Structure and Function of Vascular Plant Communities in Created and Restored Wetlands
in Ohio
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Kay Christine Stefanik, B.S.
Environmental Science Graduate Program
The Ohio State University
2012
Dissertation Committee:
William J. Mitsch, Advisor
Gil Borher
Charles Goebel
Allison Snow
Copyright by
Kay Christine Stefanik
2012
Abstract
Throughout Ohio there are a number of wetlands created or restored to fulfill
requirements under Section 404 of the Clean Water Act. The purpose of these
created/restored wetlands is to replace both habitat area and ecosystem processes of
natural wetlands. The created/restored wetlands are usually monitored for vegetation
structure, but rarely for ecosystem function. The purpose of this study was to examine
vegetation development in created and restored wetlands from both a structural and
functional standpoint.
Structural and functional characteristics of the dominant vegetation of mitigation
bank wetlands (<20 years of age) were examined from 2008-2010 for five wetland
mitigation bank sites and compared to a natural and a created reference wetland sites.
Wetlands were sampled for vegetation structure (species richness, Ohio floristic quality
assessment index, Shannon-Wiener diversity index, and community diversity index) and
function (aboveground net primary productivity and functional group composition of the
dominant species). The mitigation wetlands were significantly different from the
reference wetlands in terms of both structure and function (p<0.001), but the younger
wetland mitigation sites were more similar structural (in terms of species richness and
floristic quality) to the reference sites than the older mitigation sites.
ii
Development of vegetative structure and function was examined 15 to 17 years
after wetland creation in a planted wetland and an unplanted wetland at the Olentangy
River Wetland Research Park (ORWRP) on a monthly basis throughout the growing
season from 2008 through 2010. Structural characteristics, as well as functional
characteristics (aboveground net primary productivity, belowground net primary
productivity, macrophyte nutrient concentration) were examined. The planted wetland
had higher species richness (p=0.019), floristic quality assessment scores (p=0.002), and
less area occupied by invasives (p<0.001) than the unplanted wetlands, while the
unplanted wetland had higher aboveground net primary productivity than the planted
wetland (p=0.006) over the course of three years. Planting may be beneficial even in a
flow-through wetland that receives propagules from an adjacent river by reducing
potential niches for invasive species, increasing species richness and increasing floristic
quality.
Open system flow-through chambers were used to measure gaseous carbon fluxes
of gross primary productivity, respiration, and methane emissions associated with
dominant macrophyte communities at the ORWRP over the growing season. Gas
samples from the inflow and outflow pipes of the chambers were collected over 48-hr
periods every month from April through September on odd hours between sunrise and
sunset and twice nightly to estimate gross primary productivity (GPP) and respiration
(R). Methane emissions were also examined from both the day and night samples. GPP
averaged 13.94 ± 0.79 g CO2-C m-2 day-1, while respiration averaged 12.55 ± 0.54 g CO2C m-2 day-1. GPP differed by both month sampled and plant community sampled
iii
(p<0.001 and p=0.002, respectively). Median methane emissions from the sample plots
was 12.81 mg CH4-C m-2 day-1 and differed by month (p<0.001) and soil temperature
(p=0.049). Combining all three carbon fluxes, there is an overall uptake of carbon within
the sample plots, suggesting that the wetlands are acting as overall carbon sinks. There
was a net retention of carbon in the two experimental wetlands ranging from 160 to 195
g C/m2 year in 2010 and from 164 to 171 g C/m2 year in 2011.
iv
Dedication
To my family and friends.
v
Acknowledgements
I would like to thank all of the people who helped with field and lab work for the
three projects, Blanca Bernal, Chris Cooley, Deblina Gosh, Rachelle Howe, Felice Forby,
Kyle Kingma, Brent Macolley, Darryl Marois, Lynn McCready, Maggie Mohr, Christine
Shannon, the Stefanik family, Maoqi Sun, Matthew Thibault, Jorge Villa-Betancur, Evan
Waletzko, Li Zhang, and Yiding Zhang, as well as the Ohio Wetlands Foundation, Lorain
County Metroparks, Hebron State Fish Hatchery, Slate Run Metropark and Davey
Resource Group (Karen Wise and Ana Burns) for providing background information and
site access. A special thanks to Brent Macolley and Matthew Thibault for their help with
metabolism chamber construction and to Brent Macolley, Maggie Mohr, and Evan
Waletzko for helping with metabolism chamber set up over the two years of the project.
I would also like to thank the Ohio Wetlands foundation for providing support for
the project (Ohio Wetlands Foundation Fellowship), as well as the U.S. Environmental
Protection Agency (Agreements EM83329801-0 Cincinnati OH; MX95413108-0 Gulf of
Mexico Program), National Science Foundation (grants CBET-1033451 and CBET0829026), the Environmental Science Graduate Program (ESGP Fay Fellowship), and the
Olentangy River Wetland Research Park (Sipp Award) at The Ohio State University.
Finally, thank you to my advisor and committee members for all of their
comments, suggestions, and insights throughout the course of my research.
vi
Vita
May 2003.........................................................Midview High School
May 2007.........................................................B.S. Biology, Baldwin-Wallace College
2007 to present.................................................Graduate Teaching and Research Associate,
The Olentangy River Wetland Research
Park, Environmental Science Graduate
Program, The Ohio State University
Publications
Stefanik, Kay C., and William J. Mitsch. 2012. Structural and functional vegetation
development in created and restored wetland mitigation banks of different ages.
Ecological Engineering, 39: 104-112.
Mitsch, William J., Li Zhang, Kay C. Stefanik, Blanca Bernal, Amanda M. Nahlik,
Christopher J. Anderson, and Maria E. Hernandez. 2012. Creating wetlands: A 15year experiment in self-design. BioScience. 62: 237-250.
