EFFECTS OF ADJACENT LAND-USE PRACTICES AND
ENVIRONMENTAL FACTORS ON RIPARIAN VEGETATION AND WATER
QUALITY IN THE SUGAR CREEK WATERSHED, NORTHEASTERN OHIO
Thesis
Presented in Partial Fulfillment of the Requirements for the Degree Master of
Science in the Graduate School of The Ohio State University
By
Heather Lyn Whitman, B.S.
Graduate Program in Environment and Natural Resources
The Ohio State University
2009
Thesis Committee:
Dr. David M. Hix, Co-advisor
Dr. P. Charles Goebel, Co-advisor
Dr. Dawn R. Ferris
ABSTRACT
Riparian areas are critical components of watersheds that provide many important
ecosystem functions, including buffering nutrients and sediments from surface runoff,
reducing erosion, and providing shade and organic matter to stream ecosystems.
However, in many agricultural watersheds, riparian areas are highly disturbed, leading to
a variety of issues including degraded water quality and loss of important habitat. One
such watershed is the Sugar Creek watershed in northeastern Ohio, which was identified
in 1998 by the Ohio Environmental Protection Agency (OEPA) as one of the most
degraded watersheds in the state. Despite this classification, OEPA also identified the
Sugar Creek watershed as a good candidate for watershed restoration suggesting that the
loss of riparian vegetation and habitat was a primary reason for the poor water quality.
Consequently, for almost a decade, efforts have been underway to restore riparian
vegetation in the Sugar Creek watershed. Despite the increased interest in riparian
restoration, very little information on the current condition of riparian vegetation and
factors that regulate the development of riparian vegetation is available. In an effort to
better understand how adjacent land use and environmental factors influence the current
composition and structure of riparian vegetation in the Sugar Creek watershed, we
examined the riparian ground-flora, woody understory, and overstory compositions and
ii
structures of the Upper, North Fork, and South Fork subwatersheds of Sugar Creek.
These subwatersheds were selected because they have different geologic and adjacent
land use histories, allowing us to contrast the relative importance of these factors on the
composition and structure of riparian vegetation across the Sugar Creek watershed.
We examined how land use influences water quality in the South Fork
subwatershed. Specifically, we used redundancy analysis to examine how land use
categories influenced concentrations of total solids, phosphorus, ammonia, nitrate, pH,
temperature, conductivity, dissolved oxygen, and turbidity. These analyses suggest that
urbanization was associated with total solids, turbidity, conductivity, and the
concentration of phosphorus and ammonia. Wooded areas and riparian buffer strips were
inversely associated with agricultural land uses, pH, and nitrate concentration levels.
These results suggest that more focus should be placed on better management practices in
urban areas and on agricultural lands, as well as increasing the amount of riparian areas to
reduce land-use impacts on Sugar Creek. These riparian buffers can help improve water
quality by reducing sediment and phosphorus loadings through reducing erosion, and can
help alleviate nitrogen loadings via plant uptake. Additionally, understanding these
relationships can help farmers and resource managers target specific subwatersheds that
need restoration plans to overall improve water quality in the Sugar Creek watershed.
Before these restoration efforts can be developed, more information on the factors
that regulate plant community and structure is needed. Using multiresponse permutation
procedures (MRPP), we found that the ground-flora species composition was
significantly different among riparian areas with various adjacent land uses in the Upper
(P=0.0018) and South Fork (P=0.0450) subwatersheds. However, we observed no
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significant differences in total cover, or cover of individual life-form guilds, among
adjacent land uses. No significant differences were observed in diversity among riparian
areas with different adjacent land uses in the Upper and North Fork subwatersheds;
however, we did observe that riparian areas adjacent to grassed pastures had the highest
species richness (P=0.0300) and Shannon Diversity Index (P=0.0200) in the South Fork
subwatershed. Using NatureServe’s online conservation status and invasiveness potential
of each species, we also assessed the restoration potential of the different riparian areas.
Many highly invasive, non-native species (e.g., Alliaria petiolata (M. Bieb.) Cavara &
Grande, Cirsium arvense (L.) Scop., and Schedonorus phoenix (Scop.) Holub) were
present in the riparian ground-flora communities adjacent to lawns in the Upper
subwatershed, grassed pastures in the North Fork subwatershed, and a mixture of row
crop and grassed pastures in the South Fork subwatershed. These results suggest that
these invasive plants will most likely prove difficult to remove, and that restoration
efforts may be more difficult in these riparian areas. Better management practices in the
adjacent land use are also needed as these practices can adversely affect the composition
and diversity of riparian areas in agricultural landscapes.
Although results of MRPP analysis revealed that overall overstory species
composition was not significantly different among riparian areas with different adjacent
land uses (P=0.3650), we observed strong relationships among individual overstory
species, adjacent land use, physiographic, and stream variables. Specifically, results of
canonical correspondence analysis revealed a positive relationship among riparian areas
with wooded pasture, a mixture of row crop and lawn, a mixture of row crop and grassed
pasture adjacent land uses and fine loamy to silt loam soil texture, sandy loam soil
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texture, and stream slope, as well as a positive relationship among riparian areas with a
mixture of lawn and wooded adjacent land uses and alluvium parent material. We also
found canopy openness to be significantly different among riparian areas adjacent to
different land uses (P=0.007). Riparian areas adjacent to wooded land uses had the most
diverse canopy structure in terms of overstory crown class, as well as the highest woody
understory density among the land-use types. Adjacent land use can have a significant
impact on the overstory composition and structure of riparian areas, with more highly
disturbed adjacent land uses often being associated with riparian areas that have
simplified canopy structures and reduced complexity. Because of the diverse structure in
riparian forests, and the important functions they provide, efforts in the Sugar Creek
watershed should focus on restoring riparian areas along Sugar Creek so that they have
more complex stand structures and increased canopy closure. In this way, these restored
riparian areas will emulate more closely the characteristics of those riparian areas that are
considered reference sites or those adjacent wooded areas in the Sugar Creek watershed.
Focus on better management practices in adjacent terrestrial areas is also necessary
because of the effect adjacent land-use practices have on riparian species composition
and structure.
v
ACKNOWLEDGEMENTS
I would like to thank my co-advisors, Dr. David M. Hix and Dr. P. Charles
Goebel, for their guidance. You have increased my knowledge base and have helped me
become better equipped to manage natural resources. Thank you for giving me the
opportunity to pursue this degree.
I would also like to thank my committee member, Dr. Dawn R. Ferris, for her
interest in my project and her helpful insights.
I am thankful to Troy Chapman, Keely Davidson-Bennett, and Sarah McNulty
who helped me immensely gathering data in the field. Without the help of these
individuals, this research would not have been possible. I would also like to thank the
landowners throughout the Sugar Creek watershed who gave me permission to sample on
their property.
I am grateful for the financial support I have received over the past two years.
Thank you to the School of Environment and Natural Resources for my teaching
assistantships and research assistantship. I am thankful for the support received from the
USDA National Water Quality Program that funded our field work.
vi
VITA
2006………………………B.S. Natural Resources Management
Grand Valley State University, Allendale, MI
2007-2009………………...Graduate Teaching Associate and Graduate Research
Associate
The Ohio State University
School of Environment and Natural Resources
FIELDS OF STUDY
Major Field: Environment and Natural Resources
vii
TABLE OF CONTENTS
Abstract…………………………………………………………………………...……....ii
Acknowledgements……………………………………………………………...….…....vi
Vita……………………………………………………………………………………....vii
List of Tables………………………………………………………………………...……x
List of Figures………………………………………………………………………...…xiii
Chapters:
1. Introduction……………………….…………………………………………………..1
Literature Cited…………………………………………………………………….….7
2. Relating land use and water quality in an agricultural watershed in northeastern
Ohio…………………………………………………………………………………..12
Introduction………………………………………………………………………….12
Study Area…………………………………………………………………………...15
Methods……………………………………………………………………………...16
Data Collection…………………………………………………………………..16
Land Use…………………………………………………………………....……16
Data Analyses……………………………………………………………………17
Results……………………………………………………………………………….18
Discussion…………………………………………………………………...….……20
Management Implications………………………………………………….…….22
Conclusion………………………………………………………………………..….25
Literature Cited…………………………………………………………….………...26
3. Compositional, diversity, and restoration efforts of riparian ground-flora communities
in an agricultural watershed of northeastern Ohio……………………………….…..34
Introduction…………………………………………………..…………………...…34
viii
Study Area……………………………………………….………………………..…38
Methods………………………………………………………………………….…..40
Data Collection…………………………………………………………………..40
Data Analyses……………………………………………………………………41
Results………………………………………………………………………….……44
Ground-flora Composition and Functional Guilds………………………………44
Ground-flora Diversity……………………………………………………..….…46
Species’ Conservation Status and I-Rank………....….…………………….……46
Discussion………………………………………………………………………..…..48
Ground-flora and Land Use Relationships………………………………………48
Implications for Restoration…………………………………………………………50
Literature Cited……………………………………………………………………....53
4. Woody riparian vegetation composition and structure associated with adjacent land
uses and vegetation-environmental relationships in an agricultural watershed of
northeastern Ohio……………………………………………………………………78
Introduction………………………………………………………………………….78
Study Area…………………………………………………………………...………83
Methods……………………………………………………………………...………85
Field and Laboratory Analyses…………………………………………...……...85
Data Analyses…………………………………………………………...…….....88
Results……………………………………………………………………...………..91
Stream, Physiographic, and Soil Characteristics.……………………....………..91
Overstory Structure…………………………………………………....…………91
Overstory Composition……………………………………………....…………..92
Understory Structure and Composition………………………..……...………....93
Vegetation and Environment Relationships………………………...………..…..94
Discussion……………………………………………………………………...….…95
Vegetation-Environment Relationships……………………………………….....96
Implications for Restoration…………………………………………………………98
Literature Cited………………………………………………………………..……100
Bibliography……………………………………………………………………............117
Appendix A: Subwatershed, sampling location name, adjacent land use, side of road
sampling occurred on, and location of each sampling location in the Upper, North
Fork, and South Fork subwatersheds in the Sugar Creek watershed in northeastern
Ohio……………………………………………………………………….…….......124
Appendix B: Species code, scientific name, and common name for overstory species in
the Sugar Creek watershed in northeastern Ohio………….………...……………...126
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LIST OF TABLES
Table 2.1. Percent of each sub-basin delineated as a particular land use for each sub-basin
in the South Fork subwatershed of the Sugar Creek watershed in northeastern Ohio...…29
Table 2.2. Year-round mean water quality parameters collected 2003-2006 for each subbasin in the South Fork subwatershed of the Sugar Creek watershed in northeastern
Ohio……………………………………………………………………………...……….30
Table 3.1. Count of sampling locations associated with riparian areas adjacent to various
land uses in the Upper, North Fork, and South Fork subwatersheds in the Sugar Creek
watershed in northeastern Ohio……………………….…………………………………58
Table 3.2. NatureServe (2009) designations for species’ conservation status and rounded
I-Rank…………………………………………..………………………………………..59
Table 3.3. Mean (+ 1 standard deviation) importance percentage of ground-flora species
occurring on 70 percent of plots for riparian areas adjacent to row crop, lawn, and
wooded land uses for the Upper subwatershed of the Sugar Creek watershed in
northeastern Ohio. An asterisk after a value indicates the species was an indicator of a
specific riparian type (Monte-Carlo test; * P < 0.05)……….…………………….……..60
Table 3.4. Mean (+ 1 standard deviation) importance percentage of ground-flora species
occurring on 70 percent of plots for riparian areas adjacent to a grassed pasture land use
for the North Fork subwatershed of the Sugar Creek watershed in northeastern Ohio. An
asterisk after a value indicates the species was an indicator of a specific adjacent land use
(Monte-Carlo test; * P < 0.05)………………………………..………………………….63
Table 3.5. Mean (+ 1 standard deviation) importance percentage of ground-flora species
occurring on 70 percent of plots for riparian areas adjacent to row crop, grassed pasture,
row crop/grassed pasture, and lawn/wooded land uses for the South Fork subwatershed of
the Sugar Creek watershed in northeastern Ohio. An asterisk after a value indicates the
species was an indicator of a specific riparian type (Monte-Carlo test; * P < 0.05)....….65
x
Table 3.6. Mean (+ 1 standard deviation) importance percentage of life-form guilds for
riparian areas adjacent to row crop, lawn, wooded, and row crop/lawn land uses for the
Upper subwatershed in the Sugar Creek watershed in northeastern Ohio. An asterisk
indicates a significant difference among the riparian types (Kruskall-Wallis;
P < 0.05)……………………………………………………………………………….…69
Table 3.7. Mean (+ 1 standard deviation) importance percentage of life-form guilds for
riparian areas adjacent to grassed pasture, wooded pasture, row crop/lawn, and row
crop/grassed pasture land uses for the North Fork subwatershed in the Sugar Creek
watershed in northeastern Ohio. An asterisk indicates a significant difference among the
riparian types (Kruskall-Wallis; P < 0.05)……………………………………………….70
Table 3.8. Mean (+ 1 standard deviation) importance percentage of functional life-form
guilds for riparian areas adjacent to row crop, grassed pasture, wooded pasture, row
crop/grassed pasture, and lawn/wooded land uses for the South Fork subwatershed in the
Sugar Creek watershed in northeastern Ohio. An asterisk indicates a significant
difference among the riparian types (Kruskall-Wallis; P < 0.05)………………...……...71
Table 3.9. Native or non-native species status, conservation status, and rounded I-Rank
determined from the NatureServe explorer online database for riparian areas adjacent to
various land uses for the Upper subwatershed in the Sugar Creek watershed in
northeastern Ohio……………………………………………………………...…………72
Table 3.10. Native or non-native species status, conservation status, and rounded I-Rank
determined from the NatureServe explorer online database for riparian areas adjacent to
various land uses for the North Fork subwatershed in the Sugar Creek watershed in
northeastern Ohio……………………………………………………………...…………73
Table 3.11. Native or non-native species status, conservation status, and rounded I-Rank
determined from the NatureServe explorer online database for riparian areas adjacent to
various land uses for the South Fork subwatershed in the Sugar Creek watershed in
northeastern Ohio……………………………………………………………..…………74
Table 4.1. Mean (+ 1 standard deviation) entrenchment ratio, width-to-depth ratio, and
percent slope for riparian areas adjacent to various land uses for stream reaches in the
Upper, North Fork, and South Fork subwatersheds in the Sugar Creek watershed in
northeastern Ohio. An asterisk after a value indicates the stream characteristic was
significantly different among riparian areas adjacent to various land uses (KruskallWallis test; * P < 0.05)…………………………………………………………………105
Table 4.2. Soil characteristics for riparian areas adjacent to various land uses in the
Upper, North Fork, and South Fork subwatersheds in the Sugar Creek watershed in
northeastern Ohio………………………………………………………………………106
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Table 4.3. Mean (+ 1 standard deviation) diameter at breast height (dbh; centimeters) for
riparian areas adjacent to various land uses for overstory riparian vegetation in the Upper,
North Fork, and South Fork subwatersheds in the Sugar Creek watershed in northeastern
Ohio. An asterisk indicates the values for the structural characteristic were significantly
different for a riparian area adjacent to a particular land use
(Kruskall-Wallis; * P < 0.05)……………………………………………………….….107
Table 4.4. Mean (+ 1 standard deviation) importance value of riparian overstory (stems >
10 cm diameter at breast height) species for riparian areas adjacent to various land uses in
the Upper, North Fork, and South Fork, located in the Sugar Creek watershed in
northeastern Ohio. An asterisk after a value indicates the species was an indicator of a
riparian area adjacent to a particular land use (Monte-Carlo test; * P < 0.05)…….…...108
Table 4.5. Relative abundance (%) (+ 1 standard deviation) of understory (stems <10 cm
diameter at breast height; > 1-m in height) species, understory species richness and mean
(+ 1 standard error) number of saplings per hectare for riparian areas adjacent to various
land uses for the Upper, North Fork, and South Fork subwatersheds, located in the Sugar
Creek watershed in northeastern Ohio. An asterisk indicates a significant difference
among riparian areas adjacent to various land uses (Monte-Carlo test; * P < 0.05)…..110
Table 4.6. Results of canonical correspondence analyses (CCA) for relating overstory
importance values to environmental characteristics in the Upper, North Fork, and South
Fork subwatersheds, located in the Sugar Creek watershed in northeastern Ohio….…111
xii
LIST OF FIGURES
Figure 1.1. Location of the Sugar Creek watershed in Ohio. Source: Ohio Environmental
Protection Agency (2002)………………………………………………………………..10
Figure 1.2. Subwatersheds in the Sugar Creek watershed in northeastern Ohio. Source:
Ohio Environmental Protection Agency (2002)…………………………………………11
Figure 2.1. Sub-basins in the South Fork subwatershed within the Sugar Creek watershed
in northeastern Ohio. Numbers on map correspond to water collection points. Source:
Holmes et al. (unpublished data)……………………………………………………...…31
Figure 2.2. Land use designations for the South Fork subwatershed within the Sugar
Creek watershed in northeastern Ohio. Numbers on map correspond to water collection
points. Source: Holmes et al. (unpublished data)…………………………………..……32
Figure 2.3. Redundancy analysis relating land use and water quality parameters for the
South Fork subwatershed within the Sugar Creek watershed in northeastern Ohio. Circles
correspond to sub-basins: sub-basin 52 (1), sub-basin 53 (2), sub-basin 54 (3), sub-basin
55 (4), sub-basin 56 (5), sub-basin 57 (6), sub-basin 58 (7), sub-basin 59 (8), sub-basin 60
(9), and sub-basin 62 (10). Blue lines correspond to water quality parameters: dissolved
oxygen (DO), temperature (Temperat), pH, nitrate, ammonia, phosphorus (Phosph),
conductivity (Conducti), turbidity (Turbidit), and total solids (Total So). Red lines
correspond to land use types: green buffers (Buffer), wooded (Wood), urban, wetland,
barren, shrub, open water (Water), agriculture (Ag)………………………………….…33
Figure 3.1. Mean (+ 1 standard deviation) richness values for riparian areas adjacent to
various land uses associated with the Upper, North Fork, and South Fork subwatersheds
in the Sugar Creek watershed in northeastern Ohio………………………………..……75
Figure 3.2. Mean (+ 1 standard deviation) Shannon’s Index values for riparian areas
adjacent to various land uses associated with the Upper, North Fork, and South Fork
subwatersheds in the Sugar Creek watershed in northeastern Ohio…………….………76
xiii
Figure 3.3. Figure 3.3. Mean (+ 1 standard deviation) evenness values for riparian areas
adjacent to various land uses associated with the Upper, North Fork, and South Fork
subwatersheds in the Sugar Creek watershed in northeastern Ohio…………..…………77
Figure 4.1. Mean canopy openness (%) (+ 1 standard deviation) for riparian areas
adjacent to various land uses for overstory riparian vegetation in the Upper, North Fork,
and South Fork subwatersheds in the Sugar Creek watershed in northeastern Ohio…...112
Figure 4.2. Count of trees in each crown class category in the overstory vegetation layer
in riparian areas for riparian areas adjacent to various land uses in the Upper, North Fork,
and South Fork subwatersheds in the Sugar Creek watershed in northeastern Ohio..….113
Figure 4.3. Average age (+ 1 standard deviation) of cored overstory riparian trees for
riparian areas adjacent to various land uses in the Upper, North Fork, and South Fork
subwatersheds, located in the Sugar Creek watershed in northeastern Ohio…………...114
Figure 4.4. Canonical correspondence analysis (CCA) biplot relating mean riparian
overstory species importance values to environmental characteristics in the Upper, North
Fork, and South Fork subwatersheds, located in the Sugar Creek watershed in
northeastern Ohio. Red lines indicate environmental variables: row crop (crop), lawn
(lawn), grassed pasture (g past), wooded pasture (w past), wooded (wood), row crop/lawn
(crop/law), row crop/grassed pasture (crop/ g p), lawn/wooded (lawn/woo), alluvium
(alluvium), glacial deposition (glacial), silt loam (silt loa), fine loamy to silt loam (fine
loa), sandy to silt loam (sandy/si), sandy loam (sandy lo), entrenchment ratio (Entrench),
width-to-depth ratio (W/D Rati), and percent slope (Slope). Triangles indicate tree-size
species. Overstory species presented in Appendix B………………………………..….115
Figure 4.5. Canonical correspondence analysis (CCA) biplot relating sampling locations
to environmental characteristics in the Upper, North Fork, and South Fork subwatersheds,
located in the Sugar Creek watershed in northeastern Ohio. Circles refer to sampling
locations: 1 is S5A, 2 is S1D, 3 is U8C, 4 is U8B, 5 is U8A, 6 is U20B, 7 is S2A, 8 is
S1B, 9 is S1A, 10 is N3, 11 is N10, 12 is S4A, 13 is N11, 14 is U24D, 15 is U24C, 16 is
U20D, 17 is U20A, 18 is U8D, 19 is N1, 20 is S3A, 21 is S2B, 22 is S6B, and 23 is S6A.
