
< 0.001 |
|
Fluorescence ratio |
1.81E-02 |
0.0026 |
< 0.001 |
||
S UV-B |
0.2339 |
0.1695 |
0.017 |
||
Constant |
− 0.0111 |
0.0023 |
< 0.001 |
Influence of UVR on Macroinvertebrates
The experimental reach is located within a section of the Little Lehigh River, a tributary to the Lehigh River. This section of the stream is composed of two distinct sites. One site is shaded by thick forest canopy cover, created by dense riparian vegetation that blocks much of the incoming UVR and PAR. The second site is exposed to sunlight, with only limited streamside vegetation, allowing both UVR and PAR to reach the water surface. The differences in the two adjacent sites were chosen to signify a change in land use, which leads to a loss of streamside vegetation.
A set of experiments was run during 2004 and 2005. These experiments were established to determine whether the experimental design would provide adequate results and whether the constructed, artificial habitat would withstand stream conditions. The artificial habitat design of this project evolved from mesocosm designs found throughout scientific literature, which focused primarily on predation and nutrient limitation responses by macroinvertebrates. The artificial habitat is composed of a plastic tray filled with rocks of similar size and number and an anchoring slab of slate. The tray is connected to the slate slab using self-locking, plastic zip ties. Twelve of these habitats were constructed and placed in the stream to control for vast amounts of heterogeneity within stream beds.
The first experiment placed six artificial habitats in the two sites along the stream: shaded (forested) and non-shaded (open). After a 1-week incubation period, the contents of the trays were removed from the stream and analyzed in the laboratory for macroinvertebrate colonization (total abundance) and biodiversity (species richness, Shannon-Weaver index, and evenness).
The second experiment followed the same timeline as the first, except for an additional incubation period. For this period, all 12 habitats were placed in the open site for 1 week to establish a uniform algal substrate on the rocks.
The equations used to calculate the biodiversity indices were the following:
- Shannon-Weaver Index = ,
where s is the total number of times that the equation is calculated, once for each species, and Pi is the proportion of individuals of a certain species out of the total number of individuals.
- Evenness = H/log(S), where H is the Shannon-Weaver Index of a site and S is the species richness.
Data for the preliminary experiments were collected on three occasions: 23 June 2004, 30 June 2004, and 27 July 2004 (July 27th data are from the experiment with an additional week incubation period in the open site). There was a significantly higher number of total macroinvertebrates in the open site compared to the forested site for all three dates (Figure 12). There was a higher number of species in the open site on 23 June (Figure 13). There was also a higher Shannon-Weaver Index for the open site on 23 June and for the forested site on 27 July (Figure 14). The forested site also had significantly more evenness than the open site on 30 June and 27 July (Figure 15).
Figure 12. Between Site Comparison of the Average (+ S.E.) Number of Macroinvertebrates Collected From the Six Artificial Habitats
Figure 13. Between Site Comparison of the Average (+ S.E.) Species Richness in the Six Artificial Habitats
Figure 14. Between Site Comparison of the Average (+ S.E.) Shannon-Weaver Index From the Six Artificial Habitats
Figure 15. Between Site Comparison of the Average (+ S.E.) Evenness Calculated From the Six Artificial Habitats
There seems to be a distinct shift in community structure and taxa composition between the open site and the forested site, which are separated by only a 25-meter or so stretch of stream. Results have been analyzed only to the major group level at this point. The categories include: ephemeroptera (mayflies), chironomidae (midges), plecoptera (stoneflies), hydracarina (water mites), megaloptera (dobsonflies), oligochaeta (aquatic worms), trichoptera (caddisflies), coleoptera (beetles), tipulidae (craneflies), and other diptera (flies). On June 23, mayflies contributed the greatest numbers to both sites, with stoneflies and chironimids secondary in abundance. The forested site had lower numbers of megalopterans, oligochaets, and tipulids, while water mites were observed only in the open site (Figure 16). On June 30, mayflies again dominated both sites, with caddisflies and stoneflies being secondary in abundance in the forested site and chironimids and stoneflies being secondary abundance in the open site (Figure 17). On July 27, mayflies dominated the forested site with six other groups present in lower abundance, while in the open site chironimids dominated with mayflies in secondary abundance and several other taxa at low abundances (Figure 18).
Figure 16. Percent Contribution of the Various Macroinvertebrate Taxa in the Open and Forested Sites on 23 June 2004
Figure 17. Percent Contribution of the Various Macroinvertebrate Taxa in the Open and Forested Sites on 30 June 2004
Figure 18. Percent Contribution of the Various Macroinvertebrate Taxa in the Open and Forested Sites on 27 July 2004
Current and future experiments are designed to separate two possible variables that may influence these results: behavioral avoidance and food limitation. It is believed that if macroinvertebrates are not food limited, either through nutrient or PAR limitation, they will selectively avoid habitats, which expose them to higher levels of UVR. Land development and land-use change can alter the optical qualities of water through both a loss in streamside vegetation and changes in the amount of DOC entered into a system. This project is focusing on the former of the two processes.
Journal Articles on this Report : 5 Displayed | Download in RIS Format
Other project views: | All 37 publications | 5 publications in selected types | All 5 journal articles |
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Belmont P, Hargreaves BR, Morris DP, Williamson CE. Estimating attenuation of ultraviolet radiation in streams: field and laboratory methods. Photochemistry and Photobiology 2007;83(6):1339-1347. |
R829642 (Final) |
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Belmont P, Morris D, Pazzaglia F, Peters S. Penetration of ultraviolet radiation in streams of eastern Pennsylvania:Topographic controls and the role of suspended particulates. AQUATIC SCIENCES 2009;71(2):189-201. |
R829642 (Final) |
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Galster JC, Pazzaglia FJ, Hargreaves BR, Morris DP, Peters SC, Weisman RN. Effects of urbanization on watershed hydrology: the scaling of discharge with drainage area. Geology 2006;34(9):713-716. |
R829642 (Final) |
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Galster JC. Natural and anthropogenic influences on the scaling of discharge with drainage area for multiple watersheds. Geosphere 2007;3(4):260-271. |
R829642 (Final) |
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Galster JC, Pazzaglia FJ, Germanoski D. Measuring the impact of urbanization on channel widths using historic aerial photographs and modern surveys. Journal of the American Water Resources Association 2008;44(4):948-960. |
R829642 (Final) |
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Supplemental Keywords:
RFA, Scientific Discipline, Air, Geographic Area, Water, Water & Watershed, climate change, State, Environmental Monitoring, Wet Weather Flows, Ecological Risk Assessment, EPA Region, Watersheds, water resources, dissolved organic matter, wetlands, hydrologic dynamics, global change, regional hydrologic vulnerability, aquatic food web, urban runoff, hydrologic models, climate models, UV radiation, hydrology, vulnerability assessment, aquatic ecosystems, watershed sustainablility, Lake Superior, solar radiation, water quality, land and water resources, Region 5, aquatic ecology, stormwater runoff, climate variability, Global Climate Change, land use, vegetation models, ecological research, Michigan (MI), land managementProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.