Fields of Study
Major Field: Environment Science
vii
Table of Contents
Abstract................................................................................................................................ii
Dedication............................................................................................................................v
Acknowledgments..............................................................................................................vi
Vita.....................................................................................................................................vii
List of Table.......................................................................................................................xii
List of Figures...................................................................................................................xiii
Chapters 1: Introduction......................................................................................................1
Chapter 2: Structural and functional vegetation development in created and restored
wetland mitigation banks of different ages........................................................................11
2.1 Abstract............................................................................................................11
2.2 Introduction......................................................................................................12
2.3 Methods............................................................................................................16
2.3.1 Study Site................................................................................................16
2.3.2 Sampling Methods..................................................................................17
2.3.3 Structural Parameters..............................................................................18
2.3.4 Functional Parameters.............................................................................20
2.3.5 Statistical Methods..................................................................................21
2.4 Results..............................................................................................................23
viii
2.4.1 Structural and Functional Parameters.....................................................23
2.4.2 Hypothesis Testing..................................................................................24
2.4.3 Redundancy Analysis..............................................................................26
2.5 Discussion........................................................................................................27
2.5.1 Structure..................................................................................................27
2.5.2 Function..................................................................................................29
2.5.3 Mitigation Bank and Reference Wetland Comparison............................30
2.6 Conclusions......................................................................................................32
2.7 References........................................................................................................33
Chapter 3: Vegetation productivity of riverine planted and unplanted wetlands fifteen to
seventeen years after creation............................................................................................48
3.1 Summary...........................................................................................................48
3.2 Introduction.......................................................................................................49
3.3 Materials and Methods......................................................................................52
3.3.1 Study Site................................................................................................52
3.3.2 Structural Characteristics........................................................................53
3.3.3 Functional Characteristics.......................................................................54
3.3.4 Statistical Analysis..................................................................................58
3.4 Results..............................................................................................................59
3.4.1 Experimental Wetlands..........................................................................59
3.4.2 Edge Vegetation.....................................................................................61
3.5 Discussion........................................................................................................62
ix
3.5.1 Structure.................................................................................................62
3.5.2 Function.................................................................................................64
3.5.3 Edge Vegetation.....................................................................................66
3.5.4 Conclusions............................................................................................66
3.6 References........................................................................................................67
Chapter 4: Metabolism and methane flux of dominant macrophyte communities in
created riverine wetlands using open system flow through chambers...............................83
4.1 Abstract............................................................................................................83
4.2 Introduction......................................................................................................84
4.3 Methods............................................................................................................88
4.3.1 Study Site................................................................................................88
4.3.2 Chamber Design......................................................................................89
4.3.3 Sampling.................................................................................................90
4.3.4 Statistical Analysis..................................................................................91
4.4 Results..............................................................................................................92
4.5 Discussion........................................................................................................96
4.5.1 Gross Primary Productivity and Respiration..........................................96
4.5.2 Carbon Balance and Global Climate Change.........................................98
4.6 References......................................................................................................101
Chapter 5: Conclusions....................................................................................................114
References........................................................................................................................117
Appendix A: In-depth Site Descriptions..........................................................................133
x
Appendix B: Regression Equations.................................................................................138
Appendix C: Mitigation Bank and Reference Wetland Vegetation.................................139
Appendix D: Olentangy River Wetland Research Park Experimental Wetlands
Vegetation Community Maps from 2008-2011...............................................................173
Appendix E: Calculations for Metabolism Chambers.....................................................178
Appendix F: Statistical Tables.........................................................................................179
F.1 Chapter 2 Statistical Tables............................................................................180
F.1.1 Mitigation vs. Reference Sites..............................................................180
F.1.2 Age of Mitigation Bank Wetlands.........................................................182
F.1.3 Hydrology..............................................................................................183
F.1.4 Location in Ohio...................................................................................184
F.2 Chapter 3 Statistical Tables............................................................................185
F.2.1 Wetland Vegetation...............................................................................185
F.2.2 Nutrient Analysis...................................................................................187
F.2.3 Edge Vegetation.....................................................................................189
F.3 Chapter 4 Statistical Tables............................................................................190
F.3.1 Gross Primary Productivity...................................................................190
F.3.2 Respiration............................................................................................193
F.3.3 Methane.................................................................................................195
xi
List of Tables
Table 2.1: Year of creation, site and wetland areas, hydrology, hydrogeomorphic
classification, soil type, and location of Ohio wetland mitigation banks and reference
wetlands used in this study.................................................................................................41
Table 2.2: Average ± standard error of structural and functional parameters at each
wetland site........................................................................................................................42
Table 2.3: Functional groups of dominant wetland plant species at the five mitigation
sites and two reference sites...............................................................................................43
Table 3.1: Average ± standard error of structural and functional characteristics of the two
experimental wetlands and edge area................................................................................75
Table 3.2: Functional classification and percent cover of the dominant plant communities
in the planted and unplanted wetlands...............................................................................76
Table 3.3: Average nutrient content of aboveground biomass from August 2010............77
Table 4.1: Monthly average ± standard error of environment variables over the course of
the growing season in 2010 and 2011..............................................................................107
Table 4.2: P-values from ANOVA and linear regression models for the dependent and
environmental variables...................................................................................................108
Table B.1: Regression equations created for dominant species.......................................138
Table C.1: Plant species found at each site over the course of the study. Includes
coefficient of conservatism and wetland indicator status for each species......................140
xii
List of Figures
Figure 2.1: Location of wetland sites within Ohio, USA..................................................44
Figure 2.2: Scatter plots of (A) aboveground net primary productivity of emergent plant
zones (ANPP), (B) species richness, (C) ANPP over the entire wetland, and (D) floristic
quality assessment index (FQAI) score with age for 5 mitigation bank wetlands.............45
Figure 2.3: Redundancy analysis (RDA) triplot of species, environmental variables, and
mitigation bank sites..........................................................................................................46
Figure 2.4: Diversity-productivity scatter plot divided into four categories for mitigating
wetland banks and reference wetlands...............................................................................47
Figure 3.1: This study was conducted in the two 1-ha experimental wetlands at the
Olentangy River Wetland Research Park...........................................................................78
Figure 3.2: Monthly weighted aboveground biomass of the two wetlands.......................79
Figure 3.3: Accumulation of monthly weighted aboveground net primary productivity of
the wetlands from 2008 to 2010........................................................................................80
Figure 3.4: Monthly belowground biomass of the two wetlands......................................81
Figure 3.5: Tree aboveground net primary productivity (ANPP) of the interior edge of
each wetland.......................................................................................................................82
Figure 4.1: Diagram of large chamber setup...................................................................109
Figure 4.2: Average gross primary productivity of the five different samples groups....110
Figure 4.3: Monthly average and standard error of carbon flux of the sample plots.......111
xiii
Figure 4.4: Monthly median methane emission for the sample plots..............................112
Figure 4.5: Carbon uptake and emission as CO2 in relation to solar radiation and
temperature.......................................................................................................................113
Figure D.1 2008 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park................................................174
Figure D.2 2009 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park................................................175
Figure D.3 2010 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park................................................176
Figure D.4 2011 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park................................................177
xiv
Chapter 1: Introduction
Wetland creation and restoration as a means to mitigate the loss of natural
wetlands due to human development has become common practice in the United States
since the passage of the Clean Water Act in 1972. Under Section 404 of the Clean Water
Act, a permit is required to dredge or fill any jurisdictional wetland (legally recognized
wetland as determined by the Wetland Delineation Manual published by the Army Corps
of Engineers; Mitsch and Gosselink 2007; Salzman and Thompson 2007). Under the
permit program, the permit holder is required to mitigate wetland loss at a ratio of greater
than 1:1, which helps ensure no-net loss of wetland area. The main reason behind the nonet loss policy is the large amount of wetland habitat that has been lost since preEuropean settlement. Through the 1970s, the United States had lost approximately 50%
of wetland habitat since European settlement in the lower forty-eight states, while Ohio
had lost around 90% (Dahl 1990). Unfortunately, there is a lack of human power,
resources, and enforcement power by the Army Corp of Engineers, The U.S.
Environmental Protection Agency, and various state agencies to ensure that permit
requirements are met (Cole and Shafer 2002; Reiss el al. 2009). Because of this, not
only are there problems with no-net loss goals not being achieved, but there is also an
issue with created and restored wetlands not replacing the ecosystem function of natural
wetlands that have been lost (NRC 2001).
1
Under a Section 404 permit, there are a few different options for mitigation. Two
of the more common options for mitigation include creation or restoration on a local
permit basis or creation or restoration on a larger scale through mitigation banks.
Mitigation banks are large wetland complexes that are created without regard to one
specific permit. Mitigation banks are monitored for approximately five years after
creation or restoration. Wetland credits, based on the overall area of successful wetland
habitat within the bank, are then sold to permit holders to fulfill their permit requirements
(Reiss et al. 2009). Since mitigation banks are created without regard to one specific
permit, there is no guarantee that in-kind replacement, or the replacement of the same
type of wetland habitat will occur. While the same type of wetland function may not be
replaced, mitigation banks are still beneficial, especially in a system where there is little
permit enforcement and there is a failure rate of 40-60% in some states (NRC 2001; Cole
and Shafer 2002; Reiss et al. 2009).