Red lines indicate environmental variables: row crop (crop), lawn (lawn), grassed pasture
(g past), wooded pasture (w past), wooded (wood), row crop/lawn (crop/law), row
crop/grassed pasture (crop/ g p), lawn/wooded (lawn/woo), alluvium (alluvium), glacial
deposition (glacial), silt loam (silt loa), fine loamy to silt loam (fine loa), sandy to silt
loam (sandy/si), sandy loam (sandy lo), entrenchment ratio (Entrench), width-to-depth
ratio (W/D Rati), and percent slope (Slope)……………………………………...…….116
xiv
CHAPTER 1
INTRODUCTION
Riparian areas, i.e., ecotones between terrestrial and aquatic ecosystems, provide
many important ecosystem functions (Gregory et al. 1991; Molles 2002; Mitsch and
Gosselink 2007). These functions include buffering the stream from nutrient and
sediment loading, regulating water flow, reducing stream-bank erosion, contributing
organic matter to the stream, providing shade, and supplying habitat for aquatic
organisms (Vannote et al. 1980; Naiman et al. 1988; Gregory et al. 1991; Osborne and
Kovacic 1993; Sweeney et al. 2002; Tockner and Stanford 2002). Due to their
importance, intact riparian areas are essential for diminishing negative impacts of land
use practices on streams (Mitsch et al. 2001; Mitsch and Day 2006). However, many of
these areas are in a degraded state or have been converted to a different land use (Naiman
et al. 1993; Tockner and Stanford 2002), which has altered aquatic conditions (e.g.,
Carpenter et al. 1998; Stauffer et al. 2000; Shandas and Alberti 2009).
According to a report by the United States Environmental Protection Agency
(2009), 44 percent of the nation’s rivers and streams are impaired, with agriculture as the
leading source of impairment. In many instances, riparian vegetation has been removed
along streams and rivers in agricultural landscapes, for purposes such as grazing
livestock, providing water sources for livestock, and growing crops. Riparian vegetation
1
removal has many effects, including altering stream habitat through a reduction of large
wood inputs and shade, and an increase in nutrient loading, stream-bank erosion, and
sedimentation (Sponseller et al. 2001). These changes in habitat have a negative impact
on the aquatic life, e.g., by covering spawning areas with sediments, increasing stream
temperature and productivity, and decreasing dissolved oxygen (Allan et al. 1997;
Stauffer et al. 2000). Specifically, nutrients, including nitrate-nitrogen (NO 3 -N) and
soluble reactive phosphorus (PO4 -P), have also increased in aquatic systems due to
agricultural practices, resulting in seasonal algal blooms that decrease levels of dissolved
oxygen (Carpenter et al. 1998; Sponseller et al. 2001). Many sensitive aquatic organisms
cannot survive these conditions (Carpenter et al. 1998; Sponseller et al. 2001).
While current land management practices have resulted in significant declines in
water quality across the Midwestern United States, agricultural activities have also been
implicated as contributing factors in the growing hypoxia zone in the Gulf of Mexico
(Turner and Rabalais 1991; Mitsch et al. 2001; Mitsch and Day 2006; Rabalais et al.
1996). Although the excessive nutrient loading from the direct application of fertilizers
and poor manure management practices are the primary causes of the hypoxia zone, the
lack of properly functioning riparian vegetation along streams and rivers contributes
significantly to the problem (Turner and Rabalais 1991; Mitsch et al. 2001; Mitsch and
Day 2006).
This is particularly true for the Ohio River basin. The Ohio River contributes 34
percent of the total nitrogen entering the Gulf of Mexico, and most of these nutrients
originate in agricultural watersheds in the headwaters (Mitsch and Day 2006). One such
watershed, in northeastern Ohio, is the Sugar Creek watershed, which drains 925 km 2 in
2
Wayne, Stark, Holmes, and Tuscarawas Counties (Ohio Environmental Protection
Agency 2002). This watershed is dominated (> 70 percent) by agricultural land uses and,
primarily due to current land use practices, is considered the second most degraded
watershed in Ohio (Ohio Environmental Protection Agency 2002). However, restoration
potential is high due to the sources of degradation within the watershed, i.e., sediment
and nutrient loading and in-stream habitat change (Ohio Environmental Protection
Agency 2002). Restoring riparian areas in this and other agricultural watersheds in the
Midwestern United States can help alleviate the hypoxia zone in the Gulf of Mexico
(Mitsch and Day 2006). Specifically, an area the size of the state of West Virginia need to
be restored to reduce the excess nutrient loadings originating from agricultural activities
(Mitsch et al. 2001).
Improving water quality in agricultural watersheds can be achieved through a
variety of approaches. First, improving agricultural management practices, e.g., using soil
tests for determining the correct amount of fertilizer to apply, planting perennial crops,
and improving manure management, would decrease nutrients which eventually enter
stream systems (e.g., Osborne and Kovacik 1993; Carpenter et al. 1998; Mitsch et al.
2001). Second, restoring riparian forests will help improve habitat conditions by
improving overall ecosystem function, including increased buffering capacities of
streams following nutrient inputs (Lowrance et al. 1984; Naiman et al. 1988; Gregory et
al. 1991; Osborne and Kovacic 1993).
Several studies have verified the importance of riparian forests in reducing the
amount of nutrients to streams. A study in Maryland found that riparian forests retain
about 90 percent of the nitrogen and 80 percent of the phosphorus flowing through the
3
area, while agricultural lands only retain 8 percent of the nitrogen and 41 percent of the
phosphorus (Peterjohn and Correll 1984). Using a model of Wisconsin streams, Diebel et
al. (2009) determined that riparian areas could reduce 70 percent of the sediment and
phosphorus loading into streams. Holmes (2004) found that riparian areas in the Sugar
Creek watershed function to buffer nutrients from the stream.
Although riparian forests serve important functions, nitrogen saturation and
subsurface tile drainage in agricultural watersheds have rendered riparian vegetation less
effective for buffering nitrogen. Particularly, high nitrogen input from agricultural fields
in the headwaters of the Sugar Creek watershed has led to nitrogen saturation (Herrman
et al. 2008a; Herrman et al. 2008b). Herrman et al. (2008a) found that because of this
saturation, the headwaters are ineffective at removing in-stream nitrogen. Many
agricultural fields are also drained directly to the stream with tiles (Ohio Environmental
Protection Agency 2002). Tile drainage has increased the amount of water, and
subsequently nutrients, entering the stream system, and contributes to a decrease in water
quality (Tomer et al. 2003). Agricultural fields, when drained, contribute the highest
nitrate concentrations to streams when plants are not growing (Cambardella et al. 1999).
Because tile drainage and nitrogen saturation have reduced the effectiveness of riparian
areas to buffer nitrogen, it is important to reduce nitrogen inputs at the source (i.e.,
agricultural fields) (Osborne and Kovacic 1993; Tomer et al. 2003; Herrman et al.
2008b).
Since 2000, researchers at the Ohio Agricultural Research and Development
Center (OARDC) of The Ohio State University and at other universities, managers with
various governmental agencies, local watershed groups, and landowners in the Sugar
4
Creek watershed have been working together to improve agricultural practices and
restore riparian habitat, e.g., livestock exclusion from riparian areas and streams (Ohio
Environmental Protection Agency 2002). These efforts have resulted in significant
improvement in water quality primarily through better agricultural practices. Water
quality issues in the Sugar Creek watershed are representative of agricultural watersheds
throughout the Midwest, and as a result, this project has become a national model for
building “local community capacity” for watershed management and restoration (Ohio
Environmental Protection Agency 2002).
As this watershed is a model for restoration, and nutrient loading is a significant
problem causing water quality issues that can be reduced with riparian forests, baseline
conditions must be identified. These baseline conditions can be used for evaluating the
effectiveness of restoration efforts. However, little information is available on the current
riparian vegetation and factors that regulate the development of riparian vegetation in the
watershed, as well as how land use practices affect riparian plant communities. Most
research to date has focused on water quality and stream habitat at individual-reach scales
(Herrman et al. 2008a; D’Ambrosio et al. 2009), although some studies have focused on
watershed-scale land uses and its effect on water quality (Holmes 2004; Prasad et al.
2005; Herrman et al. 2008b).
The overall goal of this study was to examine how adjacent land use practices and
environmental factors influence the composition, structure, and diversity of riparian areas
in the Sugar Creek watershed. Specifically, in chapter 2 we examine how water quality is
related to land use in the South Fork subwatershed of the Sugar Creek watershed. In
Chapter 3 we examine patterns of ground-flora composition, structure, and diversity
5
across three subwatersheds in the Sugar Creek watershed and relate these patterns to
different adjacent land uses. Finally, we focus on the composition and structure of woody
riparian vegetation, and examine how physiographic factors and adjacent land use
influence these riparian plant communities across the Sugar Creek watershed in chapter
4.
6
LITERATURE CITED
Allan, J.D., D.L. Erickson, and J. Fay. 1997. The influence of catchment land-use on
stream integrity across multiple spatial scales. Freshwater Biology 37: 149-161.
Cambardella, C.A., T.B. Moorman, D.B. Jaynes, J.L. Hatfield, T.B. Parkin, W.W.
Simpkins, and D.L. Karlen. 1999. Water quality in Walnut Creek watershed:
nitrate-nitrogen in soils, subsurface drainage water, and shallow groundwater.
Journal of Environmental Quality 28: 25-34.
Carpenter, S.R., N.F. Caraco, D.L. Correll, R.W. Howarth, A.N. Sharpley, and V.H.
Smith. 1998. Nonpoint pollution of surface waters with phosphorus and nitrogen.
Ecological Applications 8: 559-568.
D’Ambrosio, J.L., L.R. Williams, J.D. Witter, and A. Ward. 2009. Effects of
geomorphology, habitat, and spatial location on fish assemblages in a watershed
in Ohio, USA. Environmental Monitoring and Assessment 148: 325-341.
Diebel, M.W., J.T. Maxted, D.M. Robertson, S. Han, and M.J. Vander Zanden. 2009.
Landscape planning for agricultural nonpoint source pollution reduction III:
assessing phosphorus and sediment reduction potential. Environmental
Management 43: 69-83.
Gregory, S.V., F.J. Swanson, W.A. McKee, and K.W. Cummins. 1991. An ecosystem
perspective of riparian zones. BioScience 41: 540-551.
Herrman, K.S., V. Bouchard, and R.H. Moore. 2008a. Factors affecting denitrification in
agricultural headwater streams in Northeast Ohio, USA. Hydrobiologia 598: 305314.
Herrman, K.S., V. Bouchard, and R.H. Moore. 2008b. An assessment of nitrogen
removal from headwater streams in an agricultural watershed, northeast Ohio,
U.S.A. Limnology and Oceanography 53: 2573-2582.
Holmes, K.L. 2004. Landscape characteristics and their relationship to water-quality in
the headwaters of a northeast Ohio watershed. M.S. Thesis. The Ohio State
University, Columbus, OH.
Lowrance, R., R. Todd, J. Fail, Jr., O. Hendrickson, Jr., R. Leonard, and L. Asmussen.
1984. Riparian forests as nutrient filters in agricultural watersheds. BioScience
34: 374-377.
7
Mitsch, W.J., and J.W. Day, Jr. 2006. Restoration of wetlands in the Mississippi-OhioMissouri (MOM) River Basin: experience and needed research. Ecological
Engineering 26: 55-69.
Mitsch, W.J., J.W. Day, Jr., J.W. Gilliam, P.M. Groffman, D.L. Hey, G.W. Randall, and
N. Wang. 2001. Reducing nitrogen loading to the Gulf of Mexico from the
Mississippi River Basin: strategies to counter a persistent ecological problem.
BioScience 51: 373-388.
Mitsch, W.J., and J.G. Gosselink. 2007. Wetlands. 4th ed. John Wiley & Sons, Inc.
Hoboken, NJ. 582 pp.
Molles, Jr., M.C. 2002. Ecology: concepts and applications. 2nd ed. McGraw-Hill, Inc.
New York, NY. 586 pp.
Naiman, R.J., H. Decamps, J. Pastor, and C.A. Johnston. 1988. The potential importance
of boundaries of fluvial ecosystems. Journal of the North American Benthological
Society 7: 289-306.
Naiman, R.J., H. Decamps, and M. Pollock. 1993. The role of riparian corridors in
maintaining regional biodiversity. Ecological Applications 3: 209-212.
Ohio Environmental Protection Agency. 2002. Total maximum daily loads for the Sugar
Creek Basin, final report. Division of Surface Water, Columbus, OH.
Osborne, L.L., and D.A. Kovacic. 1993. Riparian vegetated buffer strips in water-quality
restoration and stream management. Freshwater Biology 29: 243-258.
Peterjohn, W.T., and D.L. Correll. 1984. Nutrient dynamics in an agricultural watershed.
Ecology 65: 1466-1475.
Prasad, K., B.R. Stinner, A. Ortiz, D. McCartney, and R.H. Moore. 2005. Exploring the
relationship between hydrologic parameters and nutrient loads using digital
elevation model and GIS – a case study from Sugarcreek headwaters, Ohio, USA.
Environmental Monitoring and Assessment 110: 141-169.
Rabalais, N.N., R.E. Turner, D. Justic, Q. Dortch, W.J. Wiseman, Jr., and B.K. Sen
Gupta. 1996. Nutrient changes in the Mississippi River and system responses on
the adjacent continental shelf. Estuaries 19: 386-407.
Shandas, V., and M. Alberti. 2009. Exploring the role of vegetation fragmentation on
aquatic conditions: linking upland with riparian areas in Puget Sound lowland
streams. Landscape and Urban Planning 90: 66-75.
8
Sponseller, R.A., E.F. Benfield, and H.M. Valett. 2001. Relationships between land-use,
spatial scale, and stream macroinvertebrates. Freshwater Biology 46: 1409-1424.
Stauffer, J.C., R.M. Goldstein, and R.M. Newman. 2000. Relationship of wooded riparian
zones and runoff potential to fish community composition in agricultural streams.
Canadian Journal of Fisheries and Aquatic Sciences 57: 307-316.
Sweeney, B.W., S.J. Czapka, and T. Yerkes. 2002. Riparian forest restoration: increasing
success by reducing plant competition and herbivory. Restoration Ecology 10:
392-400.
Tockner, K., and J.A. Stanford. 2002. Riverine floodplains: Present state and future
trends. Environmental Conservation 29: 308-330.
Tomer, M.D., D.W. Meek, D.B. Jaynes, and J.L. Hatfield. 2003. Evaluation of nitrate
nitrogen fluxes from a tile-drained watershed in central Iowa. Journal of
Environmental Quality 32: 642-653.
Turner, R.E., and N.N. Rabalais. 1991. Changes in Mississippi River water-quality this
century. BioScience 41: 140-147.
United States Environmental Protection Agency. 2009. National water-quality inventory:
report to congress. United States Environmental Protection Agency, Office of
Water. Washington, D.C.
Vannote, R.L., G.W. Minshall, K.W. Cummins, J.R. Sedell, and C.E. Cushing. 1980. The
river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37:
130-137.
9
Figure. 1.1. Location of the Sugar Creek watershed in Ohio. Source: Ohio Environmental
Protection Agency (2002).
10
Figure 1.2. Subwatersheds in the Sugar Creek watershed in northeastern Ohio. Source:
Ohio Environmental Protection Agency (2002).
11
CHAPTER 2
RELATING LAND USE AND WATER QUALITY IN AN AGRICULTURAL
WATERSHED IN NORTHEASTERN OHIO
Introduction
According to the United States Environmental Protection Agency (2009), 44
percent of the nation’s rivers and streams are impaired, primarily because of agriculture.
Agricultural practices which lead to impairment include allowing livestock free access to
streams, excessive fertilizer use, spreading more manure than is necessary for plant
growth or on frozen ground, and using conventional tillage practices (e.g., Carpenter et
al. 1998). Another important cause of water quality impairment is urbanization (United
States Environmental Protection Agency 2009). A main issue associated with an urban
land use is the large amount of surface runoff associated with impervious surfaces
(Mitsch and Day 2006; United States Environmental Protection Agency 2009).
These non-point source pollution sources lead to issues for both aquatic and
terrestrial organisms (Carpenter et al. 1998; United States Environmental Protection
Agency 2009). There are many consequences in the stream ecosystem because of the
increased nutrient and sediment loadings (Carpenter et al. 1998; Mitsch et al. 2001;
12
Herrman et al. 2008a; Herrman et al. 2008b). More nutrients enhance primary production,
thereby increasing the occurrence of algal blooms and causing a decrease in dissolved
oxygen, which leads to fish kills and overall habitat degradation (e.g., Carpenter et al.
1998; Sponseller et al. 2001). Allowing livestock free access to streams also causes
stream-bank erosion. The resultant siltation may also increase nutrient loading in streams
when phosphorus is attached to soil particles (Carpenter et al. 1998). Siltation decreases
the suitability for aquatic organisms through increasing turbidity and covering spawning
stream habitat with sediment (Allan et al. 1997; Stauffer et al. 2000). All of these
conditions create an environment which is suitable only for aquatic organisms that can
survive in poor quality habitat (Allan et al. 1997; Carpenter et al. 1998; Stauffer et al.
2000; Sponseller et al. 2001).
Nutrients in the water supply also affect human health. High nitrate levels in
drinking water have been linked to non-Hodgkin’s lymphoma (Ward et al. 1996).
Livestock use of streams has caused bacterial issues, e.g., E. coli, which can cause
livestock health issues, including colitis (Wang et al. 1996). E. coli survives in cattle fecal
matter, which leads to water source contamination (Wang et al. 1996).
The effects of land-use practices also impact downstream environments. Recently,
increased attention has focused on the approximately two-million hectare hypoxia zone in
the Gulf of Mexico (Turner and Rabalais 1994; Mitsch and Gosselink 2007). The low
oxygen levels caused by an increase in nutrient concentrations have resulted in low
densities of fish and invertebrates in the hypoxia zone (Turner and Rabalais 1994). The
water quality degradation has been attributed to poor land use practices in the
Mississippi-Ohio-Missouri River Basin, which drains into the Gulf of Mexico (Turner
13
and Rabalais 1994; Mitsch and Gosselink 2007). Approximately 90 percent of the nitratenitrogen input into the Gulf of Mexico can be attributed to the Ohio, Missouri, and upper
Mississippi Rivers (Mitsch and Day 2006). The Ohio River is estimated to contribute 34
percent of the total nitrogen entering the Gulf of Mexico, and most of these nutrients
originate in agricultural watersheds in the headwaters (Mitsch and Day 2006). One such
watershed is the Sugar Creek watershed located in northeastern Ohio, which drains 925
km2 in Wayne, Stark, Holmes, and Tuscarawas Counties (Ohio Environmental Protection
Agency 2002). This watershed is dominated (> 70 percent) by agricultural land uses and,
primarily due to current land-use practices, is considered the second most degraded
watershed in Ohio (Ohio Environmental Protection Agency 2002). However, restoration
potential is high due to the sources of degradation within the watershed, i.e., sediment
and nutrient loading and in-stream habitat change (Ohio Environmental Protection
Agency 2002).
Recently, the contribution of headwater basins to overall water quality and
watershed impairment has been highlighted, as well as the potential restoration efforts in
these headwater basins may have on water quality locally and regionally (Mitsch et al.
2001; Peterson et al. 2001; Meyer et al. 2007). Water-quality issues and land-use
practices in the Sugar Creek watershed are similar throughout other agricultural
watersheds in the Midwest, so this project has become a national model for building
“local community capacity” for watershed management and restoration (Ohio
Environmental Protection Agency 2002).
With the growing concern about water degradation, focus needs to be placed on
finding and improving upon those land uses practices which contribute most to the
14
degradation for management purposes. Recent efforts have explored the influence of land
use on water quality in portions of the Sugar Creek watershed (Holmes 2004). The
specific objective of this study was to continue these efforts through examining how land
use influences water quality parameters, i.e., total solids, phosphorus, ammonia, nitrate,
pH, temperature, conductivity, dissolved oxygen, and turbidity, in the South Fork
subwatershed of the Sugar Creek watershed in northeastern Ohio.
Study Area
We focused our efforts on one of the subwatersheds in the Sugar Creek
watershed. The South Fork subwatershed (hydrologic unit code (HUC) 05-040001-110)
is a lower-stem tributary located in Holmes and Tuscarawas Counties that drains 356.9
km2 of land (Ohio Environmental Protection Agency 2002). Previous efforts have
focused on other subwatersheds in the Sugar Creek watershed, including the Upper
subwatershed (Holmes 2004) and the North Fork subwatershed (Holmes et al.,
unpublished data). This subwatershed is located in the unglaciated Western Allegheny
Plateau Ecoregion (United States Environmental Protection Agency 2007), and is
characterized by steeper slopes and higher stream gradients than the Upper and North
Fork subwatersheds (Ohio Environmental Protection Agency 2002). Due to the
limitations of steep terrain on row-crop agriculture, there are many small-scale dairy
operations in this subwatershed. Amish communities operate many of these operations,
which are typically smaller farms and have more mixed agricultural practices, i.e., both
crop and animal production on the same farm (Parker 2006).
15
Holmes County has a mean summer temperature of 21.1°C and a mean winter
temperature of -1.7°C (Seaholm and Graham 1997). Of the average annual precipitation
(94 cm), 60 percent falls between April and September (Seaholm and Graham 1997).
Tuscarawas County’s mean summer temperature is 21.1°C, and the mean winter
temperature is -2.8°C (Waters and Roth 1986). This county receives more precipitation,
on average, than Holmes County, with 55 percent of the 98.3 cm falling between April
and September (Waters and Roth 1986).