There has also been concern over the ability of mitigation wetlands to meet the
criteria for success within a 5-year time frame (Mitsch and Wilson 1996). Studies have
found that it may take up to 20 years for vegetation characteristics to resemble that of
natural wetlands (Balcombe et al. 2005; Spieles 2005; Gutrich et al. 2009). Construction
techniques have also been linked to problems with wetland development. The removal or
compaction of soil from a site has been shown to alter organic matter content and bulk
density, which can inhibit the penetration of roots within the soil and reduce nutrient
availability to the vegetation, thus hindering the establishment of plant species and
primary productivity (Campbell et al. 2002; Bruland and Richardson 2005; Batilan-Smith
2
et al. 2009; Ahn and Dee 2011). It may take a number of years for a created wetland to
develop soils similar to natural wetlands in terms of soil organic matter, nutrient content
and seed bank (Choi 2004).
Even if a wetland meets the criteria for success after 5 years, a wetland could be
deemed unsuccessful in the future depending on the trajectory of succession in the
wetland. At the five year point, it is quite possible that the trajectory of vegetation
succession may not be fully visible (Matthews et al. 2009). Studies have shown that
there tends to be an initial peak in structural parameters of a system, which then begin to
decline over time (Fennessey and Roehrs 1997; Campbell et al. 2002; Balcombe et al.
2005; Spieles 2006; Gutrich et al. 2009). Future disturbances to a system will also likely
alter the successional trajectories of a wetland.
One way to accelerate the succession of a created or restored wetland may be
through vegetation planting. Gutrich et al. (2009) found that wetlands in which there was
a strong initial restoration effort (planting of wetland vegetation and contouring within
basins) had higher floristic composition for plant species richness, number of native
species, and number of hydrophytes. Not only can planting positively influence
vegetation structural characteristics, but it may also be able to help increase the resiliency
of a system to future disturbances. Newly created wetlands can be thought of as highly
disturbed systems that will progress along a successional trajectory that will lead to a
system that falls along the continuum from low quality to high quality ecosystems and
from early to late successional types of vegetation (Suding and Cross 2006). The
frequency and severity of disturbances to an ecosystem will also affect the length of time
3
spent in a particular successional stage, how often the ecosystem reverts to early
successional stages, and the ability of an ecosystem to progress into later successional
stages (Walker and del Moral 2008). Disturbances that affect the dominant species of a
system more so than the minor species will likely have a greater impact on ecosystem
processes than disturbances that primarily affect minor species. If there is a functional
overlap between the minor and dominant species within an ecosystem, i.e. there are
minor species that can maintain the functional roles if dominant species are damaged or
destroyed, an ecosystem is much more likely to be resilient to disturbances (White and
Jentsch 2001). Mitsch and Wilson (1996) suggested that the best approach to vegetation
planting in created and restored wetlands may be to introduce as many native vegetation
species as possible and allow the system to self-design and chose the species that are
most appropriate for the system.
While mitigation banks may help to ensure “no net loss” of wetland habitat is
occurring, this does not guarantee that there is not a loss of wetland functional processes.
Current monitoring practices of mitigation wetlands tends to focus on the soil, water, and
vegetation of the system. Of these three parameters, vegetation is a widely used measure
for the success of a wetland. Vegetation monitoring practices focus on the structural
characteristics of the plants, such as percent vegetation cover, species richness, percent
invasive species, and indicator status of the species present (Mitsch and Wilson 1996).
Structural characteristics have been favored due to the speed and cost efficiency of the
practices, but may not reflect ecosystem processes (Mitsch and Wilson 1996; Spieles
2005; Ahn and Dee 2011). It has been suggested that functional vegetation
4
characteristics (e.g. productivity and functional group composition) are better
determinates of ecosystem processes than structural characteristics (Tilman et al. 1997).
Functional characteristics are the physical, chemical and, biological processes that occur
within a wetland (Marble 1992). Vegetation functional characteristics include net
primary productivity, gross primary productivity, respiration, and functional group
composition.
Not only can functional characteristics aid in determining the success of created
and restored wetlands, they can also aid in understanding the role that wetlands play in
regional, landscape, and global patterns. Gross primary productivity is the main driver of
the carbon cycle in wetlands. The gaseous carbon budget of a wetland is dependent on
the amount of carbon taken up through photosynthesis and the amount of carbon lost
through respiration and methane emissions (Whiting and Chanton 2001; Cornell et al.
2007; Yvon-Durocher et al. 2007). The difference between uptake and emissions
determines if the wetland is acting as a carbon sink or a carbon source. The fate of
carbon in wetland ecosystems is of great concern in regards to global climate change.
Carbon dioxide and methane are two of the main greenhouse gases identified by the
IPCC (2007). Of these two gases, methane has a global warming potential (GWP) of 25
relative to carbon dioxide, so a small change in levels of atmospheric methane will have a
greater impact on global climate change than small changes in the level of carbon dioxide
(IPCC 2007). Through understanding the balance of carbon in wetlands it is possible to
determine if a wetland is acting as a carbon sink or a carbon source, and may provide
information on the types of conditions necessary in created and restored wetlands to
5
constructed wetlands that function as carbon sinks.
The goal of this study was to examine vegetation development in created and
restored wetlands in Ohio from both a structural and functional standpoint. The specific
objectives of this study were to:
1) Examine the structural and functional characteristics of the dominant
vegetation of several mitigation bank wetlands <20 years of age and
compare these to a natural wetland and a successful created reference
wetland of similar age;
2) Compare development of vegetative structure and function in planted and
unplanted wetlands maintained with identical hydrology for 15 to 17 years
after the wetlands were created: and
3) Estimate gross primary productivity, respiration, methane emissions, and
net carbon retention of dominant macrophyte communities in the wetlands
using open system flow through chambers.
Each objective is explored in its own chapter in this dissertation. All research was
conducted in Ohio, USA, with chapter two examining mitigation sites in northern and
central Ohio, and chapters three and four examining structural and functional
characteristics in greater detail in the two experimental wetlands at the Olentangy River
Wetland Research Park, The Ohio State University, Columbus, Ohio. The majority of the
field research occurred during the growing seasons of April through September 20082011.
6
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Ahn, C., & Dee, S., 2011. Early development of plant community in a created mitigation
wetland as affected by introduced hydrologic design elements. Ecological
Engineering. 37, 1324–1333.
Balcombe, C.K., Anderson, J.T., Fortney, R.H., Rentch, J.S., Grafton, W.M., & Kordek,
W.S., 2005. A comparison of plant communities in mitigation and reference
wetlands in the Mid Appalachians. Wetlands 25, 130–142.
Bantilan-Smith, M., Bruland, G.L., MacKenzie, R.A., Henry, A.R., & Ryder, C.R., 2009.
A comparison of the vegetation and soils of natural, restored, and created coastal
lowland wetlands in Hawai’i. Wetlands 29, 1023–1035.
Bruland, G.L., & Richardson, C.J., 2005. Spatial variability of soil properties in created,
restored, and paired natural wetlands. Soil Science Society of America Journal 69,
273–284.
Campbell, D.A., Cole, C.A., &Brooks, R.P., 2002. A comparison of created and natural
wetlands in Pennsylvania, USA. Wetland Ecology and Management 10, 41–49.
Choi, Y.D., 2004. Theories for ecological restoration in changing environment: Toward a
'futuristic' restoration. Ecological Research 19, 75-81.
Cole, C.A., & Shafer, D., 2002. Section 404 wetland mitigation and permit success
criteria in Pennsylvania, USA, 1986–1999. Environmental Management 30, 508–
515.
Cornell, J.A., Craft, C.B., & Megonigal J.P., 2007. Ecosystem gas exchange across a
created salt marsh chronosequence. Wetlands 27, 240-250.
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Dahl, T.E., 1990. Wetlands Losses in the United States 1780s to 1980s. U.S. Department
of the Interior, Fish and Wildlife Service, Washington, DC, l3 pp.
Fennessey, S., & Roehrs, J., 1997. A Functional Assessment of Mitigation Wetlands
in Ohio: Comparisons With Natural Systems. Ohio Environmental Protection
Agency Division of Surface Waters, Columbus, OH.
Gutrich, J.J., Taylor, K.J., & Fennessy, M.S., 2009. Restoration of vegetation
communities of created depressional marshes in Ohio and Colorado (USA): the
importance of initial effort for mitigation success. Ecological Engineering 35,
351–368.
Intergovernmental Panel on Climate Change (IPCC) 2007. Contribution of Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change, Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt
KB, Tignor M, Miller HL (eds.) Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA.
Marble, A.D., 1992. A guide to wetland functional design. Pp 1-14, CRC Press, Boca
Raton, FL.
Matthews, J.W., Spyreas, G. & Endress, A.G., 2009. Trajectories of vegetation-based
indicators used to assess wetland restoration progress. Ecological Applications 19,
2093-2107.
Mitsch, W.J., & Wilson, R.F., 1996. Improving the success of wetland creation and
restoration with know-how, time, and self-design. Ecological Applications 6, 77–
83.
8
Mitsch, W.J., & Gosselink, J.G., 2007. Wetlands, 4th ed. John Wiley & Sons, Inc.,
Hoboken, NJ.
National Research Council, 2001. Compensating for Wetland Losses under the Clean
Water Act, Committee on Mitigating Wetland Losses. National Academies Press,
Washington, DC.
Reiss, K.C., Hernandez, E., & Brown, M.T., 2009. Evaluation of permit success in
wetland mitigation banking: a Florida case study. Wetlands 29, 917–918.
Salzman, J., & Thompson Jr., B.H., 2007. Environmental Law and Policy. Foundation
Press, New York, NY.
Spieles, D.J., 2005. Vegetation development in created, restored, and enhanced mitigation
wetland banks of the United States. Wetlands 25, 51–63.
Suding, K.N., & Cross, K.L., 2006. The dynamic nature of ecological systems: Multiple
states and restoration trajectories. Foundations of Restoration Ecology (eds D.A.
Falk, M.A. Palmer, & J.B. Zedler) pp. 190-209 Island Press, Washington, D.C.
Tilman, D., Knops, J., Wedin, D., Reich, P., Ritchie, & M., Siemann, E., 1997. The
influence of functional diversity and composition on ecosystem processes.
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Walker, L.R. & del Moral, R. 2008. Transition dynamics in succession: Implications for
rates, trajectories, and restoration. New Models for Ecosystem Dynamics and
Restoration (eds K. Suding & R.J. Hobbs), pp. 33-49 , Island Press, Washington,
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ecosystem dynamics. Progress in Botany, 62, 399-450
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emissions versus carbon sequestration. Tellus Series B-Chemical and Physical
Meteorology 53, 521-528.
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Warming increases the proportion of primary production emitted as methane from
freshwater mesocosms. Global Change Biology 17, 1225-1234.
10
Chapter 2: Structural and functional vegetation development in created and restored
wetland mitigation banks of different ages1
2.1 Abstract
Vegetation surveys of seven Ohio wetland sites were conducted from 2008 to
2010 during peak biomass (August). These seven sites included five created/restored
mitigation bank wetlands, a created riverine research wetland, and a natural reference
wetland. All of the created/restored wetlands ranged in age from 3 to 18 years. The
objective of this study was to examine the development of vegetation structure and
function of mitigation bank wetlands less then 20 years of age and to compare these to
reference wetlands. Vegetation structure examined included species richness, floristic
quality assessment index (FQAI), Shannon–Wiener diversity index (H), and community
diversity index (CDI). Functional attributes included aboveground net primary
productivity (ANPP) and functional group composition of dominant species. For both
structure and function, the reference wetlands were statistically different from the
wetland mitigation bank sites (P < 0.001, MANOVA). Structurally, there were significant
differences of FQAI score (P < 0.05) and species richness (P < 0.05) with age in the
mitigation sites. Functionally, there was a significant difference between ANPP (P < 0.05)
and age in the mitigation sites. Over the different types of wetlands, the reference
1 Stefanik, K.C., & Mitsch, W.J. 2012. Structural and functional vegetation development in created and
restored wetland mitigation banks of different ages. Ecological Engineering 39, 104-112.
11
wetlands had significantly different ANPP, FQAI scores, and species richness than did the
mitigation sites (P < 0.001, P < 0.05, and P < 0.001, respectively). CDI and H were not
statistically different between mitigation sites and the reference wetlands. ANPP, FQAI,
and species richness tended to be higher in the reference sites than in the mitigation sites.
Overall, the mitigation bank wetlands were not statistically similar to the reference sites.
Within the mitigation banks, the younger sites had higher values for structural attributes
than the older mitigation sites.
2.2 Introduction
Throughout much of the United States, there has been a significant loss of natural
wetlands since European settlement. Most of the Upper Midwestern states have lost
more than 80% of their wetlands, while Ohio has lost approximately 90% of its natural
wetlands since the 18th century (Dahl 1990). One way to combat further loss has been
through wetland mitigation. Under Section 404 of the U.S. Clean Water Act, a permit is
required to drain, damage, or destroy a jurisdictional wetland (legally recognized wetland
as determined by the Wetland Delineation Manual
published by the Army Corps of Engineers) (Mitsch and Gosselink 2007; Salzman and
Thompson 2007). The permit holder is then required to mitigate the loss of the wetlands
that were damaged or destroyed, usually at a ratio greater than 1:1, i.e. where more
wetland area is created or restored than was lost. The purpose of a greater than 1:1 ratio is
to avoid a net loss of wetland area and ecosystem processes. Mitigation can be done on
an individual basis or by an organization that specializes in wetland creation and
12
restoration. The second type of mitigation, often referred to as a wetland mitigation bank,
is when wetlands are created or restored without regard to a specific permit prior to
wetland destruction. Wetland area credits from mitigation banks are then sold to permit
holders to fulfill their permit requirements (Mitsch and Gosselink 2007).
Ideally, the design of a created or restored wetland should be modeled after the
natural wetland that was damaged or destroyed in order to replicate the wetland type and
ecosystem processes of the natural site. In many cases, particularly with mitigation bank
wetlands, information regarding the natural wetland that was damaged or destroyed
cannot be used in the planning of the mitigation wetland. Instead, reference wetlands
located near the mitigation site are used as a guide for mitigation construction and
monitoring (LePage 2011). The use of high quality reference wetlands can illustrate the
overall gain or loss of wetland function due to mitigation and can aid in better initial
construction of the mitigation wetlands (Brinson and Rheinhardt 1996).
Monitoring of mitigation wetlands usually lasts for the first five years after
creation/restoration, at which point the success of the wetland is determined (NRC 2001).
The success and quality of these created and restored wetlands are determined using
hydrologic, vegetation, and soil characteristics, with emphasis being placed on vegetation
(NRC 2001; Spieles et al. 2006). Vegetation parameters commonly used are structural in
nature and include such things as species richness, percent ground cover, and percent
native plant species. There is some debate as to whether or not current structural
parameters are adequate for determining success. It has been suggested that structural
parameters have been chosen for speed and cost efficiency and may not reflect ecological
13
processes (Mitsch and Wilson 1996; Spieles 2005; Ahn and Dee 2011). Tilman et al.