Methods
Data Collection
In 2003, as part of a larger effort to monitor water quality in the Sugar Creek
watershed, ten sampling locations were established in the South Fork subwatershed
(Figure 2.1). Water samples were collected bimonthly from 2003-2006 by researchers
associated with the Sugar Creek Project at the Ohio Agricultural Research and
Development Center (OARDC) when the streamflow was adequate to collect samples
and the stream was ice-free. In the field, a Quanta field probe was used to estimate water
quality parameters including: total solids, phosphorus, ammonia, nitrate, pH, temperature,
conductivity, dissolved oxygen (DO), and turbidity.
Land Use
Using a geographic information system (GIS), data layers were created to
examine land use in the South Fork subwatershed (Figure 2.2). Land-cover data was
16
obtained from the Ohio Department of Natural Resources, utilizing a remotely sensed
dataset of land-use based upon a 1994 classification. Land-use types were urban,
agriculture, shrub, wooded, open water, wetland, or barren.
To determine the area upstream from each of the ten sampling locations, subbasins were delineated using GIS and HEC-GeoHMS v1.0 (U.S. Army Corps of
Engineers). HEC-GeoHMS is a GIS extension that models watershed geospatial
hydrology. ATtILA, Analytical Tools Interface for Landscape Assessments, is an
ArcView extension that generates landscape metric calculations. GIS and ATtILA v3.0
were used to determine the percent cover of each land use within each sub-basin. To
obtain percentage of buffer strips adjacent to Sugar Creek, a 50-ft buffer was created
around Sugar Creek in the South Fork subwatershed and the riparian areas were clipped
using ArcGIS (ESRI, Redlands, CA).
Data Analyses
We transformed the land-use data prior to analysis using an arcsine
transformation to normalize the data. We then performed detrended canonical
correspondence analysis (DCCA) using CANOCO software (Version 4.53 BiometrisPlant Research International, Wageningen, The Netherlands) to determine if canonical
correspondence analysis (CCA) or redundancy analysis (RDA) would be the more
appropriate analysis for the dataset. Typically, a gradient length below 3 in a DCCA
suggests that a redundancy analysis is a better choice than CCA (Leps and Smilauer
2003). From the results of the DCCA, we found the length of the gradient of our water
quality analysis to be 0.080, and, therefore, we conducted an RDA to examine the
17
influence of land use on water quality. Due to the wide range in the water quality
parameters, we used the centering and standardizing option in CANOCO.
Results
The most common land use for all sub-basins in the South Fork subwatershed is
agriculture, followed by wooded (Table 2.1). The remaining land uses (urban, shrubland,
open water, wetland, barren, and green buffer) occupy much smaller areas of each subbasin (Table 2.1). The sampling point from sub-basin 54, which has the least amounts of
agriculture, shrubland, and barren land and the highest areas of wooded land use and of
green buffers, had the lowest total solid, ammonia, nitrate, and conductivity values
(Tables 2.1 and 2.2). Sub-basin 56 has the highest area of agriculture land use as well as
highest average stream temperature of the sub-basins, but the lowest areas of wooded
land use and green buffers (Tables 2.1 and 2.2). Total solids, ammonia, conductivity, and
turbidity are highest in sub-basin 62, which also has the highest area of urbanization, but
the lowest DO value (Tables 2.1 and 2.2).
Of the variation in water quality parameters, 62.6 percent is explained by land use
along the first RDA axis, and an additional 21.5 percent is explained by the second RDA
axis. The RDA shows definite patterns among land uses and water quality parameters
(Figure 2.3). Positively associated with axis 1 were the urban, wetland, and barren land
uses, and total solids, phosphorus, turbidity, conductivity, and ammonia (Figure 2.3).
Two water-quality parameters were negatively associated with axis 1: DO and
temperature (Figure 2.3). Total solids were positively associated with urban areas,
18
whereas DO and temperature were negatively associated with urban areas (Figure 2.3).
We determined axis 1 to be a gradient of urbanization because of these associations.
Lower temperatures and higher DO are associated with less urbanized subwatersheds. On
the other end of the gradient, where higher urbanization levels are found in the subbasins, total solids are higher. Phosphorus, turbidity, and conductivity and the wetland
land-use type are associated with urban land use; ammonia is also related to this land use
(Figure 2.3).
Positively associated with axis 2 were green buffers and wooded land use (Figure
2.3). Negatively associated with this axis were agriculture, open water, and shrub, and pH
and nitrate (Figure 2.3). Because of these associations, we determined axis 2 is a gradient
of basins dominated by agriculture to more wooded land uses (i.e., more natural habitats).
Increased levels of nitrate and pH are associated with increased levels of agricultural uses
in the watershed. The shrub land use was found to be highly correlated to increased levels
of nitrate. Phosphorous, turbidity, conductivity, and wetland are not closely associated
with agriculture because they are associated more closely with the urbanization gradient
(Figure 2.3).
All but two sub-basins have similar characteristics (Figure 2.3). Sub-basin 54 is
associated with more wooded land use (Figure 2.3) and this sub-basin has the highest
percentage of both wooded areas and buffer strips (Table 2.1); therefore, this sub-basin
may be contributing little nitrate and pH changes in Sugar Creek. Sub-basin 62 was
associated with urban areas (Figure 2.3), and this sub-basin has the highest percentage of
urban areas of the sub-basins (Table 2.1).
19
Discussion
The results of the RDA show definite patterns among land uses and water quality
parameters in the South Fork subwatershed of the Sugar Creek watershed. Our results
suggest that urban areas are having more impact on water quality than the other land uses
examined. Although agriculture is the dominant land use in the Sugar Creek watershed,
we found urban areas to be highly associated with more water quality parameters. Other
studies have found the opposite to be true, in that agricultural areas typically have a
greater effect on water quality than urban areas (Allan et al. 1997; Mitsch et al. 2001;
Holmes 2004; United States Environmental Protection Agency 2009). Developed areas
have many impervious surfaces, e.g., parking lots and roads, which do not allow water
infiltration (Mitsch et al. 2001), and pollutants and sediments carried in the water are then
transported to the stream (Carpenter et al. 1998).
Many homes in the South Fork subwatershed are older farmsteads and rural
houses (Ohio Environmental Protection Agency 2002). The older homes may have older
septic systems, and if failing, contribute to phosphorus loadings in Sugar Creek (Ohio
Environmental Protection Agency 2002). Many urban areas are located adjacent to Sugar
Creek; therefore, the stream directly receives runoff from urban areas.
Although sub-basins with higher areas of urban land use were associated with
total solids, turbidity, phosphorus, conductivity, and ammonia, agriculture was associated
with an important nutrient, i.e., nitrate-nitrogen, which causes human health issues (Ward
et al. 1996) and impairs aquatic habitat (Allan et al. 1997; Carpenter et al. 1998; Stauffer
et al. 2000; Sponseller et al. 2001). Nitrate-nitrogen concentrations are especially
20
important because Sugar Creek is one of the headwaters contributing water which
eventually reaches the Gulf of Mexico, thus contributing to the hypoxic zone (Mitsch et
al. 2001; Mitsch and Day 2006).
In a similar study in the headwaters of the Sugar Creek watershed, Holmes (2004)
found phosphorus associated with agriculture; however, we found phosphorus associated
with urbanization. In the headwaters, total solids were positively associated with wooded
land use, which is probably because wooded land uses are not riparian buffers but are
located near development (Holmes 2004). We found total solids associated with
urbanization, which is likely related to erosion and subsequent runoff from urban areas
(Carpenter et al. 1998). Dissimilarities between the two studies are most likely due to
geologic and management differences between the two subwatersheds. The headwaters of
Sugar Creek are located in the glaciated portion of Ohio, whereas the South Fork is
unglaciated (Ohio Environmental Protection Agency 2002). In part due to these
physiographic differences, there are more dairy operations in the South Fork than
traditional row crop agriculture because of the steeper topography. The social aspect is
important to consider because of different management techniques. Conventional
agriculture is practiced in the headwaters, whereas agriculture in the South Fork is
operated by Amish communities (Parker 2006).
It is surprising the RDA showed nitrate is more associated with a shrub land use
than agricultural land use because nitrate and agriculture are typically highly related
(Mitsch et al. 2001; Holmes 2004; Mitsch and Day 2006). This could be an artifact of the
scale of the GIS land cover layer used for this analysis. As the ODNR GIS data layer was
collected in 1994, a more recent data layer is needed for better accuracy.
21
High DO and low temperatures within streams are typically linked with properly
functioning riparian areas (Gregory et al. 1991); however, results from the RDA showed
DO and temperature were negatively associated with urban areas. Less than one percent
of the South Fork has a buffer strip associated with Sugar Creek (Table 2.1). There are
few green buffers to shade the stream which results in lower stream temperatures and
higher dissolved oxygen content (e.g., Gregory et al. 1991; Osborne and Kovacic 1993).
Management Implications
As land-use practices and water quality issues are similar throughout the Midwest,
the Sugar Creek watershed has become a model for restoration efforts (Ohio
Environmental Protection Agency 2002). Holmes (2004) found that for two and a half
years studied, water quality of most subwatersheds in the headwaters of Sugar Creek
exceeded Ohio Environmental Protection Agency standards for water quality. Water
quality improvement has occurred, in part because of the implementation of conservation
practices such as installing stream exclusion fences free of charge for farmers (Ohio
Environmental Protection Agency 2002).
Other management practices should be implemented to improve the water quality
of Sugar Creek. As urbanization is a contributor to water quality degradation (United
States Environmental Protection Agency 2009), better management practices should be
emphasized in urban areas in the South Fork. High erosion rates at construction sites are a
large source of urban non-point source pollution (Carpenter et al. 1998). Sediment
reduction can be accomplished through the use of silt fences (Chiras et al. 2002). Another
solution to reduce the effect of urbanization is to construct wetlands within urban areas to
22
capture flow from impervious surfaces (Carpenter et al. 1998; Mitsch and Jorgensen
2004). Runoff is held until sediment drops out of suspension and nutrient loads are
reduced, resulting in cleaner water entering Sugar Creek (Mitsch and Jorgensen 2004).
Because agriculture was positively associated with nitrate-nitrogen in the South
Fork subwatershed, better management practices can reduce nitrate-nitrogen loading. One
specific practice is applying only the amount of nutrients needed to grow crops (Mitsch et
al. 2001; Mitsch and Day 2006), and this can be accomplished through soil testing
(Prasad et al. 2005). Using soil tests for determining the correct amount of fertilizer and
manure to apply and planting perennial crops, would decrease nutrient sources which
eventually enter stream systems (e.g., Osborne and Kovacik 1993; Carpenter et al. 1998;
Mitsch et al. 2001).
It is also important to reduce nitrate-nitrogen sources at the source because of
nitrogen saturation (Tomer et al. 2003; Herrman et al. 2008a; Herrman et al. 2008b).
High nitrogen input in the headwaters of the Sugar Creek watershed has led to nitrogen
saturation (Herrman et al. 2008a; Herrman et al. 2008b). Herrman et al. (2008a) found
that because of this saturation, the headwaters are ineffective at removing in-stream
nitrogen. Herrman et al. (2008b) also examined denitrification rates and nitrate
concentrations in the Sugar Creek headwaters between streams with an agriculture
riparian land use and riparian forests. Nitrogen removal was low in streams adjacent to
both riparian land uses due to nitrogen saturation (Herrman et al. 2008b). In-stream
denitrification in the headwaters is therefore ineffective for reducing nitrogen
concentrations under these conditions (Herrman et al. 2008b).
23
As riparian areas provide buffers for streams from excessive nutrients and
sedimentation and reduce erosion, they are extremely important to stream and watershed
health (Gregory et al. 1991). In the South Fork subwatershed, few of these areas are
intact. Therefore, these buffers, which are negatively associated with nitrate levels,
should be placed along the stream to reduce nitrate loadings to Sugar Creek (Mitsch and
Day 2006). The ideal riparian areas would be a forested riparian area rather than simply
grassed buffer strips as riparian forests have been shown to remove more nitrogen than
grassed buffers (Osborne and Kovacic 1993). These riparian forests, however, are
ineffective for buffering nutrients and sediments when fields have modified hydrology
associated with tile drainage (Osborne and Kovacic 1993). Many agricultural fields in the
Sugar Creek watershed are drained directly to the stream with tiles (Ohio Environmental
Protection Agency 2002). Tile drainage increases the amount of water, and subsequently
nutrients, entering the stream system, and contributes to a decrease in water quality
(Tomer et al. 2003). Agricultural fields, which are drained, contribute the highest nitrate
concentrations to streams when plants are not growing (Cambardella et al. 1999).
Although riparian areas may be ineffective at removing nutrients from water that
is drained by tiles, riparian function is important to restore for nutrient removal from nonpoint source pollution. Mitsch et al. (2001) determined that if riparian areas are restored
throughout the Mississippi-Ohio-Missouri river basin, the hypoxic zone in the Gulf of
Mexico could be reduced. With water-quality improvement in headwater watersheds, we
can help reduce the impact of excessive nutrient loading downstream, i.e., in the Gulf of
Mexico (Mitsch et al. 2001; Mitsch and Day 2006).
24
Conclusion
Although a finer-scale, and more current, GIS data land cover layer is necessary,
the information gathered in this study is useful in understanding how specific land uses
are associated with certain water-quality parameters. Although we observed that urban
areas are associated with most of the negative aspects of the water-quality parameters
examined (total solids, turbidity, phosphorus, conductivity, ammonia, DO, and
temperature), agricultural areas are associated with higher pH and high concentrations of
nitrate. These relationships can form the basis for designing more in-depth analyses of
land-use influences on water quality and help guide future restoration efforts in the Sugar
Creek watershed.
25
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Mitsch, W.J., J.W. Day, Jr., J.W. Gilliam, D.L. Hey, G.W. Randall, and N. Wang. 2001.
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Mitsch, W.J., and J.G. Gosselink. 2007. Wetlands. John Wiley & Sons, Inc., Hoboken,
NJ. 582 pp.
Mitsch, W.J., and S.E. Jorgensen. 2004. Ecological engineering and ecosystem
restoration. John Wiley & Sons, Inc., Hoboken, NJ. 411 pp.
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University, Columbus, OH.
Peterson, B.J., W.M. Wollheim, P.J. Mulholland, J.R. Webster, J.L. Meyer, J.L. Tank, E.
Marti, W.B. Bowden, H.M. Valett, A.E. Hershey, W.H. McDowell, W.K. Dodds,
S.K. Hamilton, S. Gregory, D.D. Morrall. 2001. Control of nitrogen export from
watersheds by headwater streams. Science 292: 86-90.
Prasad, V.K., A. Ortiz, B. Stinner, D. McCartney, J. Parker, D. Hudgins, C. Hoy, and R.
Moore. 2005. Exploring the relationship between hydrologic parameters and
nutrient loads using digital elevation model and GIS – a case study from
Sugarcreek headwaters, Ohio, U.S.A. Environmental Monitoring and Assessment
110: 141-169.
Seaholm, J.E., and T.E. Graham. 1997. Soil survey of Holmes County, Ohio. United
States Department of Agriculture, Natural Resources Conservation Service.
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spatial scale, and stream macroinvertebrates. Freshwater Biology 46: 1409-1424.
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Tomer, M.D., D.W. Meek, D.B. Jaynes, and J.L. Hatfield. 2003. Evaluation of nitrate
nitrogen fluxes from a tile-drained watershed in central Iowa. Journal of
Environmental Quality 32: 642-653.
Turner, R.E., and N.N. Rabalais. 1994. Coastal eutrophication near the Mississippi river
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Epidemiology 7: 465-471.
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States Department of Agriculture, Soil Conservation Service. Washington, D.C.
28
Table 2.1. Percent of each sub-basin delineated as a particular land use for each sub-basin
in the South Fork subwatershed of the Sugar Creek watershed in northeastern Ohio.
Sub-basin
52
53
54
55
56
57
58
59
60
62
Urban
0.28
0.31
0.26
0.24
0.92
0.43
1.09
1.03
1.47
2.58
Agriculture
Shrub
Wooded
83.76
86.23
76.79
91.96
92.59
86.29
81.48
83.22
84.72
83.73
0.23
0.20
0.10
0.11
0.18
0.21
0.25
0.22
0.21
0.21
15.50
13.05
22.64
7.07
3.96
12.28
15.81
14.28
13.22
12.33
29
Open
Water
0.00
0.01
0.00
0.13
0.08
0.02
0.03
0.02
0.02
0.03
Wetland
0.21
0.19
0.22
0.01
0.54
0.30
0.38
0.38
0.35
0.34
Barren
0.01
0.02
0.00
0.48
1.71
0.48
0.96
0.85
0.00
0.79
Green Buffer
0.18
0.27
0.83
0.00
0.00
0.10
0.14
0.11
0.10
0.08
Table 2.2. Year-round mean water quality parameters collected 2003-2006 for each sub-basin in the South Fork subwatershed of the
Sugar Creek watershed in northeastern Ohio.
Sub-basin
total solids
phosphorus
nitrate
pH
temperature
conductivity
dissolved oxygen
turbidity
ammonia
(mg/L)
(mg/L)
(mg/L)
(mmho/cm)
(mg/L)
(NTU)
(
°F)
(mg/L)
52
53
54
55
56
57
58
59
60
62
1009
1007
995
1032
1029
1008
1448
1278
1434
2473
0.10
0.10
0.10
0.10
0.12
0.12
0.17
0.37
0.30
0.28
0.20
0.16
0.13
0.18
0.25
0.21
0.20
0.23
0.25
0.27
3.84
3.94
3.16
4.35
4.02
4.97
3.65
3.97
4.18
4.15
7.68
7.65
7.40
7.65
7.59
7.69
7.74
7.75
7.74
7.39
42.3
41.8
41.5
42.2
43.9
41.9
42.3
42.73
41.35
33.66
30
30
0.33
0.34
0.25
0.39
0.39
0.39
0.51
0.56
0.59
0.68
10.60
10.43
10.07
10.38
10.13
10.44
10.23
9.83
9.79
9.63
50.30
41.50
28.75
28.66
58.22
62.50
67.20
99.70
105.00
116.30
Figure 2.1. Sub-basins in the South Fork subwatershed within the Sugar Creek watershed
in northeastern Ohio. Numbers on map correspond to water collection points. Source:
Holmes et al. (unpublished data).
31
Figure 2.2. Land use designations for the South Fork subwatershed within the Sugar
Creek watershed in northeastern Ohio. Numbers on map correspond to water collection
points. Source: Holmes et al. (unpublished data).
32
1.0
3
Buffer
10
Wood
Total So
2
Urban
Wetland
1
5
7
4
8
Barren
Turbidit
Phosphor
Conducti
DO
6
Temperat
Ammonia
Water
Ag
9
Shrub
-1.0
Nitrate
pH
-1.0
1.0
Figure 2.3. Redundancy analysis relating land use and water quality parameters for the
South Fork subwatershed within the Sugar Creek watershed in northeastern Ohio. Circles
correspond to sub-basins: sub-basin 52 (1), sub-basin 53 (2), sub-basin 54 (3), sub-basin
55 (4), sub-basin 56 (5), sub-basin 57 (6), sub-basin 58 (7), sub-basin 59 (8), sub-basin 60
(9), and sub-basin 62 (10). Blue lines correspond to water quality parameters: dissolved
oxygen (DO), temperature (Temperat), pH, nitrate, ammonia, phosphorus (Phosph),
conductivity (Conducti), turbidity (Turbidit), and total solids (Total So). Red lines
correspond to land use types: green buffers (Buffer), wooded (Wood), urban, wetland,
barren, shrub, open water (Water), agriculture (Ag).
33
CHAPTER 3
COMPOSITION, DIVERSITY, AND RESTORATION EFFORTS OF RIPARIAN
GROUND-FLORA COMMUNITIES IN AN AGRICULTURAL WATERSHED OF
NORTHEASTERN OHIO
Introduction
Riparian areas are ecotones between terrestrial and aquatic ecosystems, which
provide many important functions, including buffering nutrients and sediments, slowing
runoff, reducing stream-bank erosion, adding woody debris to the stream, and shading
streams (Lowrance et al. 1984; Naiman et al. 1988; Gregory et al. 1991; Osborne and
Kovacic 1993; Zaimes et al. 2004). Despite the importance of these areas for reducing the
impacts of land use on water quality, riparian forests are often modified extensively,
especially in agricultural landscapes (Peterjohn and Correll 1984; Turner and Rabalais
1991; Naiman et al. 1993; Mitsch et al. 2001; Tockner and Stanford 2002; United States
Environmental Protection Agency 2009). Reasons for vegetation removal include grazing
livestock, providing water sources for livestock, and growing crops.
The loss of these riparian functions has had a cascading effect through aquatic
ecosystems and food webs (Allan et al. 1997; Carpenter et al. 1998; Stauffer et al. 2000;
34
Sponseller et al. 2001). Without properly functioning riparian vegetation to buffer
nutrients and sediments, primary production increases within the stream (Allan et al.
1997; Carpenter et al. 1998). Higher biological activity thereby increases the occurrence
of algal blooms and causes a decrease in dissolved oxygen, which leads to fish kills and
overall habitat degradation (e.g., Carpenter et al. 1998; Sponseller et al. 2001). Allowing
livestock free access to streams also contributes to stream-bank erosion. The resultant
siltation may also increase stream nutrient loading when phosphorus is attached to soil
particles (Carpenter et al. 1998). Siltation decreases the stream habitat suitability for
aquatic organisms through increasing turbidity and covering spawning habitat with
sediment (Allan et al. 1997; Stauffer et al. 2000). All of these conditions create an
environment which is suitable only for aquatic organisms that can survive in poor quality
habitat (Allan et al. 1997; Carpenter et al. 1998; Stauffer et al. 2000; Sponseller et al.