(1997) found that in grassland ecosystems, functional characteristics (e.g. productivity
and functional group composition) are better determinants of ecosystem processes (e.g.
photosynthesis, nutrient cycling) than structural characteristics (e.g. species richness,
percent cover).
Another issue of concern is the 5-year monitoring period. It has been suggested
that 15–20 years of maturation may be necessary before the true success of a wetland can
be assessed (Mitsch and Wilson 1996). Even more time may be needed if the desired
result is a forested wetland (Niswander and Mitsch 1995; NRC 2001). From a regulatory
standpoint, some studies have shown poor wetland structure and function in created and
restored mitigation wetlands within the desired time frame of 5 years. Success rate,
described as the ability of a site to meet regulatory permit requirements within the
allotted monitoring time, range from approximately 40 to 60% in some states (Cole and
Shafer 2002; Reiss et al. 2009). While regulatory success is not the equivalent of
ecological success (ability of a mitigation wetland to function as does a natural wetland;
see Mitsch and Wilson 1996), the inability of wetlands to meet permit requirements
suggests that mitigation wetlands may not adequately be capturing the ecological
processes that were lost during the destruction of natural wetlands or not all wetland
ecosystems are capable of achieving ecological success within a 5-year time frame.
Atkinson et al. (2005) found that mitigation wetlands, after 20 years, began to reach a
state of equilibrium. Additional studies have seen a similar trend, with mitigation wetland
vegetation beginning to resemble vegetation of natural wetlands within approximately 20
14
years (Balcombe et al. 2005; Spieles 2005; Gutrich et al. 2009).
Much of the mitigation banking within the U.S. relies on creation, restoration, and
conservation techniques. Wetland creation tends to have a fairly low rate of success
compared to restoration of a site, while conservation does not add any addition wetland
area or replace lost ecosystem functions (ELI 2002; Spieles 2005). Construction
techniques have been linked to problems with wetland development due to such things as
improper placement within the landscape and soil removal/compaction by earth moving
equipment (Mitsch and Wilson 1996; Campbell et al. 2002; Bruland and Richardson
2005; Bantilan-Smith et al. 2009; van der Valk et al. 2009; Ahn and Dee 2011). Wetland
development can be impaired if the wetland is constructed in an area where proper
hydrology cannot be obtained or if the wetland lacks connectivity to propagule sources
(Mitsch and Wilson 1996; van der Valk et al. 2009). Soil removal to form wetland basins,
as well as soil compaction, can alter the organic matter content and bulk density of the
soil. These changes in soil characteristics can inhibit the penetration of roots within the
soil and reduce nutrient availability, thus hindering the establishment of desired plant
species and primary productivity (Campbell et al. 2002; Bruland and Richardson 2005;
Bantilan-Smith et al. 2009; Ahn and Dee 2011). Delays in wetland development due to
construction techniques may account for the inability of some mitigation projects to
achieve both legal and ecological success within the allotted monitoring period.
The goal of this project was to examine vegetation of created/restored mitigation
bank wetlands from both a structural and functional standpoint and to compare these
mitigation bank wetlands to well-maintained research wetlands of a similar age and a
15
natural reference wetland. It was hypothesized that (1) structural and functional
parameters will differ between the natural/research reference wetlands and mitigation
bank wetlands; (2) older wetlands will have higher productivity, species richness, and
diversity than younger wetlands due to additional development time; (3) productivity in
wetlands will vary based on location within Ohio due to differences in climate and
ecoregion location; and (4) flow-through wetlands will have higher primary productivity
and species richness resulting from the continual input of propagules into the system via
surface water flow.
2.3 Methods
2.3.1 Study Site
Samples were collected at seven wetland sites throughout northern and central
Ohio. Three of the wetland sites are located in northern Ohio (Erie/Ontario drift lake
plain ecoregion), while the remaining four sites are located in central Ohio (Eastern
cornbelt plain ecoregion) (Fig. 2.1). The five mitigation bank sites used in this study are
Hebron, Sandy Ridge, Slate Run, Trumbull Creek Phase I, and Trumbull Creek Phase II.
The reference wetlands used included the created experimental wetlands at the Olentangy
River Wetland Research Park (ORWRP) and Calamus Swamp, a natural wetland in
southern Ohio owned by the Columbus Chapter of the Audubon Society (Appendix A).
Not only did we want to compare the mitigation sites to a natural reference wetland, we
also wanted to compare the mitigation sites to a successful created wetland of similar age.
The experimental wetlands at the ORWRP are located on the northern end of The Ohio
State University Columbus campus along the Olentangy River. These wetlands, created
16
in 1994, were shown more than a decade earlier (1998) to be comparable to natural
wetlands in Ohio based on FQAI scores (structure) and Ohio Wetland Assessment
Method ratings (function) (Elifritz and Fennessy 1999). The wetlands at the ORWRP
developed quickly due to their optimum hydroperiod, hydrologic connection to the
Olentangy River which acts as a propagule source for the wetlands, and continual
exchange of biotic and abiotic factors with the surrounding environment (Mitsch et al.
1998, 2005a,b, 2012; Mitsch and Gosselink 2007).
All of the created and restored sites contain multiple wetlands with emergent
marsh vegetation and are less than 20 years in age (Table 2.1). The natural reference
wetland is a marsh that is beginning a transition along the boarder into a shrub/scrub
wetland dominated by Cephalanthus occidentalis. Hydrologic conditions varied
from site to site and were put into one of three categories; continuously flooded, high
spring low fall, and pulsing (Table 2.1).
2.3.2 Sampling Methods
Sampling was performed at each of the sites in August of 2008, 2009 and 2010.
Across the seven sites, 250 vegetation plots were sampled in a total of 52 wetland basins.
The number of basins examined per site varied based on the overall size of the site, as
well as the size of the individual wetland basins present to ensure similarities in overall
area and number of sample plots examined at each site. A basin is defined as a depression
within the landscape that meets the criteria of a wetland (proper hydrology, wetland
vegetation, and hydric soils) and is hydrologically isolated except under major storm
events. A 0.25 m2 portable pvc sampling frame was placed in the vegetation plots every
17
40 paces (approximately 0.5 m per pace) along transects established in each plant
community. Plant communities were determined using visual observation and were based
on the dominant species present in the community. Data collected at the plots included
species present, stem density and average height for each species. These data were used
to determine aboveground net primary productivity (ANPP) via regression equations.
Data collected from the entire site included area of major plant communities and
maximum water depth. The collected data was used to calculate species richness, Ohio
floristic quality assessment index (FQAI), Shannon–Wiener diversity index (H'),
community diversity index (CDI), and functional
group composition of major plant communities. Additional information on site
characteristics was obtained from monitoring reports and previous studies.
Area of each major plant community, as well as total area of emergent vegetation
was determined by ground-truthing and GPS. A Thales MoblileMapper GPS unit was
used to find the area of each plant community and area of open water within each
wetland. Data collected was used to determine overall area of the wetlands sampled at
each site, to create vegetation maps, and to calculate CDI for each basin. Elevation maps
from the mitigation monitoring reports were used to find the approximate location of
lowest elevation each wetland. A weighted measuring tape was used to estimate the depth
of the water at this location during each sampling visit.