2001).
Nutrients in the water supply also affect human health. High nitrate levels in
drinking water have been linked to non-Hodgkin’s lymphoma (Ward et al. 1996).
Livestock use of streams has caused bacterial issues, e.g., E. coli, which can cause
livestock health issues, including colitis (Wang et al. 1996). E. coli survives in cattle fecal
matter, which leads to water source contamination (Wang et al. 1996).
The effects of the loss of riparian functions are compounded when land use
practices further contribute to water-quality impairment. Therefore, several studies have
documented the impact of watershed-scale and adjacent land use, on both riparian
vegetation and stream water quality (e.g., Tockner and Stanford 2002; Holmes 2004;
Moffatt et al. 2004). In comparison to forests, agricultural areas are typically associated
35
with lower plant species diversity (Boutin and Jobin 1998). A study in Canada
determined riparian ground-flora vegetation species richness in riparian areas adjacent to
urban land use was lower than in riparian areas adjacent to agricultural land use, although
riparian areas adjacent to a wooded land use had the highest number of species (Moffatt
et al. 2004). Because of the important functions provided by riparian areas and the role
that adjacent land use can play in shaping riparian vegetation, it is important to
understand how adjacent land use affects riparian community development. This is
particularly true for riparian ground-flora communities as this vegetation layer is often
more sensitive to disturbances than overstory vegetation (Stromberg et al. 1996; Goebel
et al. 2006).
Agriculture is the dominant source of water quality impairment in the nation’s
rivers and streams (United States Environmental Protection Agency 2009). This land use
is extensive throughout the Midwest and poor agricultural practices, as well as riparian
vegetation removal and degradation, have been attributed as the leading cause of the
extensive hypoxia zone in the Gulf of Mexico (Turner and Rabalais 1994; Mitsch and
Gosselink 2007). Approximately 90 percent of the nitrate-nitrogen input into the Gulf of
Mexico can be attributed to the Ohio, Missouri, and upper Mississippi Rivers (Mitsch and
Day 2006). The Ohio River is estimated to contribute 34 percent of the total nitrogen
entering the Gulf of Mexico, and most of these nutrients originate in agricultural
watersheds in the headwaters (Mitsch and Day 2006). One such watershed is the Sugar
Creek watershed in northeastern Ohio, which drains 925 km2 in Wayne, Stark, Holmes,
and Tuscarawas Counties (Ohio Environmental Protection Agency 2002). This watershed
is dominated (> 70 percent) by agricultural land uses and, primarily due to current land36
use practices, is considered the second most degraded watershed in Ohio (Ohio
Environmental Protection Agency 2002). However, restoration potential is high due to
the sources of degradation within the watershed, i.e., sediment and nutrient loading and
in-stream habitat change (Ohio Environmental Protection Agency 2002).
Water-quality issues and land-use practices in the Sugar Creek watershed are
similar throughout other agricultural watersheds in the Midwest, so this project has
become a national model for building “local community capacity” for watershed
management and restoration (Ohio Environmental Protection Agency 2002). As this
watershed is a model for restoration, and nutrient loading is a significant problem causing
water-quality issues that can be reduced with riparian forests, baseline conditions need to
be identified. These baseline conditions can be used for evaluating the effectiveness of
restoration efforts as well as where to prioritize those efforts. However, little information
is available on the current condition of riparian vegetation and factors that regulate the
development of riparian vegetation in the watershed, as well as how land-use practices
affect riparian plant communities. Most research to date has focused on water quality and
stream habitat at individual-reach scales (Herrman et al. 2008a; D’Ambrosio et al. 2009),
although some studies have focused on watershed-scale land uses and its effect on water
quality (Holmes 2004; Prasad et al. 2005; Herrman et al. 2008b).
The overall objective of this study was to examine patterns in riparian groundflora communities adjacent to different land uses in a watershed in northeastern Ohio.
Specifically, we addressed the following questions. First, what is the influence of
adjacent land use on the composition and diversity of ground-flora communities? Second,
what restoration efforts are needed for local riparian areas based upon an assessment of
37
the conservation status and invasiveness of individual species that compose the groundflora?
Study Area
We focused our efforts in three representative subwatersheds of the Sugar Creek
watershed. The Upper Sugar Creek subwatershed (hydrologic unit code (HUC) 05040001-100) is located in the headwaters of the Sugar Creek in Wayne County, the
North Fork subwatershed (HUC 05-040001-100) is a mid-stem tributary located in
Holmes and Wayne Counties, and the South Fork subwatershed (HUC 05-040001-110) is
a lower-stem tributary located in Holmes and Tuscarawas Counties (Ohio Environmental
Protection Agency 2002).
The Upper and North Fork subwatersheds are located in the glaciated Erie Drift
Plains Ecoregion (United States Environmental Protection Agency 2007). The landscape
of this Ecoregion, dominated by glacial landforms of Wisconsinan age, is characterized
by gently sloping topography and poorly drained soils (Bureau et al. 1984; Seaholm and
Graham 1997; Ohio Environmental Protection Agency 2002). These areas are highly
productive and are characterized by a mixture of both large- and small-scale dairy farms
and row-crop agriculture (Bureau et al. 1984; Seaholm and Graham 1997). The South
Fork subwatershed is located in the unglaciated Western Allegheny Plateau Ecoregion
(United States Environmental Protection Agency 2007), and is characterized by steeper
slopes and higher stream gradients than the Upper and North Fork subwatersheds (Ohio
Environmental Protection Agency 2002). Due to the limitations of steep terrain on row38
crop agriculture, there are many small-scale dairy operations in this subwatershed
(Seaholm and Graham 1997).
The climatic conditions of all three subwatersheds are similar. Mean summer
temperatures range from 20.6 to 21.1°C, and mean winter temperatures range from -1.7
to -2.8°C (Bureau et al. 1984; Waters and Roth 1986; Seaholm and Graham 1997).
Average annual precipitation ranges from 90 to 98.3 cm, with increasing levels as one
moves south from the Upper subwatershed to the South Fork subwatershed (Bureau et al.
1984; Waters and Roth 1986; Seaholm and Graham 1997). Over half of the precipitation
falls between April and September in all three subwatersheds (Bureau et al. 1984; Waters
and Roth 1986; Seaholm and Graham 1997).
In addition to the physiographic and climatic gradients associated with the three
subwatersheds, Parker (2006) describes the following differences in the social structure
between the subwatersheds. The Upper subwatershed is dominated by non-Amish
farmers and larger municipalities compared with the other two subwatersheds. In the
North Fork and South Fork subwatersheds, Amish communities are more common
resulting in smaller farms and more mixed agricultural practices, i.e., both crop and
animal production on the same farm, than found in the Upper subwatershed (Parker
2006).
Specific water-quality problems in the Upper subwatershed are associated with
siltation stemming from intensive agricultural practices and point sources associated with
municipalities and limited industrial operations (Ohio Environmental Protection Agency
2002). The North Fork subwatershed shares some of these issues, in part due to the
greater number of Amish communities and the subsequent increased use of livestock for
39
farming and transportation (Ohio Environmental Protection Agency 2002). These land
uses have historically resulted in the decline of riparian vegetation as there is increased
livestock pasturing along and in the stream (Ohio Environmental Protection Agency
2002). As a result, the North Fork subwatershed has the highest levels of nitrite-nitrogen
(NO2-N) and nitrate-nitrogen (NO3 -N) loading, as well as the most non-point source
pollution, per square mile in the Sugar Creek watershed (Ohio Environmental Protection
Agency 2002). Due to the similar social systems, the South Fork subwatershed
experiences similar water quality issues that the North Fork subwatershed does, i.e.,
increased livestock pasturing along and in the stream has led to high levels of nutrients,
although the steeper topography limits agricultural crop production to smaller field sizes
and there tends to be more forests (Ohio Environmental Protection Agency 2002).
Methods
Data Collection
Within each subwatershed, we utilized existing sample locations established for a
water quality monitoring program to sample riparian vegetation during June through
August of 2008. Sampling locations are presented in Appendix A. Because these water
quality monitoring points were established near roads, we established our sample
transects 30-m upstream or downstream from these locations to reduce the effects of
roads.
We established two 100-m transects, one on each side of the stream, 2-m from the
stream bankfull stage. When sampling occurred upstream of the road, the first 1-m2
40
square vegetation quadrat was established 12-m upstream from the start of the transect, a
second was established at 52-m, and a third at 92-m. If the transect was located
downstream of a road, the first quadrat was established 122-m downstream from the road,
a second was established 82-m, and a third at 42-m. As we sampled both sides of the
stream, each sampling location had six quadrats.
Within each quadrat, we visually estimated the percent cover of each ground-flora
species (defined as vascular plants less than 1-m tall, including woody species) into one
of seven cover classes (< 1, 1-5, 6-10, 11-20, 21-40, 41-70, and 71-100 percent). Plants
that overhung the quadrat but were rooted outside the quadrat were not counted.
Individual species were identified as native or non-native and classified into functional
life-form guilds (annual forbs, perennial forbs, graminoids, pteridophytes, woody vines,
woody shrubs, and woody tree seedlings). Nomenclature, functional life-form guild, and
native or non-native status of each species was in accordance with the PLANTS database
(United States Department of Agriculture, Natural Resources Conservation Service
2009).
Data Analyses
Prior to any analyses, we summarized the ground-flora cover data by calculating
the importance percentage (IP) of each ground-flora species at each sampling location.
For each transect, the average cover-class value for each species was considered the
relative abundance. Relative frequency is the number of quadrats the species occurred on
at each sampling location. We calculated the IP as the sum of the relative abundance and
41
relative frequency divided by two, after replacing the cover-class value with the midpoint
value of the specific cover-class range.
We classified each sampling location as a specific riparian “type” along the
stream based upon adjacent land uses: row crop, lawn, grassed pasture, wooded pasture,
wooded, row crop/lawn, row crop/grassed pasture, and lawn/wooded. Adjacent land
use(s) associated with each sampling location are presented in Appendix A. Mixed
riparian types are those that had two major land use types adjacent to the stream, one on
each side. There were twelve sampling locations associated with the Upper subwatershed,
seven with the North Fork subwatershed, and twelve with the South Fork subwatershed
(Table 3.1).
We calculated mean species IP using Minitab (Version 15.1.2) for each riparian
type in the three subwatersheds. To test the hypothesis that there are no significant
differences in ground-flora community composition among riparian types in the Upper
and South Fork subwatersheds, we utilized multi-response permutation procedures
(MRPP). In the North Fork subwatershed, grassed pasture was the only riparian type we
sampled at more than one sampling location. Because MRPP is used for testing for
differences among two or more groups, we were not able to use this statistical analysis
for the North Fork subwatershed (McCune and Grace 2002). We used the species IP with
a weighting factor and Sorenson’s distance, and made pairwise comparisons in the
software package PC-ORD (Version 5.0 MjM Software, Gleneden Beach, OR).
Indicator Species Analysis with PC-ORD (Version 5.0 MjM Software, Gleneden
Beach, OR) was used to determine if species distribution is significantly different from a
random distribution. We used the species’ IPs for this analysis, grouped them by riparian
42
type, ran a randomization test, and utilized the Monte-Carlo test results to determine
individual indicator species.
Using life-form guild information, we calculated the mean IP by averaging all
species associated with each guild per sampling location for each subwatershed and
significant differences were determined using Kruskall-Wallis tests in Minitab (Version
15.1.2). For these analyses, riparian types that were represented by only one sampling
location were not included in the analyses (McCune and Grace 2002), i.e., one in the
Upper subwatershed categorized as the row crop/lawn riparian type, three in the North
Fork subwatershed associated with the wooded pasture, row crop/lawn, and row
crop/grassed pasture riparian types, and one in the South Fork subwatershed associated
with the wooded pasture riparian type.
For examining differences in ground-flora diversity among riparian types, we
calculated species richness (S), the number of species present in each 1-m2 quadrat;
Shannon’s Index (((H’; H’ = S*ln(pi)), where pi is the proportion of individuals found in
the ith species); Ludwig and Reynolds 1988); and an evenness ratio ((E; E = H’/ln(S));
Ludwig and Reynolds 1988). We averaged each diversity metric by riparian type for each
subwatershed. We then used a Kruskall-Wallis test in Minitab (Version 15.1.2) to
determine if there were significant differences among riparian types in these diversity
metrics.
Finally, to determine the effort needed to restore each riparian type, we utilized
information from the NatureServe (2009) database to determine the conservation status
and invasive potential of individual species present at each sampling location (Table 3.2).
We determined the conservation status of each species (G1-G5), which represents
43
whether that species has a very high risk of extinction (G1), has a high risk of extinction
(G2), has a moderate risk of extinction (G3), is slightly more at risk of extinction (G4), or
is common and thus secure from extinction (G5) (Table 3.2). To determine the potential
negative impact a particular non-native species has on natural biodiversity, we utilized
the Invasive Species Impact Rank (I-Rank) (NatureServe 2009). I-Rank categories
include: high, which indicates the species is a severe threat to the native community;
medium, which indicates the species is a moderate threat; low, which indicates the
species is a low threat; or insignificant, which indicates the species is an insignificant
threat. Once all species were classified, we summarized this information for each riparian
type in each subwatershed.
Results
Ground-flora Composition and Functional Guilds
MRPP results suggest the riparian ground-flora composition is significantly
different among riparian types for both the Upper (T = -3.74; P = 0.0018) and South Fork
(T = -1.88; P = 0.0450) subwatersheds. Within the Upper subwatershed, pairwise
comparisons indicate significant differences among all the three riparian types: lawn and
wooded riparian types (T = -1.82; P = 0.0430), lawn and row crop riparian types (T = 2.55; P = 0.0140), and wooded and row crop riparian types (T = -2.63; P = 0.0170).
Pairwise comparisons for ground-flora community compositions within the South Fork
subwatershed indicated significant differences among grassed pasture and row crop
riparian types (T = -1.45; P < 0.0001), grassed pasture and the lawn/wooded riparian
44
types (T = -1.46; P < 0.0001), and among the row crop/grassed pasture and lawn/wooded
riparian types (T = -2.40; P = 0.0210).
The differences in overall ground-flora community compositions are reflected in
the dominant species of each riparian type. For example, the dominant species in the row
crop riparian type in the Upper subwatershed is Phalaris arundinacea L. (native;
graminoid), while the lawn riparian type is dominated by Poa annua L. (non-native;
graminoid), and the wooded riparian type is dominated by Impatiens capensis Meerb.
(native; annual forb), Prunus serotina Ehrh. (native; woody tree seedling), and Viola
sororia Willd. (native; perennial forb) (Table 3.3). In the North Fork subwatershed,
Glechoma hederacea L. (non-native; perennial fob), Poa pratensis L. (native;
graminoid), Schedonorus phoenix (Scop.) Holub (non-native; graminoid), and Trifolium
repens L. (non-native; perennial forb) dominate grassed pastures (Table 3.4), while in the
South Fork subwatershed grassed pasture riparian types are dominated by Oxalis stricta
L. (native; perennial forb) and Viola sororia (Table 3.5). As in the Upper subwatershed,
the row crop riparian type in the South Fork subwatershed is dominated by Phalaris
arundinacea, while Schedonorus phoenix and Trifolium repens dominate the ground-flora
of the row crop/grassed pasture riparian types, and Impatiens pallida Nutt. (native; annual
forb) dominates the lawn/wooded riparian types (Table 3.5).
Corresponding to the dominant species in each riparian type, we found that some
species distributions are significantly different from random for particular riparian types.
In the Upper subwatershed, Phalaris arundinacea is an indicator of a row crop riparian
type (P = 0.0170), while Oxalis stricta is an indicator of a lawn riparian type (P =
0.0210), and Toxicodendron radicans L. is an indicator of a wooded riparian type (P =
45
0.0064) (Table 3.3). Additionally, while we found no species were indicators of riparian
types in the North Fork subwatershed (Table 3.4), we did find indicators for the South
Fork subwatershed. Specifically, we found that Ambrosia trifida L. is indicative of a row
crop riparian type (P = 0.0270), and Solidago canadensis L. is an indicator of a
lawn/wooded riparian type (P = 0.0324) (Table 3.5).
We found no significant differences (P > 0.0790) in total cover or cover of
individual life-form guilds among the different riparian types in any of the three
subwatersheds (Tables 3.6, 3.7, 3.8).
Ground-flora Diversity
We found no significant differences (P > 0.1040) in ground-flora species richness,
Shannon’s Diversity Index, or evenness ratio values for the Upper or North Fork
subwatersheds (Figures 3.1, 3.2, 3.3). Ground-flora species richness and Shannon’s
Diversity Index values, however, are significantly different among riparian types (P =
0.0300 and P = 0.0200, respectively) for the South Fork subwatershed (Figures 3.1, 3.2).
Specifically, in the South Fork subwatershed, species richness was highest for the grassed
pasture riparian type and lowest for the wooded pasture riparian type (Figure 3.1).
Shannon’s Diversity Index values were highest for the grassed pasture riparian type
(Figure 3.2).
Species’ Conservation Status and I-Rank
In the Upper subwatershed, the highest number of species in little danger of
extinction occurred in the lawn and wooded riparian types, 34 and 33 species,
46
respectively (Table 3.9). In the North Fork subwatershed, we found 33 secure species in
the grassed pasture riparian type, the most of any riparian type in this subwatershed
(Table 3.10). We found one national critically imperiled species in both the grassed
pasture (Nasturtium officinale W.T. Aiton) and row crop/grassed pasture (Ranunculus
acris L.) riparian types (Table 3.10). In the South Fork subwatershed, the row
crop/grassed pasture riparian type contained 41 secure species, and we found 38 secure
species in the grassed pasture riparian type; these were the highest numbers for any
riparian type in this subwatershed (Table 3.11).
In the Upper subwatershed, the lawn and wooded riparian types have the most
highly invasive species, with nine and seven species, respectively, while the lawn riparian
type also has the most (four species) that were designated as least invasive (Table 3.9). In
the North Fork subwatershed, the grassed pasture riparian type has the most high IRanked species (six species), as well as the most (five species) low I-Ranked species
(Table 3.10). In the South Fork subwatershed, row crop and row crop/grassed pasture
have the most high I-Ranked species, with five and six species, respectively (Table 3.11).
The row crop/grassed pasture riparian type has four low threat species, the most of any
riparian type (Table 3.11).
The wooded riparian type in the Upper subwatershed contains the most (30)
native species, while the lawn riparian type has the most non-native species (29) (Table
3.9). In the North Fork subwatershed, we found the grassed pasture riparian type has the
most native (30) and non-native (36) species (Table 3.10). In the South Fork
subwatershed, the row crop/grassed pasture riparian type has the most native (37 species)
as well as non-native species (31) (Table 3.11).
47
Discussion
Although it is well understood that riparian forests provide many important
functions (Vannote et al. 1980; Gregory et al. 1991; Osborne and Kovacic 1993), riparian
vegetation removal and degradation has resulted in poorly functioning riparian areas
(Peterjohn and Correll 1984; Naiman et al. 1993; Tockner and Stanford 2002).
Particularly in agricultural watersheds, the effects of riparian forest loss have negatively
affected stream water quality and aquatic food webs (United States Environmental
Protection Agency 2009). Therefore, restoration of riparian areas in agricultural
watersheds, such as the Sugar Creek watershed, where water quality is impaired has
increased greatly over the past twenty years. However, little information is available
about the current vegetation condition or where riparian restoration efforts should be
implemented. Due to the sensitivity of riparian ground-flora species to disturbance
(Stromberg et al. 1996; Goebel et al. 2006), understanding how this vegetation layer
responds to different environmental factors and adjacent land use practices can be used to
help focus restoration efforts.
Ground-flora and Land Use Relationships
We observed that adjacent land use has an impact on the composition and
diversity of the riparian ground-flora communities in the three subwatersheds we
examined in the Sugar Creek watershed. In a disturbed watershed in southern Manitoba,
Canada, Moffatt et al. (2004) also found that land use has an influence on ground-flora
vegetation. Specifically, riparian areas adjacent to urban land use had a lower ground48
flora species diversity and more non-native species than the other riparian areas examined
(Moffatt et al. 2004).
Although adjacent land use influenced ground-flora composition, there were no
significant differences in plant functional guilds among the riparian types. This is
surprising because other studies of riparian areas have found significant differences in
ground-flora functional guilds and not community composition (e.g., Goebel et al. 2003a;
Goebel et al. 2003b; Holmes et al. 2005; Goebel et al. 2006). However, these studies
compared ground-flora communities among fluvial landforms in relatively undisturbed
riparian forests. In the Sugar Creek watershed, most streams have been channelized and
modified, and are disconnected from their floodplains. As a result, disturbances
associated with land-use practices, rather than differences associated with fluvial
landforms or hydrology, are likely driving compositional differences found in groundflora communities. Over time, similarities in riparian ground-flora functional guilds may
be attributed to land use homogeneity in the larger landscape, which may be driving
patterns in riparian area vegetation more so than adjacent land use (Moffatt et al. 2004).
It is well documented that riparian areas are highly diverse ecotones affected by
disturbances both from the adjacent terrestrial as well as aquatic ecosystems (e.g.,
Gregory et al. 1991; Naiman et al. 1993). In the Sugar Creek watershed, land use
influenced ground-flora species composition, but did not significantly affect species
diversity in the Upper and North Fork subwatersheds (P > 0.1040 and P>0.471,
respectively). Similarities in diversity may seem counterintuitive, but might be a function
of a homogenous landscape (Moffatt et al. 2004). The one exception to this pattern was
observed in the South Fork subwatershed, where the landscape is more heterogeneous in
49
terms of land-use practices given the topography and the dominance of Amish
communities that foster a more diverse landscape structure (Seaholm and Graham 1997;
Parker 2006). When comparing the diversity metrics for the riparian types of the Sugar
Creek watershed with those from Johnson Woods, an old-growth forest located in the
headwaters of the adjacent Chippewa Creek watershed, the riparian areas along Sugar
Creek have both higher species richness and evenness ratio values than the headwater
riparian areas in the old-growth forest ecosystem (Holmes et al. 2005).
Implications for Restoration
The Sugar Creek watershed is a highly disturbed agricultural watershed with
impaired water quality due to land-use practices and poorly functioning riparian areas
(Ohio Environmental Protection Agency 2002). Because of the important functions
riparian areas provide (Lowrance et al. 1984; Gregory et al. 1991; Naiman et al. 1993;
Osborne and Kovacic 1993; Molles 2002; Mitsch and Gosselink 2007), restoration is
necessary to reduce the impacts of land use practices. Because of resource limitations, we
need to choose riparian types in which to focus restoration efforts. Among the riparian
types examined in our study least disturbed riparian types include the row crop and row
crop/lawn riparian types in the Upper subwatershed that have the fewest number of
species which are highly invasive and pose a threat to ecosystems. In the North Fork
subwatershed the row crop/grassed pasture riparian type may be the best candidate for
restoration, and the grassed pasture, wooded pasture, and lawn/wooded riparian types in
the South Fork subwatershed. The wooded riparian type may be utilized as a reference to
50
guide restoration efforts. After restoration, the information gathered in this study can be
used as baseline conditions by which to compare riparian vegetation composition and
structure.
Restoration efforts may be complicated by invasive species and in some cases
active site amelioration may be necessary when invasive species have long dominated a
site (Correll 2005). Our results clearly show that the riparian ground-flora communities in
the Sugar Creek watershed contain invasive species that have the potential to negatively
impact plant communities and ecosystem function. Invasive species occupied the riparian
areas in all three subwatersheds. Riparian types with many highly invasive species will
most likely pose a challenge to managers hoping to restore native species to riparian
areas. Restoration may be difficult in the riparian areas adjacent to lawns in the Upper
subwatershed, in the riparian areas adjacent to grassed pasture in the North Fork
subwatershed, and in the riparian areas adjacent to a mixture of row crops and grassed
pastures in the South Fork subwatershed. We determined that these areas are part of a
disturbed landscape which may difficult to restore due to the difficulties of controlling
invasive species and establishing native species, and the fact that these riparian types are
frequently disturbed, either by humans or livestock.
Although riparian forests serve important functions, nitrogen saturation and
subsurface tile drainage in agricultural watersheds have rendered riparian vegetation less
effective for buffering nitrogen. Particularly, high nitrogen input from agricultural fields
in the headwaters of the Sugar Creek watershed has led to nitrogen saturation (Herrman
et al. 2008a; Herrman et al. 2008b). Herrman et al. (2008a) found that because of this
saturation, the headwaters are ineffective at removing in-stream nitrogen. Many
51
agricultural fields are also drained directly to the stream with tiles (Ohio Environmental
Protection Agency 2002). Tile drainage has increased the amount of water, and
subsequently nutrients, entering the stream system, and contributes to a decrease in water
quality (Tomer et al. 2003). Agricultural fields, when drained, contribute the highest
nitrate concentrations to streams when plants are not growing (Cambardella et al. 1999).
Because tile drainage and nitrogen saturation have reduced the effectiveness of
riparian areas to buffer nitrogen, it is important to reduce nitrogen inputs at the source,
i.e., agricultural fields (Osborne and Kovacic 1993; Tomer et al. 2003; Herrman et al.
2008b). Improving agricultural management practices, e.g., using soil tests for
determining the correct amount of fertilizer to apply, planting perennial crops, and
improving manure management, would decrease nutrient sources which eventually enter
stream systems (e.g., Osborne and Kovacik 1993; Carpenter et al. 1998; Mitsch et al.
2001). As a model for restoration, solutions for improving water quality in the Sugar
Creek watershed can be applied to other agricultural watersheds throughout the Midwest.
52
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Table 3.1. Count of sampling locations associated with riparian areas adjacent to various land uses in the Upper, North Fork, and
South Fork subwatersheds in the Sugar Creek watershed in northeastern Ohio.
Subwatershed
Upper
North Fork
South Fork
row crop
3
0
2
lawn
4
0
0
grassed pasture
0
4
4
wooded pasture
0
1
0
wooded
4
0
0
58
58
row crop/ lawn
1
1
0
row crop/grassed pasture
0
1
4
lawn/wooded
0
0
2
Table 3.2. NatureServe (2009) designations for species’ conservation status and rounded I-Rank.
Conservation Status
G1
G2
G3
G4
G5
GNR
GNA
N1
N2
59
N3
N4
N5
NNR
NNA
Rounded I-Rank
High
Medium
Low
Insignificant
Not Yet Assessed
Provides an estimate of extinction risk
Globally critically imperiled-at very high risk of extinction due to extreme rarity, very steep declines, or other factors
Globally imperiled-at high risk of extinction or elimination due to very restricted range, very few populations, steep declines, or other
factors
Globally vulnerable-at moderate risk of extinction or elimination due to a restricted range, relatively few populations, recent and
widespread declines, or other factors
Globally apparently secure-uncommon but not rare; some cause for long-term concern due to declines or other factors
Globally secure-common; widespread and abundant
Globally unranked-global rank not yet assessed
Globally not applicable-a conservation status rank is not applicable because the species is not a suitable target for conservation activities
Nationally critically imperiled-critically imperiled because of extreme rarity or because of some factor(s) such as very steep declines
making it especially vulnerable to extirpation
Nationally imperiled-imperiled because of rarity due to very restricted range, very few populations, steep declines, or other factors
making it very vulnerable to extirpation
Nationally vulnerable-vulnerable due to a restricted range, relatively few populations, recent and widespread declines, or other factors
making it vulnerable
Nationally apparently secure-uncommon but not rare; some cause for long-term concern due to declines or other factors
Nationally secure-common, widespread, and abundant
Nationally unranked-national conservation status not yet assessed
Nationally not applicable-a conservation status rank is not applicable because the species or ecosystem is not a suitable target for
conservation activities
Invasive Species Impact Rank; a protocol used to assess a non-native plant species negative impact on natural biodiversity
Species represents a severe threat to native species and ecological communities
Species represents a moderate threat to native species and ecological communities
Species represents a significant but relatively low threat to native species and ecological communities
Species represents an insignificant threat to native species and ecological communities
Species was not assessed using the protocol
59
Table 3.3. Mean (+ 1 standard deviation) importance percentage of ground-flora species
for riparian areas adjacent to row crop, lawn, and wooded land uses for the Upper
subwatershed of the Sugar Creek watershed in northeastern Ohio. An asterisk after a
value indicates the species was an indicator of a specific riparian type (Monte-Carlo test;
* P < 0.05).
Adjacent Land Use
Species Name
Acer rubrum
Acer saccharum
Alliaria petiolata
Ambrosia trifida
Arabis hirsuta
Arisaema triphyllum
Asarum canadense
Barbarea vulgaris
Berberis thunbergii
Bromus inermis
Calystegia sepium
Carex laxiflora
Carex trisperma
Carya ovata
Cerastium fontanum
Chamaesyce maculate
Chelidonium majus
Circaea lutetiana
Cirsium arvense
Convolvulus arvensis
Cynodon dactylon
Dactylis glomerata
Daucus carota
Digitaria ischaemum
Dipsacus fullonum
Dryopteris carthusiana
Echinocystis lobata
Equisetum arvense
Erigeron strigosus
Fraxinus pennsylvanica
Galinsoga quadriradiata
Galium aparine
Galium mollugo
Row crop
Lawn
Wooded
5.72 (9.91)
2.86 (4.96)
35.80 (31.20)
26.90 (23.90)
21.60 (23.90)
18.30 (31.70)
8.64 (14.96)
2.86 (4.96)
2.86 (4.96)
2.86 (4.96)
2.79 (4.84)
-
2.09 (4.19)
4.19 (4.84)
8.53 (9.85)
2.72 (5.44)
15.12 (17.81)
2.09 (4.19)
14.71 (19.80)
2.09 (4.19)
2.09 (4.19)
11.41 (8.20)
8.53 (17.06)
8.80 (17.60)
2.41 (4.81)
4.24 (8.48)
4.29 (8.58)
2.09 (4.19)
2.15 (4.29)
8.43 (16.85)
15.00 (24.60)
8.69 (0.36)
2.72 (4.50)
4.34 (8.69)
2.09 (4.19)
2.15 (4.29)
4.19 (8.37)
2.15 (4.29)
2.25 (4.50)
2.15 (4.29)
6.44 (12.87)
2.25 (4.50)
2.41 (4.81)
-
Table 3.3. Continued
60
Table 3.3. Continued
Geum canadense
Geum rivale
Glechoma hederacea
Gratiola neglecta
Heliopsis helianthoides
Holcus lanatus
Impatiens capensis
Impatiens pallida
Lactuca canadensis
Ligustrum vulgare
Lolium perenne
Lonicera morrowii
Lysimachia nummularia
Maianthemum racemosum
Matricaria discoidea
Medicago lupulina
Mentha arvensis
Oxalis stricta
Panicum capillare
Parthenocissus quinquefolia
Phalaris arundinacea
Phleum pratense
Picea pungens
Pilea pumila
Plantago lanceolata
Plantago major
Poa annua
Poa pratensis
Podophyllum peltatum
Polygonatum biflorum
Polygonum aviculare
Polygonum hydropiper
Polygonum pensylvanicum
Prunella vulgaris
Prunus serotina
Ranunculus repens
Rosa multiflora
Rubus allegheniensis
Rubus occidentalis
6.21 (10.75)
2.79 (4.84)
21.70 (22.30)
6.62 (11.47)
2.86 (4.96)
2.86 (4.96)
5.65 (4.90)
65.50 (8.57) *
2.86 (4.96)
2.79 (4.84)
11.57 (11.93)
23.70 (24.40)
-
22.20 (22.00)
2.09 (4.19)
4.86 (9.73)
4.66 (9.31)
4.97 (9.94)
2.09 (4.19)
4.40 (8.79)
2.09 (4.19)
2.15 (4.29)
2.09 (4.19)
4.19 (8.37)
2.09 (4.19)
25.27 (11.78) *
11.90 (23.90)
4.86 (9.73)
2.25 (4.50)
2.09 (4.19)
10.63 (10.72)
4.40 (5.08)
4.24 (8.48)
40.80 (41.30)
4.50 (9.00)
4.19 (8.37)
2.15 (4.29)
10.99 (16.86)
4.19 (4.84)
4.29 (8.58)
-
2.15 (4.29)
9.31 (10.77)
11.15 (10.49)
28.50 (24.8)
2.15 (4.29)
4.34 (8.69)
6.44 (4.29)
4.29 (4.96)
8.53 (7.01)
2.25 (31.60)
25.30 (31.60)
16.30 (22.50)
4.29 (4.96)
2.15 (4.29)
4.86 (5.69)
2.41 (4.81)
2.15 (4.29)
2.15 (4.29)
2.25 (4.50)
20.90 (24.20)
2.09 (4.19)
4.66 (5.38)
17.40 (20.80)
7.11 (4.85)
Table 3.3. Continued
61
Table 3.3. Continued
Rumex acetosella
Sambucus nigra
Sanicula marilandica
Schedonorus phoenix
Solidago canadensis
Solidago juncea
Solidago nemoralis
Sonchus arvensis
Spiraea japonica
Symphyotrichum prenanthoides
Taraxacum officinale
Thalictrum pubescens
Toxicodendron radicans
Trifolium repens
Urtica dioica
Verbesina alternifolia
Veronica arvensis
Veronica filiformis
Veronica serpyllifolia
Vinca minor
Viola canadensis
Viburnum nudum
Viola sororia
9.21 (15.95)
3.00 (5.20)
6.49 (11.23)
13.00 (22.50)
5.65 (9.79)
3.21 (5.56)
25.70 (22.30)
12.10 (20.90)
2.79 (4.84)
8.51 (8.58)
2.15 (4.29)
6.70 (8.68)
4.29 (8.58)
4.19 (4.84)
6.28 (12.56)
4.50 (9.00)
4.40 (8.79)
17.22 (15.83)
9.00 (18.00)
8.04 (16.08)
2.15 (4.29)
4.19 (8.37)
13.86 (16.09)
2.41 (4.81)
14.86 (14.55)
62
2.25 (4.50)
4.97 (9.94)
2.25 (4.50)
8.53 (9.85)
11.56 (5.96) *
2.72 (5.44)
2.09 (4.19)
2.41 (4.81)
21.50 (22.50)
Table 3.4. Mean (+ 1 standard deviation) importance percentage of ground-flora species
for riparian areas adjacent to a grassed pasture land use for the North Fork subwatershed
of the Sugar Creek watershed in northeastern Ohio. An asterisk after a value indicates the
species was an indicator of a specific adjacent land use (Monte-Carlo test; * P < 0.05).
Adjacent Land Use
Species Name
Acer rubrum
Achillea millefolium
Agrostis gigantean
Allium vineale
Ambrosia artemisiifolia
Ambrosia trifida
Asarum canadense
Brassica nigra
Bromus inermis
Calystegia sepium
Capsella bursa-pastoris
Cerastium fontanum
Chenopodium album
Cirsium arvense
Crataegus spp.
Dactylis glomerata
Daucus carota
Digitaria ischaemum
Equisetum arvense
Erigeron annuus
Eupatorium perfoliatum
Galinsoga quadriradiata
Glechoma hederacea
Holcus lanatus
Impatiens capensis
Juncus tenuis
Leucanthemum vulgare
Lolium perenne ssp. multiflorum
Lolium perenne ssp. perenne
Lycopus virginicus
Lysimachia nummularia
Malus spp.
Matricaria discoidea
Mentha ×piperita
Grassed pasture
2.09 (4.19)
2.15 (4.29)
2.09 (4.19)
4.19 (8.37)
4.29 (8.58)
2.09 (4.19)
2.15 (4.29)
2.09 (4.19)
2.41 (4.81)
2.15 (4.29)
4.19 (8.37)
6.33 (8.04)
2.09 (4.19)
24.27 (19.39)
2.09 (4.19)
21.97 (8.20)
10.73 (10.69)
14.10 (28.30)
4.24 (4.90)
2.09 (4.19)
2.09 (4.19)
2.09 (4.19)
30.56 (11.06)
6.54 (8.26)
19.04 (17.22)
2.09 (4.19)
4.24 (4.90)
6.44 (12.87)
7.53 (15.06)
8.48 (9.79)
6.39 (8.20)
2.09 (4.19)
2.09 (4.19)
4.34 (8.69)
Table 3.4. Continued
63
Table 3.4. Continued
Mentha spicata
Nasturtium officinale
Oxalis stricta
Pastinaca sativa
Panicum virgatum
Phalaris arundinacea
Phleum pretense
Pilea pumila
Plantago major
Poa annua
Polygonum pensylvanicum
Polygonum persicaria
Poa pratensis
Ranunculus bulbosus
Ranunculus repens
Rosa multiflora
Rubus occidentalis
Rumex acetosella
Saponaria officinalis
Schedonorus phoenix
Sinapis arvensis
Solanum carolinense
Solidago juncea
Solidago nemoralis
Sorghastrum nutans
Stellaria graminea
Symphyotrichum prenanthoides
Taraxacum officinale
Toxicodendron radicans
Trifolium repens
Veronica anagallis-aquatica
Veronica arvensis
Vernonia gigantea
Veronica serpyllifolia
Viola sororia
2.15 (4.29)
2.15 (4.29)
10.47 (10.54)
2.09 (4.19)
8.84 (17.69)
6.48 (12.96)
17.53 (18.30)
4.24 (4.90)
8.43 (16.85)
7.84 (15.69)
6.49 (8.31)
4.55 (9.10)
30.70 (31.90)
14.00 (28.00)
2.09 (4.19)
3.86 (7.73)
2.15 (4.29)
2.15 (4.29)
2.15 (4.29)
29.20 (38.30)
2.15 (4.29)
10.68 (10.81)
4.24 (8.48)
2.15 (4.29)
2.15 (4.29)
4.19 (4.84)
2.09 (4.19)
23.55 (13.05)
2.09 (4.19)
29.20 (21.90)
2.25 (4.50)
2.09 (4.19)
2.09 (4.19)
2.09 (4.19)
4.24 (4.90)
64
Table 3.5. Mean (+ 1 standard deviation) importance percentage of ground-flora species
for riparian areas adjacent to row crop, grassed pasture, row crop/grassed pasture, and
lawn/wooded land uses for the South Fork subwatershed of the Sugar Creek watershed in
northeastern Ohio. An asterisk after a value indicates the species was an indicator of a
specific riparian type (Monte-Carlo test; * P < 0.05).
Species Name
Acer rubrum
Acer saccharinum
Achillea millefolium
Acorus americanus
Agrimonia parviflora
Agrostis gigantean
Alliaria petiolata
Ambrosia artemisiifolia
Ambrosia trifida
Amphicarpaea bracteata
Apios americana
Arctium minus
Asarum canadense
Asclepias syriaca
Avena sativa
Boehmeria cylindrical
Bromus inermis
Calystegia sepium
Carex spp.
Carex vulpinoidea
Carya ovata
Cerastium fontanum
Cirsium arvense
Cirsium vulgare
Convolvulus arvensis
Cryptotaenia canadensis
Daucus carota
Dactylis glomerata
Dichanthelium aciculare
Digitaria ischaemum
Echinochloa crus-galli
Eleocharis acicularis
Row crop
8.37 (11.84)
17.60 (24.90)
19.10 (15.20) *
4.50 (6.36)
26.80 (37.90)
4.19 (5.92)
21.70 (30.6)
17.27 (11.70)
4.29 (6.07)
4.19 (5.92)
5.44 (7.69)
-
Adjacent Land Use
Grassed
Row crop/
pasture
grassed pasture
12.70 (22.00)
2.09 (4.19)
2.86 (4.96)
5.79 (5.03)
2.09 (4.19)
20.80 (18.20)
4.19 (4.84)
4.50 (5.22)
Lawn/wooded
4.19 (5.92)
8.69 (0.44)
2.86 (4.96)
2.86 (4.96)
2.86 (4.96)
2.86 (4.96)
2.79 (4.84)
3.00 (5.20)
11.20 (19.30)
14.03 (12.82)
25.47 (0.43)
32.30 (29.60)
7.53 (13.04)
13.67 (16.94)
6.00 (10.39)
3.00 (5.20)
8.79 (0.30)
9.00 (<0.001)
8.58 (12.14)
9.10 (0.74)
4.29 (6.07)
9.94 (1.33)
4.50 (6.36)
-
6.39 (4.26)
7.84 (15.69)
2.15 (4.29)
6.49 (12.98)
2.25 (4.50)
6.59 (8.35)
2.15 (4.29)
8.37 (9.67)
8.64 (0.26)
10.90 (21.80)
23.45 (16.18)
25.20 (30.90)
7.84 (15.69)
-
Table 3.5. Continued
65
Table 3.5. Continued
Elymus riparius
Equisetum arvense
Erigeron spp.
Erigeron annuus
Erigeron strigosus
Eupatorium perfoliatum
Festuca brevipila
Fraxinus americana
Geum rivale
Glechoma hederacea
Helianthus spp.