2.3.3 Structural Parameters
FQAI scores were calculated using the methods outlined in Andreas et al. (2004):
FQAI= ∑ (CofC i ) / √( N )
18
where C of Ci is the coefficient of conservatism for each species and N is the number of
native species. All species were assigned a coefficient of conservatism value between 0
and 10 which is “an ordinal weighting factor of the degree of conservatism (or fidelity)
displayed by that species in relation to all other species of the region” that was
determined by Andreas et al. (2004) to be used within the state of Ohio. Species with
lower coefficient of conservatism values are found in highly disturbed areas, whereas
higher values are indicative of species that have a relatively narrow ecological niche
(Lopez and Fennessy 2002; Ahn and Dee 2011). This system does not give additional
weight to endangered or rare species. Within wetland sites of Ohio, the FQAI scores for
emergent marshes tend to be around 20 or 21, with a range of approximately 11–34
(Lopez and Fennessy 2002; Andreas et al. 2004).
The Shannon–Wiener diversity index (H')was also used to examine species
richness and evenness between the different wetland sites:
H'=−∑ ( pi ln pi )
where H is the diversity index score, s is the number of species, and p is the relative
abundance of a particular species (McCune and Grace 2002). Unlike the FQAI, the H'
includes non-native and invasive species in the calculations and does not include a
system to give additional weight to particular species.
The community diversity index (CDI; Mitsch et al. 2005a,b) was calculated using
the equation:
CDI=− ∑ (C i ln Ci )
where N is the number of wetland communities and C is the relative area of each wetland
19
community. This CDI is a landscape index that estimated the richness and evenness of
spatial patterns of vegetation communities, using the area of communities rather than
stem counts as the dependent variables. A CDI value of 0.0 represents a monospecific
landscapes, whereas higher numbers indicate diverse patterns of several communities.
2.3.4 Functional Parameters
Emergent macrophyte above ground net primary productivity (ANPP) was
estimated from peak biomass measurements in August each year with non-destructive
sampling techniques for the five mitigation bank sites and the natural wetland site
(Thursby et al. 2002). Regression equations from other studies in Ohio (Johnson 1998)
and the United States (Muzika et al. 1987) were used to estimate biomass for most
macrophyte species. When equations were not available for a particular species, multiple
biomass samples were collected for that species and dried at 105 ◦ C for 48 h.
Relationship between biomass dry weight and stem height and stem density were then
used to create regression equations for those species (Appendix B). Plot primary
productivity data were then averaged to estimate ANPP of each wetland basin. ANPP was
weighted based on area of the plant communities within the emergent zone using the
equation:
WANPP= ∑ ( A i Bi )/ E
where WANPP is the weighted aboveground net primary productivity, A is the area of a
specific community, B is the average biomass for that specific community, and E is the
total area of all emergent plant communities within the wetland basin. In addition to
ANPP, ANPP of emergent vegetation across the entire wetland area was determined,
20
taking into account the amount of open water at each wetland. In rare instances when no
equation was available for a plant species, the plot that the species was present in was
omitted from calculations and statistics regarding net primary productivity. Data collected
from the omitted plots were still used for species richness, FQAI scores, and other
appropriate variables.
Destructive primary productivity sampling was used at the ORWRP. Vegetation
samples were harvested from within a 0.5 m2 frame at ground level from 24 plots. Wet
weight was determined in the lab shortly after harvesting. Harvested biomass was then
separated by species and dried at 105 ◦ C for 48 h or until constant weight. For species
that occurred in both the ORWRP and at least one of the mitigation sites, the regression
equations used for determining primary productivity for non-destructive methods were
checked for accuracy using the data collected at the ORWRP.
The dominant species at each wetland site were categorized into three functional
groups based on Boutin and Keddy’s (1993) functional classification of wetland plants.
These groups were ruderals, interstitial and matrix species. Ruderals consist of obligate
and facultative annuals, interstitial included reeds, clonal plants , and tussocks, while
matrix species were clonal stress-tolerators and clonal dominants. Highly disturbed sites
tend to be dominated by ruderals, whereas natural wetlands are dominated by matrix
species (Matthews and Endress 2010).
2.3.5 Statistical Methods
Multivariate analysis of variance (MANOVA) and analysis of variance (ANOVA)
were used to test hypotheses. To avoid problems with pseudoreplication due to small
21
wetland size and proximity of the wetlands across the sites, data for the wetlands at
Trumbull Creek Phase II South, as well as Trumbull Creek Phase II North were combined
to obtain one value for each parameter at these sites. This reduced the data set from 52 to
13 wetland basins. The statistical software program R was used to test assumptions and
perform the MANOVA and ANOVA tests (determined assumptions were met using
D’agostino skewness test, Anscomb–Glynn kurtosis test, Shapiro–Wilks normality test all
at ST = 0.1, normal quantile–quantile plots, and scatter plots) (The R Foundation for
Statistical Computing 2009). Linear regression analysis was then preformed to determine
the type of relationship (positive or negative) between the structural and functional
parameters and age of the mitigation sites. Statistical tables can be found in Appendix F.
A detrended canonical correspondence analysis (DCCA) was preformed to
determine the type of analysis, canonical correspondence analysis or redundancy analysis
(CCA and RDA, respectively), to be used for the data set. A gradient length of 2.52 was
obtained for the first axis. Since the gradient length is less than three, using the
suggestion from Leps and Smilauer (2003), a redundancy analysis (RDA) was used to
analyze patterns in the data. A Monte Carlo test was preformed to test the null hypothesis
that the species data are independent from the environmental variables. The computer
software CANOCO was used for DCCA, RDA, and the Monte Carlo test (ter Braak and
Smilauer 2004). The DCCA, as well as the RDA, were performed on species
presence/absence data and environmental data for the sites. Categorical environmental
variable for type of hydrology and location/ecoregion in Ohio were turned into dummy
variables. All of the continuous environmental variables were standardized since they
22
were on different scales.
2.4 Results
2.4.1 Structural and Functional Parameters
Species richness was highest at the reference research site (98 species), Sandy
Ridge (102 species), and Trumbull Creek Phase I (109 species). The wetland sites had 2–
9 dominant species and 2–13 identifiable plant communities. The Hebron site had the
lowest number of dominant species and communities while the Sandy Ridge site had the
highest number of dominant species and number of plant communities. FQAI scores for
the wetlands ranged from 13.5 to 29.5 at the mitigation banks, 19.9 to 23.8 at the research
site, and 26.9 at the natural wetland. The macrophyte H' ranged from 0.51 to 1.90 at the
mitigation bank wetlands and was 1.75 at the natural reference wetland. The CDI ranged
from 0.90 to 1.75 for the mitigation sites and from 0.84 to 1.44 for the reference sites
(Table 2.2) (Appendix C).
Water depth varied greatly from site to site. Trumbull Creek Phase I North, Sandy
Ridge, and two wetlands at Slate Run had more than 1 m of standing water during August
and September sampling, whereas many of the wetlands within Trumbull Creek Phase II
North and Trumbull Creek Phase II South had no standing water at the time of sampling.
The wetlands at Hebron, ORWRP, Calamus Swamp, and the two remaining wetlands at
Slate Run had water depths between 10 and 100 cm. Area of emergent vegetation was
largest at Sandy Ridge (7.6 ha) and smallest at Trumbull Creek Phase II North (0.23 ha).
Hebron and Trumbull Creek Phase I wetlands had the most area of emergent vegetation,
while ORWRP, Slate Run, and Trumbull Creek Phase II South had the least.