Hesperis matronalis
Impatiens capensis
Impatiens pallida
Juglans nigra
Juncus tenuis
Laportea canadensis
Lolium perenne ssp.multiflorum
Lolium perenne ssp. perenne
Lonicera canadensis
Lysimachia nummularia
Matricaria discoidea
Medicago lupulina
Medicago sativa
Mentha ×piperita
Mentha spicata
Monarda fistulosa
Morus alba
Osmorhiza longistylis
Oxalis stricta
Panicum virgatum
Parthenocissus quinquefolia
Phalaris arundinacea
Phleum pretense
Pilea pumila
Plantago lanceolata
Plantago major
Poa annua
Poa pratensis
4.29 (6.07)
9.10 (12.88)
18.10 (0.74)
15.00 (21.20)
4.29 (6.07)
18.50 (26.20)
14.20 (20.10)
4.81 (6.81)
21.60 (18.60)
8.58 (12.14)
5.44 (7.69)
49.90 (23.80)
9.31 (0.44)
4.29 (6.07)
4.19 (5.92)
-
20.00 (17.80)
11.51 (13.22)
6.21 (10.75)
17.86 (15.54)
2.79 (4.84)
2.79 (4.84)
25.60 (22.70)
8.37 (8.37)
18.28 (16.96)
2.79 (4.84)
2.79 (4.84)
14.38 (9.67)
11.38 (9.85)
2.79 (4.84)
36.80 (17.70)
3.62 (6.28)
15.69 (11.96)
8.44 (14.63)
29.20 (25.3)
22.54 (12.79)
11.51 (9.97)
25.00 (23.90)
6.33 (8.11)
2.09 (4.19)
2.41 (4.81)
2.15 (4.29)
21.98 (17.17)
11.46 (11.94)
12.90 (25.80)
8.43 (11.94)
2.09 (4.19)
2.09 (4.19)
6.49 (12.98)
6.39 (12.77)
4.34 (5.02)
2.15 (4.29)
20.99 (16.09)
4.29 (4.96)
2.09 (4.19)
24.80 (37.20)
6.54 (13.08)
6.28 (8.02)
4.24 (4.90)
4.34 (8.69)
9.83 (11.48)
11.25 (11.22)
4.29 (6.07)
4.29 (6.07)
13.29 (6.07)
34.85 (12.29)
4.29 (6.07)
4.29 (6.07)
53.70 (19.00)
9.94 (14.05)
4.50 (6.36)
4.29 (6.07)
9.10 (12.88)
4.19 (5.92)
34.75 (13.32)
4.29 (6.07)
17.69 (0.44)
21.35 (6.22)
8.58 (12.14)
-
Table 3.5. Continued
66
Table 3.5. Continued
Podophyllum peltatum
Polygonum hydropiper
Polygonum pensylvanicum
Polygonum persicaria
Polygonum sagittatum
Polygonum scandens
Prunus serotina
Prunella vulgaris
Pyrola elliptica
Ranunculus repens
Rosa multiflora
Rubus allegheniensis
Rubus occidentalis
Rumex acetosella
Rumex crispus
Sanicula canadensis
Sanicula marilandica
Sambucus nigra
Saponaria officinalis
Schedonorus phoenix
Sisymbrium officinale
Solanum carolinense
Solidago altissima
Solidago caesia
Solidago canadensis
Solidago juncea
Solidago nemoralis
Sonchus arvensis
Sorghastrum nutans
Stellaria graminea
Strophostyles umbellata
Symphyotrichum prenanthoides
Taraxacum officinale
Thalictrum pubescens
Toxicodendron radicans
Trifolium pratense
Trifolium repens
Urtica dioica
Verbascum thapsus
4.81 (6.81)
8.48 (11.99)
8.35 (11.81)
8.69 (0.44)
4.50 (6.36)
15.06 (5.92)
8.92 (12.61)
4.29 (6.07)
4.19 (5.92)
11.29 (2.36)
4.19 (5.92)
12.90 (18.20)
4.29 (6.07)
12.87 (6.36)
4.19 (5.92)
9.10 (12.88)
8.79 (0.30)
-
8.37 (8.37)
2.86 (4.96)
2.79 (4.84)
8.44 (8.48)
6.00 (0.39)
14.00 (17.60)
2.86 (4.96)
33.00 (29.30)
3.00 (5.20)
3.00 (5.20)
8.44 (14.63)
11.65 (13.59)
5.58 (9.67)
17.51 (15.17)
30.99 (5.08)
31.50 (25.60)
2.86 (4.96)
-
2.09 (4.19)
2.15 (4.29)
2.15 (4.29)
2.25 (4.50)
4.81 (5.65)
2.25 (4.50)
6.33 (12.67)
2.15 (4.29)
35.00 (20.70)
2.25 (4.50)
6.33 (8.11)
2.72 (5.44)
6.75 (8.62)
4.24 (4.90)
22.10 (25.50)
2.09 (4.19)
2.09 (4.19)
4.24 (8.48)
21.25 (17.84)
4.81 (9.63)
2.15 (4.29)
31.20 (31.40)
11.60 (23.10)
2.15 (4.29)
4.19 (5.92)
4.19 (5.92)
18.60 (26.30)
5.44 (7.69)
4.50 (6.36)
4.81 (6.81)
5.44 (7.69)
14.23 (4.74) *
5.44 (7.69)
4.29 (6.07)
8.79 (12.43)
17.4 (24.60)
9.63 (<0.0001)
4.29 (6.07)
9.63 (<0.0001)
-
Table 3.5. Continued
67
Table 3.5. Continued
Verbesina alternifolia
Vernonia gigantea
Veronica arvensis
Veronica peregrine
Viola canadensis
Viola sororia
Xanthium strumarium
Zea mays
20.60 (29.10)
18.30 (25.90)
4.29 (6.07)
4.19 (5.92)
15.83 (10.75)
2.79 (4.84)
37.20 (24.80)
-
68
10.56 (14.79)
2.09 (4.19)
2.09 (4.19)
2.09 (4.19)
17.58 (19.45)
-
4.19 (5.92)
22.50 (16.40)
-
Table 3.6. Mean (+ 1 standard deviation) importance percentage of life-form guilds for
riparian areas adjacent to row crop, lawn, wooded, and row crop/lawn land uses for the
Upper subwatershed in the Sugar Creek watershed in northeastern Ohio. An asterisk
indicates a significant difference among the riparian types (Kruskall-Wallis; P < 0.05).
Life-form
Annual forbs
Perennial forbs
Graminoids
Pteridophytes
Woody vines
Woody shrubs
Woody tree seedlings
Total Cover
Row crop
20.02 (9.87)
22.86 (3.29)
42.38 (14.68)
2.86 (<0.001)
0
19.67 (9.90)
0
107.79 (15.57)
Adjacent Land Use
Lawn
Wooded
13.21 (6.43)
17.48 (17.26)
20.42 (1.84)
14.05 (4.29)
22.08 (16.13)
12.32 (13.01)
0
2.41 (<0.001)
4.86 (<0.001)
20.51 (19.02)
5.55 (3.28)
16.02 (9.07)
7.01 (2.54)
14.78 (12.28)
73.12 (8.35)
97.56 (5.72)
69
Row crop/lawn
8.58 (<0.001)
14.16 (<0.001)
19.65 (<0.001)
0
32.83 (<0.001)
13.19 (<0.001)
25.33 (<0.001)
113.75 (10.85)
Table 3.7. Mean (+ 1 standard deviation) importance percentage of life-form guilds for
riparian areas adjacent to grassed pasture, wooded pasture, row crop/lawn, and row
crop/grassed pasture land uses for the North Fork subwatershed in the Sugar Creek
watershed in northeastern Ohio. An asterisk indicates a significant difference among the
riparian types (Kruskall-Wallis; P < 0.05).
Life-form
Annual forbs
Perennial forbs
Graminoids
Pteridophytes
Woody vines
Woody shrubs
Woody tree seedlings
Total Cover
Grassed pasture
16.27 (9.28)
17.56 (3.40)
27.37 (7.02)
4.24 (<0.001)
2.09 (<0.001)
5.98 (0.07)
2.09 (<0.001)
75.61 (9.73)
Adjacent Land Use
Wooded pasture
Row crop/lawn
47.29 (<0.001)
15.96 (<0.001)
18.20 (<0.001)
0
0
24.98 (<0.001)
8.58 (<0.001)
115.01 (16.48)
70
16.04 (<0.001)
13.74 (<0.001)
17.74 (<0.001)
0
0
8.38 (<0.001)
0
55.90 (8.01)
Row crop/
grassed pasture
9.28 (<0.001)
18.55 (<0.001)
21.70 (<0.001)
16.75 (<0.001)
0
0
0
66.28 (9.61)
Table 3.8. Mean (+ 1 standard deviation) importance percentage of functional life-form guilds for riparian areas adjacent to row crop,
grassed pasture, wooded pasture, row crop/grassed pasture, and lawn/wooded land uses for the South Fork subwatershed in the Sugar
Creek watershed in northeastern Ohio. An asterisk indicates a significant difference among the riparian types (Kruskall-Wallis; P <
0.05).
71
Life-form
Row crop
Grassed pasture
Adjacent Land Use
Wooded pasture
Annual forbs
Perennial forbs
Graminoids
Pteridophytes
Woody vines
Woody shrubs
Woody tree
seedlings
Total Cover
22.44 (0.52)
18.56 (2.62)
18.32 (7.82)
0
7.27 (3.67)
13.19 (2.15)
10.82 (3.82)
16.24 (7.67)
21.38 (1.87)
23.91 (10.00)
20.03 (<0.001)
0
6.00 (<0.001)
5.79 (<0.001)
90.60 (7.69)
93.35 (9.29)
Lawn/ wooded
13.98 (<0.001)
13.54 (<0.001)
13.02 (<0.001)
0
0
14.21 (<0.001)
8.38 (<0.001)
Row crop/ grassed
pasture
11.31 (4.70)
21.44 (5.46)
25.92 (9.36)
6.33 (<0.001)
6.91 (<0.001)
7.06 (<0.001)
0
63.13 (6.47)
78.97 (9.18)
84.36 (8.41)
71
25.34 (12.51)
15.24 (1.92)
9.37 (0.42)
0
13.66 (0.31)
16.57 (12.65)
4.19 (<0.001)
Table 3.9. Native or non-native species status, conservation status, and rounded I-Rank
determined from the NatureServe explorer online database for riparian areas adjacent to
various land uses for the Upper subwatershed in the Sugar Creek watershed in
northeastern Ohio.
Native Species
Non-Native Species
Conservation Status
G5
GNR
N4
N5
NNR
NNA
Rounded I-Rank
High
Medium
Low
Insignificant
Not yet assessed
Adjacent Land Use
Wooded
30
18
Row crop
20
10
Lawn
28
29
25
7
1
20
4
8
34
23
1
26
5
26
33
15
0
27
6
14
10
8
1
6
3
9
3
4
2
0
23
9
4
4
0
37
7
5
2
0
33
3
4
2
0
8
72
Row crop/ lawn
8
9
Table 3.10. Native or non-native species status, conservation status, and rounded I-Rank
determined from the NatureServe explorer online database for riparian areas adjacent to
various land uses for the North Fork subwatershed in the Sugar Creek watershed in
northeastern Ohio.
Grassed pasture
Native Species
Non-Native Species
Conservation Status
G5
GNR
N1
N4
N5
NNR
NNA
Rounded I-Rank
High
Medium
Low
Insignificant
Not yet assessed
Adjacent Land Use
Wooded pasture
Row crop/lawn
30
36
12
10
13
15
Grassed pasture/
row crop
13
16
33
28
1
1
25
5
31
14
9
0
0
11
3
9
18
12
1
0
15
2
12
16
9
0
0
11
3
13
6
9
5
1
42
5
3
1
0
13
4
3
2
0
18
2
3
2
0
19
73
Table 3.11. Native or non-native species status, conservation status, and rounded I-Rank
determined from the NatureServe explorer online database for riparian areas adjacent to
various land uses for the South Fork subwatershed in the Sugar Creek watershed in
northeastern Ohio.
Row crop
Native Species
Non-Native Species
Conservation Status
G5
GNR
N4
N5
NNR
NNA
Rounded I-Rank
High
Medium
Low
Insignificant
Not yet assessed
Adjacent Land Use
Wooded
Row crop/
pasture
grassed pasture
21
37
9
31
26
20
Grassed
pasture
35
26
28
16
0
25
3
17
38
21
0
31
6
24
22
7
0
20
2
8
41
23
1
31
9
28
27
13
0
21
6
15
5
4
1
1
33
2
7
3
0
44
3
3
1
0
22
6
9
4
1
47
3
6
2
0
30
74
Lawn/
wooded
26
18
18
row crop
lawn
grassed pasture
wooded pasture
wooded
row crop/lawn
row crop/grassed pasture
lawn/wooded
16
Species Richness m 2
14
12
10
8
6
4
2
0
Upper
North Fork
South Fork
Subwatershed
Figure 3.1. Mean (+ 1 standard deviation) richness values for riparian areas adjacent to
various land uses associated with the Upper, North Fork, and South Fork subwatersheds
in the Sugar Creek watershed in northeastern Ohio.
75
3.0
row crop
lawn
grassed pasture
wooded pasture
wooded
row crop/lawn
row crop/grassed pasture
lawn/wooded
Shannon Diversity Index
2.5
2.0
1.5
1.0
0.5
0.0
Upper
North Fork
South Fork
Subwatershed
Figure 3.2. Mean (+ 1 standard deviation) Shannon’s Index values for riparian areas
adjacent to various land uses associated with the Upper, North Fork, and South Fork
subwatersheds in the Sugar Creek watershed in northeastern Ohio.
76
1.0
row crop
lawn
grassed pasture
wooded pasture
wooded
row crop/lawn
row crop/grassed pasture
lawn/wooded
Evenness
0.8
0.6
0.4
0.2
0.0
Upper
North Fork
South Fork
Subwatershed
Figure 3.3. Mean (+ 1 standard deviation) evenness values for riparian areas adjacent to
various land uses associated with the Upper, North Fork, and South Fork subwatersheds
in the Sugar Creek watershed in northeastern Ohio.
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CHAPTER 4
WOODY RIPARIAN VEGETATION COMPOSITION AND STRUCTURE
ASSOCIATED WITH ADJACENT LAND USES AND VEGETATIONENVIRONMENTAL RELATIONSHIPS IN AN AGRICULTURAL WATERSHED OF
NORTHEASTERN OHIO
Introduction
Riparian areas are transition zones, or ecotones, between terrestrial and aquatic
ecosystems that provide many important functions which are critical to overall watershed
health (Gregory et al. 1991; Naiman et al. 1993; Molles 2002; Mitsch and Gosselink
2007). Some of these functions include retaining nutrients, sediments, and water, as well
as shading the stream, providing organic matter and large wood for streams, and reducing
stream-bank erosion (Vannote et al. 1980; Lowrance et al. 1984; Gregory et al. 1991;
Osborne and Kovacic 1993). Riparian areas may only constitute a small proportion of a
watershed, yet the ecosystem functions they provide illustrate their importance in the
landscape (e.g., Gregory et al. 1991; Roy et al. 2005; Meyer et al. 2007).
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Despite the importance of riparian areas, many are highly disturbed, and in the
Midwestern U.S., many agricultural watersheds lack natural riparian forests (Peterjohn
and Correll 1984; Naiman et al. 1993; Tockner and Stanford 2002). Reasons for
vegetation removal include grazing livestock, providing water sources for livestock, and
growing crops. The loss of riparian forests has many negative effects for both terrestrial
organisms and stream ecosystems. Specifically, excess nutrients and sediments degrade
water quality (Naiman et al. 1988; Osborne and Kovacic 1993).
Increased nutrient concentrations in streams leads to an increase in primary
production (Allan et al. 1997; Carpenter et al. 1998), and this higher biological activity
often results in algal blooms which decrease dissolved oxygen, leading to fish kills and
overall habitat degradation (e.g., Carpenter et al. 1998; Sponseller et al. 2001). Allowing
livestock free access to streams also causes stream-bank erosion, and the resultant
siltation may also increase stream nutrient loading when phosphorus is attached to soil
particles (Carpenter et al. 1998). Siltation also decreases the stream habitat suitability for
aquatic organisms through increasing turbidity and covering spawning habitat with
sediment (Allan et al. 1997; Stauffer et al. 2000). All of these conditions create an
environment which is suitable only for aquatic organisms that can survive in poor quality
habitat (Allan et al. 1997; Carpenter et al. 1998; Stauffer et al. 2000; Sponseller et al.
2001).
Although land use practices impact the aquatic ecosystem, terrestrial organisms
are also affected. Livestock use of streams has caused bacterial issues, e.g., E. coli, which
can cause livestock health issues, including colitis (Wang et al. 1996). E. coli survives in
cattle fecal matter, which leads to water contamination (Wang et al. 1996). Human health
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may also be affected by land use practices. For example, high nitrate levels in drinking
water have been linked to non-Hodgkin’s lymphoma (Ward et al. 1996).
In an effort to better understand the factors that regulate riparian forest
development and to help guide the management and restoration of riparian areas, many
studies have focused on the influence of fluvial geomorphology and flooding on riparian
communities’ composition, structure, and diversity (e.g., Gregory et al. 1991; Holmes et
al. 2005; Goebel et al. 2006). However, these studies were located in relatively
undisturbed settings, not in areas where terrestrial land-use practices have significantly
impacted the development of riparian plant communities. As many current riparian
restoration efforts are occurring in highly disturbed watersheds, including agricultural
watersheds of the Midwestern U.S., it is also important to focus research on
understanding the factors that regulate plant community development in these disturbed
riparian areas.
One factor that is likely to be an important driver of riparian community
development in disturbed watersheds is adjacent terrestrial land use (Moffatt et al. 2004).
For example, the composition and structure of riparian vegetation is an important
determinant of the functioning, as well as the efficiency, of riparian areas (Naiman et al.
1988). An intact, riparian forest provides shade to streams (Osborne and Kovacic 1993),
and with tree removal, stream temperatures rise, and primary production increases
(Vannote et al. 1980; Gregory et al. 1991; Lyons et al. 2000). Additionally, most stream
ecosystems flowing through riparian forests are heterotrophic, and increased stream
temperatures and reductions in organic inputs to the stream negatively affect aquatic
organisms that need these cool conditions to thrive (Gregory et al. 1991). As a result,
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streams flowing through open riparian areas contain fewer fish and insects than streams
flowing through riparian forests (Stauffer et al. 2000).
Riparian vegetation also influences the amount of nutrients and sediments
entering the stream ecosystem. Riparian forests are known to be efficient at removing
nitrogen and other nutrients (Osborne and Kovacic 1993). While both riparian grasses
and trees will reduce stream-bank erosion (Gregory et al. 1991; Lyons et al. 2000; Zaimes
et al. 2004), tree roots penetrate deeper into the soil than when perennial grasses
dominate riparian areas, and are thus able to remove more water and nutrients from
subsurface water (Lyons et al. 2000). Unfortunately, in many agricultural watersheds, any
benefit from riparian vegetation is lost if the areas are used for livestock grazing (Zaimes
et al. 2004). These pastures, along with row crop agriculture, were found to increase
stream sediment loadings through stream-bank erosion much more than forests (Zaimes
et al. 2004). The degradation of water resources and aquatic food webs has also been
attributed to adjacent terrestrial land-use practices, especially within agricultural
watersheds (Turner and Rabalais 1991; Mitsch et al. 2001; Holmes 2004; United States
Environmental Protection Agency 2009).
Agriculture is the dominant source of water-quality impairment of the nation’s
rivers and streams (United States Environmental Protection Agency 2009). This land u se
is extensive throughout the Midwest and poor agricultural practices, as well as riparian
vegetation removal and degradation, here have been attributed as the leading cause of the
extensive hypoxia zone in the Gulf of Mexico (Turner and Rabalais 1994; Mitsch and
Gosselink 2007). Approximately 90 percent of the nitrate-nitrogen input into the Gulf of
Mexico can be attributed to the Ohio, Missouri, and upper Mississippi Rivers (Mitsch and
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Day 2006). The Ohio River is estimated to contribute 34 percent of the total nitrogen
entering the Gulf of Mexico, and most of these nutrients originate in agricultural
watersheds in the headwaters (Mitsch and Day 2006). One such watershed is the Sugar
Creek watershed in northeastern Ohio, which drains 925 km2 in Wayne, Stark, Holmes,
and Tuscarawas Counties (Ohio Environmental Protection Agency 2002). Increased
nutrient levels in Sugar Creek, therefore, contributes to the hypoxia zone in the Gulf of
Mexico. This watershed is dominated (> 70 percent) by agricultural land uses and,
primarily due to current land-use practices, is considered the second most degraded
watershed in Ohio (Ohio Environmental Protection Agency 2002). However, restoration
potential is high due to the sources of degradation within the watershed, i.e., sediment
and nutrient loading and in-stream habitat change (Ohio Environmental Protection
Agency 2002).
Water-quality issues and land-use practices in the Sugar Creek watershed are
similar throughout other agricultural watersheds in the Midwest, so this project has
become a national model for building “local community capacity” for watershed
management and restoration (Ohio Environmental Protection Agency 2002). As this
watershed is a model for restoration, and nutrient loading is a significant problem causing
water-quality issues that can be reduced with riparian forests, baseline conditions must be
identified. These baseline conditions can be used for evaluating the effectiveness of
restoration efforts. However, little information is available on the current condition of
riparian vegetation and factors that regulate the development of riparian vegetation in the
watershed, as well as how land-use practices affect riparian plant communities. Most
research to date has focused on water quality and stream habitat at individual-reach scales
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(Herrman et al. 2008a; D’Ambrosio et al. 2009), although some studies have focused on
watershed-scale land uses and its effect on water quality (Holmes 2004; Prasad et al.
2005; Herrman et al. 2008b).
The objective of this study was to examine the influence of adjacent land use on
the composition and structure of woody vegetation in riparian areas of an agricultural
watershed. Specifically, we addressed the following questions. First, what differences are
there in the overstory and understory composition and structure of riparian areas
associated with different adjacent land uses? Second, what factors (e.g., physiographic
characteristics, adjacent land use, and stream characteristics) are most important in
regulating the overstory and understory vegetation layers of riparian areas in agricultural
watersheds? Third, how can this information be utilized to help guide the restoration of
riparian areas in agricultural watersheds in the Midwestern United States?
Study Area
We focused our efforts in three representative subwatersheds in the Sugar Creek
watershed. The Upper Sugar Creek subwatershed (hydrologic unit code (HUC) 05040001-100) is located in the headwaters of the Sugar Creek in Wayne County, the
North Fork subwatershed (HUC 05-040001-100) is a mid-stem tributary located in
Holmes and Wayne Counties, and the South Fork subwatershed (HUC 05-040001-110) is
a lower-stem tributary located in Holmes and Tuscarawas Counties (Ohio Environmental
Protection Agency 2002).
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The Upper and North Fork subwatersheds are located in the glaciated Erie Drift
Plains Ecoregion (United States Environmental Protection Agency 2007). The landscape
of this Ecoregion, dominated by glacial landforms of Wisconsinan age, is characterized
by gently sloping topography and poorly drained soils (Bureau et al. 1984; Seaholm and
Graham 1997; Ohio Environmental Protection Agency 2002). These areas are highly
productive and are characterized by a mixture of both large- and small-scale dairy and
row-crop agriculture (Bureau et al. 1984; Seaholm and Graham 1997). The South Fork
subwatershed is located in the unglaciated Western Allegheny Plateau Ecoregion (United
States Environmental Protection Agency 2007) and is characterized by steeper slopes and
higher stream gradients than the Upper and North Fork subwatersheds (Ohio
Environmental Protection Agency 2002). Due to the limitations of steep terrain on rowcrop agriculture, there are many small-scale dairy operations in this subwatershed
(Seaholm and Graham 1997).
The climatic conditions of all three subwatersheds are similar. Mean summer
temperatures range from 20.6 to 21.1°C, and mean winter temperatures range from -1.7
to -2.8°C (Bureau et al. 1984; Waters and Roth 1986; Seaholm and Graham 1997).
Average annual precipitation ranges from 90 to 98.3 cm, with increasing levels as one
moves south from the Upper subwatershed to the South Fork subwatershed (Bureau et al.
1984; Waters and Roth 1986; Seaholm and Graham 1997). Over half of the precipitation
falls between April and September in all three subwatersheds (Bureau et al. 1984; Waters
and Roth 1986; Seaholm and Graham 1997).
In addition to the physiographic and climatic gradients associated with the three
subwatersheds, Parker (2006) describes the following differences in the social structure
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between the subwatersheds. The Upper subwatershed is dominated by non-Amish
farmers and larger municipalities compared with the other two subwatersheds. In the
North Fork and South Fork subwatersheds, Amish communities are more common
resulting in smaller farms and more mixed agricultural practices, i.e., both crop and
animal production on the same farm, than found in the Upper subwatershed (Parker
2006).
Methods
Field and Laboratory Analyses
Within each subwatershed, we utilized existing sample locations established for a
water-quality monitoring program to sample riparian vegetation during June through
August of 2008. Sampling locations are included in Appendix A. Because these waterquality monitoring points were established near roads, we established our sample
transects 30-m upstream or downstream from these locations to reduce the effects of
roads.
We established two 100-m transects, one on each side of the stream, 2-m from the
stream-bankfull stage. Along each transect, we sampled sampling woody vegetation
using the point-centered quarter method (Cottam and Curtis 1956; Bonham 1989). This
method has been shown to be an effective method to sample narrow riparian areas in
other regions (e.g., Palik et al. 1998). When sampling occurred upstream of the road, the
first point was established 10-m upstream from the start of the 100-m transect, with four
additional points established at 20-m intervals along the transect (e.g., 30-, 50-, 70-, and
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90-m). If the transect was located downstream from the road, the first point was sampled
122-m downstream from the bridge, with four additional points established at 20-m
intervals upstream from the first point (e.g., 100-m, 80-, 60-, and 40-m). As we sampled
both sides of the stream, each sampling location had ten sampling points.
At each point, we divided the riparian area into four 90° quarters using the four
cardinal directions (i.e., north, east, south, and west). Within each quarter we sampled
living trees >10.0 cm at diameter at breast height (dbh) (1.37 m from the ground) to
collect information about the overstory layer in riparian areas. We recorded the species,
dbh, and crown class (overtopped, intermediate, co-dominant, dominant) of the closest
living tree, as well as the distance from the center of the tree to the point. If no tree
existed within 20.0 m of the point, none was recorded in the specific quarter. At the
center of each point, we established a 1.78-m radius plot for sampling the understory
layer in riparian areas, i.e., saplings (stems <10.0 cm dbh and >1.0 m tall) and recorded
species and dbh. Nomenclature follows the PLANTS database (United States Department
of Agriculture, Natural Resources Conservation Service 2009).
We took hemispherical photographs using a Nikon Coolpix 8400 digital camera
with a Nikon FC-E9 Fisheye lens 2 m to the right of every other woody vegetation
sampling point to determine canopy closure, totaling six hemispherical photographs per
sampling location. The camera was mounted 1-m above the ground on a tripod. In the
laboratory, we utilized the software program WinSCanopy (Regent Instruments, Inc.,
Canada) for digital image processing and determination of percent canopy openness of
each sample location. Specifically, we used pixel classification to define sky versus
canopy. An accurate pixel classification is necessary for analyses; therefore, we used a
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hemispherical threshold compensation to rectify a bright sun in the photograph causing
canopy to be classified as sky. We utilized the six estimates of canopy openness at each
sampling location to calculate mean percent openness among riparian type.
To determine the average age of the overstory, we collected increment cores from
two to four dominant or codominant trees, if present. In the laboratory, each increment
core was mounted on a wooden board and then sanded with a series of progressively finer
sandpapers to allow clear recognition of annual rings, which were then counted by
individual growth rings. To estimate the age of each sampled tree, we added the number
of individual growth rings from the bark to the pith.
In addition to vegetation sampling, we also characterized stream channel
characteristics at each sampling location. Specifically, we measured bankfull width (m)
and depth (m), floodprone area width (maximum bankfull depth divided by the bankfull
width and multiplied by two, then the width of the channel remeasured at this calculated
depth), and percent stream slope, using a Suunto clinometer. Bankfull width was
identified and measured at the same elevation on both sides of the stream using a distance
measuring tape, and bankfull depth measurements were taken at the deepest part of the
stream cross-section of each sampled stream. From this data, we calculated an
entrenchment ratio (floodprone area width divided by bankfull width) to determine the
vertical containment of a stream, as well as the width-to-depth ratio (bankfull width
divided by bankfull depth), which indicates the channel cross-section shape (Rosgen
1996).
Along with this information, we utilized the United States Department of
Agriculture web soil survey to determine the soil series name; parent material (glacial till
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or alluvium); texture, i.e., the proportion of sand, silt, and clay (sandy loam, sandy to silt
loam, silt loam, or fine loamy to silt loam); and color for each sampling location (Soil
Survey Staff, United States Department of Agriculture, Natural Resources Conservation
Service 2008). To verify this information in the field, we collected a soil sample from
each sampling location using a split tube sampler to a maximum depth of 30 cm, and
compared texture and color with what the web soil survey indicated, where possible
given landowner consent.
Data Analyses
Prior to any statistical analyses, we classified each sampling location into a
riparian type based upon the adjacent land use(s), including row crop, lawn, grassed
pasture, wooded pasture, wooded, row crop/lawn, row crop/grassed pasture, and
lawn/wooded. Adjacent land use(s) associated with each study site are presented in
Appendix A. Mixed land-use classes are those that had two major land uses adjacent to
the sampling location. Across the three subwatersheds, we sampled the following riparian
types: row crop (five sampling locations), lawn (four sampling locations), grassed pasture
(seven sampling locations), wooded pasture (two sampling locations), wooded (four
sampling locations), row crop/lawn (two sampling locations), row crop/grassed pasture
(five sampling locations), and lawn/wooded (two sampling locations). The number of
times each soil characteristic occurred was summed for each riparian type. We also
averaged (+ 1 standard deviation) stream characteristics for each riparian type using
Minitab (Version 15.1.2).
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Using the overstory data, we first calculated importance values (IV; sum of
relative density, relative dominance as basal area (basal area = (dbh)2 *0.00007854), and
relative frequency, divided by three) for each species at each sampling location. First, the
mean point-to-tree distance (m) was calculated (mean distance of all trees sampled for the
sampling location), then obtained an estimate of the mean density of trees (m2) for each
sampling location (mean distance2), and then calculated the mean density of trees
(trees/ha) for the sampling location (10,000 m2/(mean distance2)). Next, we determined
the relative density by species for each sampling location ((number of trees/total number
of trees)*100) and then calculated the density by species ((relative density/100)*average
density). To determine relative dominance, we calculated individual tree basal areas. We
then obtained basal area (m2/ha) for each species (mean density*mean basal area), and
then determined the relative basal area for each species ((basal area/sum of all basal
areas)*100). Finally, we calculated the frequency for each species (number of points a
species occurs/total number of points sampled), and relative frequency of each species
((frequency/total frequency for all species)*100).
We calculated the mean species’ IV, mean (+ 1 standard deviation) dbh and mean
canopy openness (+ 1 standard deviation) for each riparian type using Minitab (Version
15.1.2). Finally, we used a non-parametric Kruskall-Wallis test in Minitab (Version
15.1.2) to examine whether there were any significant differences in overstory and
understory composition and structure, as well as environmental variables among riparian
types. We also summarized the total number of trees per crown class for each riparian
type to understand canopy structure.
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To determine if there was a difference in overstory composition associated with
riparian type, we used a multi-response permutation procedure (MRPP) in PC-ORD
(Version 5.0 MjM Software, Gleneden Beach, OR). MRPP is a non-parametric test,
which requires neither normally distributed data nor homogeneity variance (McCune and
Grace 2002). With MRPP, we used Sorenson’s distance and a weighting factor, and made
pairwise comparisons.
Indicator Species Analysis with PC-ORD (Version 5.0 MjM Software, Gleneden
Beach, OR) was used to determine if species distributions were significantly different
from random. We used the species’ IVs for this analysis, grouped the IVs by riparian
type, ran a randomization test, and utilized the Monte-Carlo test results to determine
individual indicator species.
Using the understory data, we calculated species richness (the number of species
present), number of saplings per hectare, and relative abundance by sapling species
among riparian types. To determine if there was a difference in understory composition
associated with the different riparian types, we used MRPP in PC-ORD (Version 5.0
MjM Software, Gleneden Beach, OR). We used the species abundance with a weighting
factor and Sorenson’s distance, and made pairwise comparisons in the software package
PC-ORD (Version 5.0 MjM Software, Gleneden Beach, OR). The row crop/grassed
pasture riparian type was deleted from this analysis because saplings only occurred at one
sampling location. Because MRPP is used for testing no differences among two or more
groups, we were not able to use this statistical analysis for the row crop/grassed pasture
riparian type (McCune and Grace 2002).
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To examine the relationship between overstory species’ IVs and environmental
factors, we used canonical correspondence analysis (CCA) using CANOCO software
(Version 4.53 Biometris-Plant Research International, Wageningen, The Netherlands).
Included in the analysis were three major groups of environmental factors: adjacent land
use (as categorical variables), soil characteristics (soil parent material and soil texture),
and stream characteristics (entrenchment ratio, width-to-depth ratio, and channel slope).
Results
Stream, Physiographic, and Soil Characteristics
We found no significant differences in stream characteristics, including
entrenchment ratio (P = 0.3550), width-to-depth ratio (P = 0.1500), and slope (P =
0.5220), among riparian types (Table 4.1). The soil profiles of most riparian types are
formed in alluvium (about 81 percent), while the remainder developed in glacial till
parent material (Table 4.2). Soil texture for all riparian type soils is predominantly (about
90 percent) silt loam, while the remaining soils consist of fine loamy to silt loam, and
sandy loam texture (Table 4.2).
Overstory Structure
We detected no significant differences in mean tree dbh (P = 0.0640) (Table 4.3).
We found that canopy openness differs significantly (P = 0.0070) among riparian types
(Figure 4.1). Specifically, we found that the riparian type with the most open canopy is
the row crop/grassed pasture riparian type (83.42% + 7.06%) (Figure 4.1), while the
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wooded pasture and wooded land uses have mean openness values of 16.86% (+ 8.86%)
and 13.56% (+ 6.63%), respectively (Figure 4.1). When we examined overstory structure
in terms of crown class, we found that the most diverse crown structure was associated
with the wooded riparian type (35 overtopped trees, 38 intermediate trees, 35 codominant trees, and 10 dominant trees) (Figure 4.2), while the row crop/grassed pasture
riparian type has the least diverse canopy structure (0 trees in the overtopped,
intermediate, and co-dominant crown classes, and 4 dominant trees) (Figure 4.2).
Average tree age was not significantly different (P = 0.4160) among riparian types, and
ranged from 22 to 113 years old.
Overstory Composition
The MRPP results indicate no significant differences in overstory composition
among riparian types (T = -0.272; P = 0.3650). We found only one pairwise comparison
resulted in a significant difference among two riparian types, i.e., among the lawn and
wooded pasture riparian types (T = -1.842; P = 0.0440).
The most ubiquitous overstory species is black cherry (Prunus serotina Ehrh.)
with the highest IV in six of the eight riparian types (Table 4.4). Other common species
include black walnut (Juglans nigra L.), red maple (Acer rubrum L.), and green ash
(Fraxinus pennsylvanica Marsh.) (Table 4.4). Red maple has the highest IV in the row
crop riparian type, black cherry in the lawn riparian type, and both white ash (Fraxinus
americana L.) and black cherry in the grassed pasture riparian type (Table 4.4). In the
wooded pasture riparian type, northern catalpa (Catalpa speciosa (Warder) Warder ex
Engelm.), American beech (Fagus grandifolia Ehrh.), and black walnut are the dominant
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species, whereas in the wooded riparian type, sugar maple (Acer saccharum Marsh.),
green ash, and black cherry are the dominant species (Table 4.4). In the row crop/lawn
riparian type, black walnut and black cherry are the species with the highest IVs;
however, in the row crop/grassed pasture riparian type, shagbark hickory (Carya ovata
(Mill.) K. Koch) and northern catalpa are the dominant species (Table 4.4). In the
lawn/wooded riparian type, black walnut and American sycamore (Platanus occidentalis
L.) are the dominant species (Table 4.4). Silver maple (Acer saccharinum L.) (P = 0326)
and red mulberry (Morus rubra L.) (P = 0.0248) have significantly higher cover in the
lawn/wooded riparian type (Table 4.4). No other indicator species were determined
(Table 4.4).
Understory Structure and Composition
The wooded riparian type has the highest understory density (774.19/ha +
2.33/ha), while the row crop/lawn riparian type has the lowest density (64.52/ha +
2.22/ha) (Table 4.5). Understory density is significantly different (P = 0.0020) among
riparian types. Both wooded and lawn/wooded riparian types have the highest understory
species richness (six species in both riparian types) of all riparian types (Table 4.5).
We found no significant differences in understory composition among riparian
types (T = -0.173; P = 0.3250) (Table 4.5). Black cherry occurred at four sites, the most
of any of the nineteen understory species sampled, and was the most common species in
both the row crop and wooded riparian types (Table 4.5). In the lawn riparian type,
American cranberrybush (Viburnum opulus L.) was the most common species in the
understory layer (Table 4.5). The most common species in the grassed pasture riparian
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type was willow (Salix spp.) (Table 4.5). In the wooded pasture riparian type, both
American beech and black walnut were the most common species in the understory layer
(Table 4.5), while hawthorn (Crataegus spp. L.) and northern red oak (Quercus rubra L.)
were the most common species in the row crop/lawn riparian type (Table 4.5). In the
lawn/wooded riparian type, American hornbeam (Carpinus caroliniana Walter) was the
most common species (Table 4.5).
Vegetation-Environment Relationships
CCA results identified associations between overstory vegetation and
environmental factors, explaining 18.4% of the variation in the overstory vegetation
along the first two canonical axes (Table 4.6). For the most part, overstory species are
arrayed along the first axis (eigenvalue = 0.664) (Figure 4.4) and explains 9.6 percent of
the variation in environmental factors, while the second axis explains an additional 8.8
percent (Table 4.6).
Wooded pasture, row crop/lawn, and row crop/grassed pasture riparian types, as
well as fine loamy to silt loam and sandy loam soil textures, and percent slope of stream
are positively associated with axis 1 (Figure 4.4), as were sampling locations S5A, S2A,
N11, S3A, and S2B (Figure 4.5). Tree species positively associated with the CCA axis 1
are eastern cottonwood (Populus deltoides Bartram ex Marsh.), hawthorn, northern
catalpa, and shagbark hickory (Figure 4.4). No environmental factors were negatively
associated with axis 1 (Figure 4.4). Black willow (Salix nigra Marsh.) is the only species
which is negatively associated with axis 1 (Figure 4.4). We suggest axis 1 to be a gradient
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of adjacent land use disturbance because the species listed above typically grow well in
open, disturbed areas (Little 1980; Kirwan et al. 2004).
Row crop and lawn/wooded riparian types and alluvium parent material are
positively associated with axis 2, as were sampling locations S6B and S6A (Figures 4.4
and 4.5). Associated positively with the second axis are American sycamore, chinkapin
oak (Quercus muehlenbergii Engelm.), honeylocust (Gleditsia triacanthos L.), pin oak
(Quercus palustris Münchh.), red mulberry (Morus rubra L.), sassafras (Sassafras
albidum (Nutt.) Nees), red maple, sweetgum, white mulberry (Morus alba L.), and
slippery elm (Ulmus rubra Muhl.) (Figure 4.4). Glacial till parent material is negatively
associated with axis 2 as were the U24C and U20A sampling locations (Figures 4.4 and
4.5). Tree species including American elm (Ulmus americana L.), bitternut hickory
(Carya cordiformis (Wangenh.) K. Koch), blackgum (Nyssa sylvatica Marsh.), Callery
pear (Pyrus calleryana Decne.), cherry spp. (Prunus spp L.), northern red oak, and willow
spp. are negatively associated with the second axis (Figure 4.4). The species highly
associated with this axis typically grow well in open disturbed areas (Little 1980; Kirwan
et al. 2004); thus, we determined axis 2 to also be a gradient of adjacent land use
disturbance. No other species were good discriminators of either axis (Figure 4.4).
Discussion
Although it is well understood that riparian forests provide many important
functions (Vannote et al. 1980; Gregory et al. 1991; Osborne and Kovacic 1993), riparian
vegetation removal and degradation has resulted in poorly functioning riparian areas
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(Peterjohn and Correll 1984; Naiman et al. 1993; Tockner and Stanford 2002).
Particularly in agricultural watersheds, the effects of riparian forest loss have negatively
affected stream water quality and aquatic food webs (United States Environmental
Protection Agency 2009). Information is needed on the factors that regulate riparian
community development and species patterns. With this knowledge, we can manipulate
those factors in our control, i.e., adjacent land use, through better management practices
to restore riparian vegetation and function.
Vegetation-Environment Relationships
Riparian woody vegetation patterns in the Sugar Creek watershed are driven by
disturbance. Other studies of riparian vegetation also found that underlying factors, e.g.,
stream geomorphology, adjacent land use disturbance, and floodplain landforms,
explained patterns in riparian vegetation (e.g., Hupp and Osterkamp 1996; Moffatt et al.
2004; Holmes et al. 2005; Goebel et al. 2006). Specifically, Hupp and Osterkamp (1996)
determined that throughout the United States, riparian vegetation is related to fluvial
geomorphology, especially along channelized streams when channel gradient increases.
Geomorphology was also found to be the factor driving riparian vegetation in forested
landscapes in both Wisconsin (Goebel et al. 2006) and Ohio (Holmes et al. 2005).
Moffatt et al. (2004) found that land use affected species composition in a watershed
disturbed by both agriculture and urbanization in Canada.
Our ordination illustrates the importance of disturbance adjacent to riparian areas
in driving riparian woody vegetation. Overstory species including eastern cottonwood,
hawthorn, northern catalpa, and shagbark hickory were associated with disturbed riparian
96
types, i.e., wooded pasture, row crop/lawn, and row crop/grassed pasture riparian types.
This is not surprising given the life-history traits of these species. Eastern cottonwood is a
very shade intolerant species which prefers wet soils (Kirwan et al. 2004), as does
northern catalpa (Little 1980). Although not as intolerant to shade, hawthorn and
shagbark hickory are characterized by an intermediate shade tolerance (Kirwan et al.
2004). A few examples of overstory species associated with the lawn/wooded riparian
type include American sycamore, honeylocust, red mulberry, and sassafras. As with the
previously listed riparian types, species associated with the lawn/wooded riparian type
are intermediate to intolerant of shade and, thus, grow well in open, disturbed areas
(Kirwan et al. 2004). In addition, American sycamore prefers wet soils along streams and
lakes (Kirwan et al. 2004). Although the wooded riparian type was not associated with
either axis 1 or 2 in the ordination, two sampling locations (U20A and U24C) were
negatively associated with axis 2 and are both characterized as wooded riparian types. A
few examples of species associated with these sites include American elm, blackgum,
cherry spp., northern red oak, and willow spp., and range in shade tolerance from fairly
tolerant to intolerant (Kirwan et al. 2004). Many of the common overstory and understory
species in each riparian type, e.g., black cherry, are also shade intolerant (Kirwan et al.
2004).
Disturbance also may be the cause of similarities found in overstory composition,
which Moffatt et al. (2004) also determined. Long-distance seed dispersal by streams and
wildlife alike can also contribute to vegetation similarities (Gregory et al. 1991). Black
cherry, which is the most ubiquitous species in both the overstory and understory, is
easily dispersed long distances by wildlife (United States Department of Agriculture,
97
Natural Resources Conservation Service, National Plant Data Center and the Biota of
North America Program 2003).
Implications for Restoration
The Sugar Creek watershed is a highly disturbed agricultural watershed with poor
water quality due to land-use practices and poorly functioning riparian areas (Ohio
Environmental Protection Agency 2002). Because of the important functions riparian
areas provide (Lowrance et al. 1984; Gregory et al. 1991; Naiman et al. 1993; Osborne
and Kovacic 1993; Molles 2002; Mitsch and Gosselink 2007), restoration is necessary to
reduce the impacts of land-use practices. We propose using the wooded riparian type as a
goal for restoration efforts because of its structural complexity and the functions wooded
areas provide (Peterjohn and Correll 1984; Diebel et al. 2009). Franklin et al. (2007)
discuss the importance of stand complexity, specifically structure, composition, and
heterogeneity for ecological based management.