23
WANPP ranged from 201 to 802 g DW m−2 yr−1 at the mitigation bank sites, 529
to 866 g DW m−2 yr−1 at the research reference wetlands and 1093 g DW m−2 yr−1 at
the natural reference wetland (Table 2.2). For functional groupings of the dominant
species at each site, the reference wetlands had 84–100% matrix species (Table 2.3). The
remaining dominant species in the reference wetlands were in the interstitial group. In the
oldest mitigation bank site (Hebron), matrix species accounted for 57% of the dominant
species, while ruderals were 29%. The second oldest mitigation bank site had 20% matrix
species and 40% ruderals (Sandy Ridge). The intermediate age wetland had 45% matrix
species and 36% ruderals. The two youngest mitigation bank sites had the lowest
percentage of matrix species (29–30%). Of all of the sites, the two youngest mitigation
banks had the highest amount of interstitial species (50–57%).
2.4.2 Hypotheses Testing
MANOVA was preformed for the four different hypotheses. P-values of ≤0.05
were found for comparisons of mitigation vs. reference wetlands (MANOVA p=0.003,
F=4.8516, df=5,31), age (MANOVA p=0.001, F=1.887, df=75,105), location in Ohio
(MANOVA p<0.001, F=10.924, df=5,31), and hydrology (MANOVA p<0.001, F=4.153,
df=10,62) for the structural and functional parameters examined. Year sampled had no
effect on any of the dependent variables (MANOVA p=0.407, F=1.8503, df=5,31).
ANOVA was then used to assess the significance of these environmental variables on the
structural and functional vegetation characteristics (Table 2.2). Significant differences
were found between age and WANPP (ANOVA p=0.006, F=8.705, df=1,28) log ANPP
over the entire wetland (ANOVA p<0.001, F=14.374, df=1,28), FQAI score (ANOVA
24
p=0.005, F=9.218, df=1,28), and species richness (ANOVA p=0.033, F=5.031, df=1,28).
Linear regression analysis was used to determine the type of relationship, positive or
negative, between the significant vegetation variables and age of the mitigation sites. A
positive relationship was found between age and WANPP (Fig. 2.2). Negative
relationships were found between age and FQAI sores and species richness (Fig. 2.2).
Log ANPP over the entire wetland did not exhibit a linear pattern, but had higher values
in the younger and older mitigation sites (Fig. 2.2). Significant differences were found
between type of hydrology and WANPP (ANOVA p=0.026, F=4.005, df=2,34), FQAI
score (ANOVA p=0.020, F=4.399, df=2,34), and species richness (ANOVA p< 0.001,
F=16.11, df=2,34). Pulsed hydrology wetlands had the highest WANPP, with high spring
low fall wetlands having the lowest. Continuously flooded wetlands had some of the
highest FQAI scores, whereas high spring, low fall wetlands had some of the lowest
scores. Continuously flooded wetlands had the largest range of species richness across
sites, while pulsed wetlands had all higher values of species richness. Additionally,
significant differences were found between location in Ohio and FQAI scores (ANOVA
p<0.001, F=14.28, df=1,35), number of communities (ANOVA p<0.001, F=29.04,
df=1,35), species richness (ANOVA p=0.011, F=7.151, df=1,35), and CDI (ANOVA
p=0.02, F=5.942, df=1,35). Overall, wetlands located in the northern portion of Ohio had
higher FQAI scores, number of communities, and species richness. Finally, significant
differences were found between the type of wetland (mitigation vs. reference) and
WANPP (ANOVA p<0.001, F=18.63, df=1,35), log ANPP over the entire wetland
(ANOVA p=0.013, F=6.864, df=1,35), FQAI score (ANOVA p=0.042, F=4.451,
25
df=1,35), and species richness (ANOVA p<0.001, F=20.33, df=1,35). WANPP, log ANPP
over entire wetland, FQAI scores and species richness tended to be higher in the natural
and research wetlands than wetlands created for mitigation. There was no statistical
difference between mitigation reference wetlands in terms of number of communities
present, H, and CDI. While, on average, the mitigation wetlands had lower WANPP,
FQAI, species richness, and number of dominant communities, two of the mitigation sites
in northern Ohio (Sandy Ridge and Trumbull Creek Phase I) had species richness, FQAI
scores, and number of dominant plant communities similar to those found in the
reference wetlands .
2.4.3 Redundancy Analysis
The Monte Carlo test found P = 0.002 for the first two canonical axes. This
suggests that species data are not independent of the environmental variables. For the
RDA, the first four axes explained less than 10% of the total variance. Only species with
greater than 20% dominance are displayed on the triplot. From the triplot (Fig. 2.3), the
sites separated out with plots from Trumbull Creek Phase 1 and Sandy Ridge on the right
hand side of the graph and Hebron, Slate Run, Trumbull Creek Phase 2 South, Trumbull
Creek Phase 2 North, ORWRP, and Calamus Swamp on the right hand side of the graph.
Area of emergent vegetation was highest in Sandy Ridge and Trumbull Creek Phase I.
These two sites also had the largest total area and deepest standing water depths. Species
common to these sites were Juncus effuses, Polygonum hydropiperoides, Scirpus
cyperinus, Sparganium americanum, Bidens cernua, and Polygonum hydropiper. The
youngest site, Trumbull Creek Phase II N seems to be following a similar path as Hebron,
26
the oldest mitigation site. The other side of the youngest site, Trumbull Creek Phase II S
seems to be developing along a similar trajectory as Calamus Swamp, the natural
reference wetland, based on environmental variables and dominant species at both sites.
Vegetation typical of these sites includes Typha spp., Cyperus esculentus, and Scirpus
cyperinus. The ORWRP is somewhat similar to Hebron, Calamus Swamp, and both
Trumbull Creek Phase II sites. Vegetation characteristic of the ORWRP include Typha
spp. and Sparganium eurycarpum. Slate Run seems to be the most unlike the other sites.
Species common here include Scirpus pungens, Leersia oryzoides, Cyperus strigosus,
and Ludwigia palustris.
2.5 Discussion
2.5.1 Structure
Within the mitigation bank wetlands, the vegetation structural characteristics
(species richness, FQAI) of the younger sites were similar to the natural and created
reference wetlands. The older sites, however, tended to have lower species richness and
FQAI than the younger sites. The number of communities, CDI, and H' for all mitigation
wetlands was similar to the reference sites. Other studies have found that mitigation
wetlands tend to have high species richness during the monitoring period, but species
richness begins to decline with age (Fennessey and Roehrs 1997; Campbell et al. 2002;
Balcombe et al. 2005; Gutrich et al. 2009) and that indicators based on species
composition were high shortly after wetland creation, but decreased over time (Matthews
2008). Data suggests this is occurring along the time gradient in the mitigation bank
wetlands used for this study. Caution should be taken when examining multiple sites
27
along a time gradient due to variation in construction techniques and goals.
Decreases in species richness and associated parameters with age of the mitigation
wetlands are likely due to disturbance and successional processes occurring after creation
and restoration. Creation and restoration techniques are major disturbances at the sites.
With wetland creation, the ecosystem begins after a large disturbed, with little to no
species present. Succession of vegetation, driven by both biotic and abiotic factors
(competitive exclusion, plant-soil feedback, nutrient input to the system, weather,
climate, and disturbance), results in changes to the structural characteristics of the
ecosystem. Changes in biotic process, such as primary productivity can greatly influence
an ecosystem. Studies have shown that low and high productivity sites tend to have low
species richness, while intermediately productive sites had the highest species richness
(Grime 1979; Tilman 1986). Examining the mitigation bank sites over a time gradient,
changes in the biotic and abiotic processes at the sites are likely responsible for the
decline in structural characteristics. Results from this study suggests that peak species
richness occurs somewhere between the 4- and 7-year mark after wetland creation. This
could be problematic when determining the success of a mitigation bank wetland since
many parameters rely on structural vegetation characteristics and monitoring tends to
only last 5 years.