Because of resource limitations, we need to choose riparian types in which to
focus restoration efforts. Among the riparian types examined in our study, this includes
the wooded pasture and lawn/wooded riparian types because they are next in structural
complexity after the wooded riparian type, and would, therefore, require less effort to
restore. The extent of effort needed to restore riparian function through restoring structure
and composition depends on the initial successional stage (Rheinhardt et al. 2009).
Rheinhardt et al. (2009) suggests that planting trees may be necessary if composition and
structure of the riparian area was highly altered due to a disturbance. After restoration,
98
the information gathered in this study can be used as baseline conditions by which to
compare riparian vegetation composition and structure.
Few studies have characterized the impacts of land use on riparian vegetation. In
this study we found disturbance to be driving riparian woody vegetation in the Sugar
Creek watershed, and to reduce the impact of adjacent land use practices we determined
riparian areas should be restored to emulate characteristics of wooded riparian types. As a
model for restoration, solutions for improving water quality in the Sugar Creek watershed
can be applied to other agricultural watersheds throughout the Midwest.
99
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Table 4.1. Mean (+ 1 standard deviation) entrenchment ratio, width-to-depth ratio, and
percent slope for riparian areas adjacent to various land uses for stream reaches in the
Upper, North Fork, and South Fork subwatersheds in the Sugar Creek watershed in
northeastern Ohio. An asterisk after a value indicates the stream characteristic was
significantly different among riparian areas adjacent to various land uses (KruskallWallis test; * P < 0.05).
Adjacent Land Use
Row crop
Lawn
Grassed pasture
Wooded pasture
Wooded
Row crop/lawn
Grassed pasture/row crop
Lawn/wooded
Stream Characteristics
Width-to-Depth Ratio
9.20 (3.21)
7.98 (6.44)
10.70 (8.87)
7.91 (5.97)
16.27 (5.00)
10.61 (6.06)
4.60 (0.74)
5.85 (1.74)
Entrenchment Ratio
1.31 (0.27)
2.58 (1.30)
3.10 (1.83)
4.07 (3.89)
1.86 (1.11)
1.86 (0.64)
3.65 (3.82)
2.53 (1.70)
105
Slope (%)
1.38 (0.75)
1.17 (0.29)
2.21 (1.60)
3.25 (2.47)
1.17 (0.29)
0.75 (0.35)
1.50 (0.58)
1.50 (0.71)
Table 4.2. Soil characteristics for riparian areas adjacent to various land uses in the
Upper, North Fork, and South Fork subwatersheds in the Sugar Creek watershed in
northeastern Ohio.
Parent Material
Adjacent Land Use
Row crop
Lawn
Grassed pasture
Wooded pasture
Wooded
Row crop/lawn
Row crop/grassed pasture
Lawn/wooded
alluvium
2
3
4
1
3
1
1
2
till
0
1
1
1
1
1
1
0
silt loam
2
4
3
1
4
2
2
2
106
Soil Texture
fine loamy to
sandy to
silt loam
silt loam
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
sandy
loam
0
0
0
1
0
0
0
0
Table 4.3. Mean (+ 1 standard deviation) diameter at breast height (dbh; centimeters) for
riparian areas adjacent to various land uses for overstory riparian vegetation in the Upper,
North Fork, and South Fork subwatersheds in the Sugar Creek watershed in northeastern
Ohio.
Adjacent Land Use
Row crop
Lawn
Grassed pasture
Wooded pasture
Wooded
Row crop/lawn
Row crop/grassed pasture
Lawn/wooded
Diameter at Breast Height (cm)
39.97 (23.41)
28.34 (17.33)
31.73 (20.54)
36.15 (16.18)
29.77 (15.57)
23.87 (10.48)
47.80 (43.80)
33.24 (21.42)
107
Table 4.4. Mean (+ 1 standard deviation) importance value of riparian overstory (stems > 10 cm diameter at breast height) species for
riparian areas adjacent to various land uses in the Upper, North Fork, and South Fork, located in the Sugar Creek watershed in
northeastern Ohio. An asterisk after a value indicates the species was an indicator of a riparian area adjacent to a particular land use
(Monte-Carlo test; * P < 0.05).
Species Name
Acer negundo
Acer rubrum
Acer saccharinum
Acer saccharum
Aesculus hippocastanum
Carpinus caroliniana
Carya cordiformis
Carya ovata
108
Catalpa speciosa
Cercis canadensis
6
Crataegus spp.
Fagus grandifolia
Fraxinus americana
Fraxinus pennsylvanica
Gleditsia triacanthos
Juglans nigra
Liquidambar styraciflua
Liriodendron tulipifera
Malus spp.
Maclura pomifera
Morus alba
Morus rubra
Row crop
14.0 (31.30)
2.8 (6.32)
4.3 (9.71)
1.1 (2.49)
1.5 (3.30)
-
Lawn
2.1 (4.16)
2.5 (5.06)
14.8 (25.50)
1.1 (2.23)
3.9 (7.71)
5.1 (10.18)
11.8 (13.58)
16.7 (16.48)
1.2 (2.31)
1.7 (3.42)
-
Grassed
pasture
2.2 (5.90)
6.2 (16.26)
1.1 (2.77)
10.6 (18.04)
3.5 (9.34)
2.4 (6.30)
5.5 (14.47)
1.5 (4.07)
-
Wooded
pasture
5.4 (7.64)
3.3 (4.60)
2.6 (3.62)
5.4 (7.65)
19.5 (27.60)
25.3 (35.70)
9.7 (1.18)
20.8 (22.20)
-
Wooded
10.1 (15.19)
22.7 (45.40)
1.2 (2.33)
5.0 (10.06)
25.8 (26.80)
1.2 (2.41)
1.5 (2.99)
1.3 (2.56)
Row crop/
lawn
18.8 (26.50)
6.2 (8.78)
3.0 (4.23)
29.6 (41.90)
2.9 (4.10)
-
Row crop/
grassed pasture
14.0 (31.3)
9.4 (21.01)
3.0 (6.62)
-
Lawn/
wooded
2.5 (3.54)
12.6 (9.79)
8.9 (4.75) *
4.1 (5.76)
6.1 (8.58)
18.7 (26.5)
6.9 (9.69)
5.6 (7.89)
7.1 (2.88) *
Table 4.4. Continued
108
Table 4.4. Continued
109
Nyssa sylvatica
Ostrya virginiana
Pinus strobus
Platanus occidentalis
Populus deltoides
Prunus serotina
Prunus spp.
Pyrus calleryana
Quercus bicolor
Quercus muehlenbergii
Quercus palustris
Quercus rubra
Robinia pseudoacacia
Sassafras albidum
Salix spp.
Salix nigra
Salix ×pendulina
Tilia americana
Ulmus americana
Ulmus rubra
10.5 (23.6)
1.7 (3.79)
2.0 (4.47)
-
1.2 (2.35)
35.2 (23.6)
2.6 (5.27)
3.4 (6.75)
3.1 (6.15)
7.9 (9.97)
1.4 (2.75)
-
17.7 (28.3)
7.9 (13.75)
-
4.9 (6.93)
3.09 (4.37)
2.6 (3.65)
6.1 (8.67)
3.1 (4.34)
-
109
2.9 (5.85)
2.3 (4.67)
39.6 (28.10)
2.9 (5.69)
6.0 (11.92)
-
31.6 (17.10)
2.8 (3.93)
4.2 (6.00)
2.8 (3.97)
3.1 (4.34)
-
3.0 (6.65)
-
22.3 (31.60)
13.8 (2.34)
2.7 (3.79)
2.9 (4.11)
9.2 (12.95)
Table 4.5. Relative abundance (%) (+ 1 standard deviation) of understory (stems <10 cm diameter at breast height; > 1-m in height)
species, understory species richness and mean (+ 1 standard deviation) number of saplings per hectare for riparian areas adjacent to
various land uses for the Upper, North Fork, and South Fork subwatersheds, located in the Sugar Creek watershed in northeastern
Ohio. An asterisk indicates a significant difference among riparian areas adjacent to various land uses (Monte-Carlo test; * P < 0.05).
Adjacent Land Use
110
3
7
Species
Acer rubrum
Acer saccharum
Carpinus caroliniana
Carya ovata
Crataegus spp.
Fagus grandifolia
Fraxinus pennsylvanica
Gleditsia triacanthos
Juglans nigra
Lonicera morrowii
Malus spp.
Picea pungens
Prunus serotina
Quercus rubra
Salix spp.
Tilia americana
Ulmus americana
Viburnum opulus
Species Richness
Saplings/hectare
Row crop
Lawn
Grassed
pasture
5.6 (7.86)
50.0 (70.70)
44.4 (62.90)
3
419.4 (13.4)
33.3 (57.70)
36.40 (55.30)
3
516.1 (14.95)
5.0 (7.07)
45.0 (63.60)
50.0 (70.70)
3
354.8 (11.3)
Wooded
pasture
Wooded
Row crop/
lawn
Row crop/
grassed pasture
Lawn/ wooded
50.0 (70.70)
50.0 (70.70)
2
96.8 (11.82)
6.3 (12.50)
20.8 (25.00)
7.1 (14.29)
14.3 (28.60)
31.3 (47.30)
20.2 (31.70)
6
774.2 (16.3)
50.0 (70.70)
50.0 (70.70)
2
64.5 (9.93) *
70.0 (31.30)
30.0 (13.42)
2
322.6 (12.29)
3.9 (5.44)
42.3 (59.8)
10.0 (14.10)
3.9 (5.44)
20.0 (28.30)
20.0 (28.30)
6
580.7 (16.31)
110
Table 4.6. Results of canonical correspondence analyses (CCA) for relating overstory
importance values to environmental characteristics in the Upper, North Fork, and South
Fork subwatersheds, located in the Sugar Creek watershed in northeastern Ohio.
Eigenvalue
Cumulative Variance Explained (%)
Correlation Coefficients
Row crop
Lawn
Grassed pasture
Wooded pasture
Wooded
Row crop/lawn
Row crop/grassed pasture
Lawn/wooded
Alluvium
Glacial till
Silt loam
Fine loamy to silt loam
Sandy to silt loam
Sandy loam
Entrenchment Ratio
Width-to-Depth Ratio
Slope (%)
CCA1
0.664
9.6
CCA2
0.611
18.4
-0.0117
-0.3673
-0.1796
0.4190
-0.0696
0.0355
0.5899
-0.0955
-0.0876
0.0876
-0.3032
0.2552
-0.2388
0.4552
0.1934
-0.3063
0.3648
0.0706
-0.3097
-0.1834
-0.0678
-0.0591
0.0093
-0.1290
0.8041
0.3974
-0.3974
0.2770
0.0022
-0.2912
-0.1618
0.2644
-0.3545
-0.0248
111
100
Canopy Openness (%)
80
60
40
20
n/w
oo
de
d
se
dp
ras
row
cro
p/g
law
as
tur
e
aw
n
cro
p/l
row
wo
od
ed
as
tur
e
wo
od
ed
p
as
tur
e
law
n
g ra
sse
dp
row
cro
p
0
Riparian Type
Figure 4.1. Mean canopy openness (%) (+ 1 standard deviation) for riparian areas
adjacent to various land uses for overstory riparian vegetation in the Upper, North Fork,
and South Fork subwatersheds in the Sugar Creek watershed in northeastern Ohio.
112
40
Overtopped
Intermediate
Codominant
Dominant
Number of Trees
30
20
10
wo
od
ed
re
ro
w
cr
op
/
gr
la
wn
/
pa
stu
as
se
d
cr
op
/l a
wn
ed
ro
w
wo
od
re
stu
pa
d
wo
od
e
as
se
d
pa
stu
re
wn
la
gr
ro
w
cr
op
0
Riparian Type
Figure 4.2. Count of trees in each crown class category in the overstory vegetation layer
in riparian areas for riparian areas adjacent to various land uses in the Upper, North Fork,
and South Fork subwatersheds in the Sugar Creek watershed in northeastern Ohio.
113
80
Age (years)
60
40
20
wn
op
/l a
cr
w
re
pa
st
u
wo
od
ed
ro
as
se
d
wo
od
ed
pa
st
u
re
wn
la
gr
ro
w
cr
o
p
0
Riparian Type
Figure 4.3. Average age (+ 1 standard deviation) of cored overstory riparian trees for
riparian areas adjacent to various land uses in the Upper, North Fork, and South Fork
subwatersheds, located in the Sugar Creek watershed in northeastern Ohio.
114
1.0
SAAL5
GLTR PLOC
lawn/woo
MORU2
LIST2
ULRU
ACSA2
alluvium
QUMU
silt loa
-1.0
Entrench
JUNI
QUBI MOAL
ACRU
TIAM QUPA2 FRPE
CACA18
crop
SANI
OSVI
crop/lawfine loa
Slope
PRSE2
ULAM
w past CRATA
ACNE2woodQURU
MAPO
CAOV2crop/g p
NYSY
SALIX
sandy lo CASP8
ROPS
g pastPYCA80
FAGR PODE3
LITU
ACSA3
TSCA
PIST
AEHI sandy/si PRUNUCACO15
FRAM2 CECA4lawn
W/D Rati
SAPE12
glacial
MALUS
-1.0
1.0
Figure 4.4. Canonical correspondence analysis (CCA) biplot relating mean riparian
overstory species importance values to environmental characteristics in the Upper, North
Fork, and South Fork subwatersheds, located in the Sugar Creek watershed in
northeastern Ohio. Red lines indicate environmental variables: row crop (crop), lawn
(lawn), grassed pasture (g past), wooded pasture (w past), wooded (wood), row crop/lawn
(crop/law), row crop/grassed pasture (crop/ g p), lawn/wooded (lawn/woo), alluvium
(alluvium), glacial deposition (glacial), silt loam (silt loa), fine loamy to silt loam (fine
loa), sandy to silt loam (sandy/si), sandy loam (sandy lo), entrenchment ratio (Entrench),
width-to-depth ratio (W/D Rati), and percent slope (Slope). Triangles indicate tree-size
species. Overstory species presented in Appendix B.
115
1.0
23
lawn/woo
22
alluvium
silt loa
Entrench
14
8
2
19
12
crop
1
crop/law
7
fine Slope
loa
wood 17
11
5
3
18
16g past
4
lawn sandy/si
W/D Rati
w past
crop/g
p
21
sandy
lo 20
13
glacial
-1.0
10
6
9
15
-1.0
1.0
Figure 4.5. Canonical correspondence analysis (CCA) biplot relating sampling locations
to environmental characteristics in the Upper, North Fork, and South Fork subwatersheds,
located in the Sugar Creek watershed in northeastern Ohio. Circles refer to sampling
locations: 1 is S5A, 2 is S1D, 3 is U8C, 4 is U8B, 5 is U8A, 6 is U20B, 7 is S2A, 8 is
S1B, 9 is S1A, 10 is N3, 11 is N10, 12 is S4A, 13 is N11, 14 is U24D, 15 is U24C, 16 is
U20D, 17 is U20A, 18 is U8D, 19 is N1, 20 is S3A, 21 is S2B, 22 is S6B, and 23 is S6A.
Red lines indicate environmental variables: row crop (crop), lawn (lawn), grassed pasture
(g past), wooded pasture (w past), wooded (wood), row crop/lawn (crop/law), row
crop/grassed pasture (crop/ g p), lawn/wooded (lawn/woo), alluvium (alluvium), glacial
deposition (glacial), silt loam (silt loa), fine loamy to silt loam (fine loa), sandy to silt
loam (sandy/si), sandy loam (sandy lo), entrenchment ratio (Entrench), width-to-depth
ratio (W/D Rati), and percent slope (Slope).
116
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123
Appendix A: Subwatershed, sampling location name, adjacent land use, side of road sampled, and location of each sampling location
in the Upper, North Fork, and South Fork subwatersheds in the Sugar Creek watershed in northeastern Ohio.
Subwatershed
124
U
U
U
U
U
U
U
U
U
U
U
U
N
N
N
N
N
N
N
S
S
S
S
S
S
S
S
Sampling
Location
U8A
U8B
U8C
U8D
U20A
U20B
U20C
U20D
U24A
U24B
U24C
U24D
N1
N3
N4
N5
N6
N10
N11
S4A
S4B
S5A
S1D
S1C
S3A
S2B
S2A
Land use
Side of Road Sampled
Location
Lawn
Lawn
Lawn
Row crop
Wooded
Lawn
Row crop
Wooded
Row crop
Row crop
Wooded
Wooded
Lawn
Grassed pasture
Grassed pasture
Grassed pasture
Grassed pasture
Grassed pasture
Wooded pasture
Wooded pasture
Grassed pasture
Row crop
Row crop
Grassed pasture
Row crop
Grassed pasture
Grassed pasture
D
U
D
D
D
U
D
D
D
U
U
D
U
U
D
D
U
D
D
D
D
D
U
U
U
U
D
Smithville, near gas station
Smithville, upstream from U8A
679 W Main St, Smithville
777 S Summit St, Smithville
Smithville, downstream from U20B
Smithville, next to credit union
Beiler Rd, W of Eby Rd
off Hutton Rd, W of Honeytown
off Applecreek Rd, near Smucker Rd
near corner of Applecreek and Smucker
off Five Points Rd
off Five Points Rd, D stream of U24C
SR241, S of W Lebanon Rd
S on SR94, below N6
N off Western Rd, E of SR94
Off Jericho Rd, from SR94
S of Western Rd on SR94
Zuercher Rd
Emerson Rd, E of Zuercher Rd
xT164 btwn SR557 & T162
xSR557 btwn T181 & T166
xSR557 btwn T181 & T166
xSR643 to SR557, W of T166
xCR114 btwn SR643 & T182
xT181, adj. & near to CR114
xT184 btwn T182 & T183
in woods near T188; from CR600
Appendix A Continued
124
Appendix A Continued
S
S
S
S
S1A
S1B
S6B
S6A
Grassed pasture
Grassed pasture
Lawn
lawn
D
D
D
D
125
125
xT185 btwn CR600 & T183
xT183 btwn T177 & T185
in park in Baltic next to playground
adj. to RR tracks, S of Baltic off SR93
Appendix B: Species code, scientific name, and common name* for overstory species in
the Sugar Creek watershed in northeastern Ohio.
Species Code
Scientific Name
Common Name
ACNE2
ACRU
Acer negundo L.
Acer rubrum L.
boxelder
red maple
ACSA2
ACSA3
AEHI
CACA18
CACO15
Acer saccharinum L.
Acer saccharum Marsh.
Aesculus hippocastanum L.
Carpinus caroliniana Walter
Carya cordiformis (Wangenh.) K. Koch
silver maple
sugar maple
horse chestnut
American hornbeam
bitternut hickory
CAOV2
CASP8
CECA4
Carya ovata (Mill.) K. Koch
Catalpa speciosa (Warder) Warder exEngelm.
Cercis canadensis L.
shagbark hickory
northern catalpa
eastern redbud
CRATA
FAGR
FRAM2
FRPE
GLTR
JUNI
LIST2
LITU
MALUS
MAPO
Crataegus spp. L.
Fagus grandifolia Ehrh.
Fraxinus americana L.
Fraxinus pennsylvanica Marsh.
Gleditsia triacanthos L.
Juglans nigra L.
Liquidambar styraciflua L.
Liriodendron tulipifera L.
Malus spp. Mill.
Maclura pomifera (Raf.) C.K. Schneid.
hawthorn
American beech
white ash
green ash
honeylocust
black walnut
sweet gum
tuliptree
apple
osage orange
MOAL
Morus alba L.
white mulberry
MORU2
Morus rubra L.
red mulberry
NYSY
Nyssa sylvatica Marsh.
blackgum
OSVI
Ostrya virginiana (Mill.) K. Koch
hophornbeam
PIST
Pinus strobus L.
eastern white pine
PLOC
Platanus occidentalis L.
American sycamore
PODE3
Populus deltoides Bartram ex Marsh.
eastern cottonwood
PRSE2
Prunus serotina Ehrh.
black cherry
PRUNU
Prunus spp L.
cherry
*Nomenclature for each species follows the United States Department of Agriculture, Natural Resources
Conservation Service PLANTS Database (United States Department of Agriculture, Natural Resources
Conservation Service 2009).
Appendix B Continued
126
Appendix B Continued
PYCA80
QUBI
Pyrus calleryana Decne.
Quercus bicolor Willd.
Callery pear
swamp white oak
QUMU
QUPA2
QURU
ROPS
SAAL5
SALIX
SANI
SAPE12
TIAM
TSCA
ULAM
Quercus muehlenbergii Engelm.
Quercus palustris Münchh.
Quercus rubra L.
Robinia pseudoacacia L.
Sassafras albidum (Nutt.) Nees
Salix spp L.
Salix nigra Marsh.
Salix ×pendulina Wender.
Tilia americana L.
Tsuga canadensis (L.) Carrière
Ulmus americana L.
chinkapin oak
pin oak
northern red oak
black locust
sassafras
willow
black willow
Wisconsin weeping willow
American basswood
eastern hemlock
American elm
ULRU
Ulmus rubra Muhl.
slippery elm
127