Since peak species richness seemed to occur between the 4- and 7-year mark,
monitoring time frames of at least 10 years may be more appropriate to capture declines
in structural characteristics after the initial peak than current monitoring time frames. As
far as these study sites are concerned, additional monitoring is needed to determine what
28
changes, if any, there will be in species richness and related parameters but, since the two
mitigation sites are currently higher than the reference wetlands the values may stabilize
near those found in the reference sites.
FQAI scores at the mitigation bank sites and the reference sites fell within the
range of scores (11–34) found in other studies examining marsh habitats in Ohio
(Andreas et al. 2004; Lopez and Fennessy 2002). The reference wetlands tended towards
the middle and top of this range while the mitigation sites fell throughout. This would
suggest that Trumbull Creek Phase I is a relatively good quality wetland given its high
FQAI score, whereas the older and the younger wetlands tend to be of lower quality.
However, differences in wetland classification of the mitigation banks may influence the
floristic quality of the site.
2.5.2 Function
Within the mitigation sites, WANPP increased with age, but was significantly
lower in the reference sites. Of the sites used, all of the reference sites, as well as the
oldest mitigation bank (2010) had high WANPP, most likely due to the high amount of
Typha spp. present in the wetlands. Primary productivity is likely dependent on the types
of species present, not necessarily on the number of species present. As an example, a
wetland dominated by Phragmites australis would likely have high primary productivity,
but low species richness due to its tendency to form monocultures. Windham (2001)
found that P. australis had a peak productivity of almost 2000 g DW m−2 yr−1 . This is
approximately double what was found at the most productive site used in this study.
Since high ANPP is typical of marshes dominated by Typha spp. and invasive species,
29
high ANPP at a mitigation site may not be desirable considering monitoring goals that in
some cases call for less than 5% invasive species at the sites (Davey 2007a,b). On the
other end of the spectrum, low ANPP is not necessarily desirable since improper
hydrology can result in low plant survivorship and thus low NPP (Fraser and Karnezis
2005). Thus the current amount of ANPP, especially at the middle age range, of the
mitigation banks may be desirable.
Functional groups of the dominant species tended towards matrix species as age
of the wetland increased. Newly created wetlands, where no or minimal planting has
occurred tend to see a successional pattern where ruderals or annual species are the first
to dominate at the site. These ruderal species eventually give way to the perennial
interstitial and matrix species (Matthews and Endress 2010). This pattern of succession
can be seen across the mitigation bank wetlands with the older wetlands having a mix of
ruderals, interstitial, and matrix species, and the younger sites have a higher percentage of
interstitial species than matrix. This suggests that all of the mitigation sites are still
maturing functionally, but that the older the sites are the closer the functional groups of
the dominant species resemble the reference wetlands. This is not surprising given that
other studies have found that at about the 20-year mark, mitigation wetlands were
reaching a state of vegetation equilibrium and that vegetation in mitigation wetlands was
similar to natural reference wetlands (Atkinson et al. 2005; Balcombe et al. 2005; Gutrich
et al. 2009; Spieles 2005).
2.5.3 Mitigation Bank and Reference Wetlands Comparison
Looking at a direct comparison of function to structure, in this case productivity
30
to diversity, the reference sites were high productivity, high diversity wetlands in
comparison to the sites used in this study (Fig. 2.4). Of the mitigation sites examined,
Trumbull Creek Phase I site was also a high productivity, high diversity wetland. Sandy
Ridge and Trumbull Creek Phase II tended to be high diversity, low productivity
wetlands, while Hebron and Slate Run were mainly low diversity, low productivity
wetlands. Trumbull Creek Phase I most resembles the reference wetlands used in this
study. Some studies examining the relationship of species richness to productivity in
plants have found that both low and high productivity sites tend to have low species
richness, while intermediate productivity sites had the highest species richness (Grime
1979; Tilman 1986). Of the sites in this study, the ORW, Trumbull Creek Phase I, and
Sandy Ridge fall somewhere in this intermediary productivity range and thus have the
highest diversity of the sites. ANPP at these three sites falls within the range of
approximately 450–750 g DW m−2 yr−1 when weighted by community area within the
zone of emergent vegetation. Based on the findings of this study regarding ANPP and
species richness, diversity is highest under median levels of ANPP, which could be useful
in the regulation process when determining target goals in created freshwater marshes to
allow for optimum productivity and diversity at the sites.
The younger mitigation banks are structurally more similar to the reference
wetlands than are the older mitigation banks. This may be due to better initial
construction techniques of the newer sites. The younger sites had more detailed and better
developed goals than the older sites (such as specifics about water depths, allowable %
invasive species cover, % cover of specific wetland habitat/wetland communities, and
31
types of specific wetland habitat) (Davey 2007c), probably due to a combination of
learning on the part of the mitigation bank creator, as well as stricter requirement
established for wetland mitigation. From a functional standpoint, none of the mitigation
sites were statistically similar to the reference sites, but the older mitigation sites were
more similar than the younger sites. This suggests that the sites are heading towards
functional similarity with the reference wetlands and may need more time to mature
before determining functional success.
It is important to take into account the fact that the wetlands examined in this
study were located in different ecoregions of the state of Ohio and fell in different
hydrogeomorphic classifications. It is possible that these differences may have
influenced the structural and functional characteristics of the sites. However, there is
some difficulty in finding mitigation bank wetlands created by the same company (which
may aid in reducing large differences associated with construction) along the desired time
gradient, in the same ecoregion, and of the same wetland type. A large-scale study
including multiple wetlands in each ecoregion and of each hydrogeomorphic
classification along the desired time gradient with multiple reference wetland in each
ecoregion and hydrogeomorphic classification would likely reduce the influence of
location and classification.
2.6 Conclusions
The younger mitigation sites were structurally similar to the reference wetlands
while none of the mitigation sites were quite on the same level functionally as the
reference sites. Only time will tell if these younger wetlands will remain structurally
32
similar to the reference wetlands and if the mitigation sites will develop functional
characteristics similar to those of the reference wetlands. Based on the results of this
study, there are three recommendations for improving the restoration/creation and
monitoring practices of mitigation bank wetlands:
• Extend monitoring to at least 10–15 years after creation to allow the
structural characteristics of the wetland to stabilize before determining if
the mitigation project was a success.
• Monitor trends in the succession of functional groups to help determine
if the mitigation wetlands are functionally equivalent to natural wetlands.
• Aim for median levels of ANPP within emergent zones for enhanced
diversity.
The use of non-destructive ANPP and functional group classifications as
monitoring tools requires little additional time in the field and can be derived mainly
from currently assessed structural parameters, particularly when regression equations for
biomass determination have already been established. Additional research
is needed to examine more in-depth the roles of individual species within these sites as
well as the environmental variables likely associated with the specific plant assemblages
at each site and how this may influence the success of a mitigation project.
2.7 References
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created depressional wetlands. Wetland Ecology and Management 13, 469–478.
Balcombe, C.K., Anderson, J.T., Fortney, R.H., Rentch, J.S., Grafton, W.M., & Kordek,
W.S., 2005. A comparison of plant communities in mitigation and reference
wetlands in the Mid Appalachians. Wetlands 25, 130–142.
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lowland wetlands in Hawai’i. Wetlands 29, 1023–1035.
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Vegetation Science 4, 591–600.
Brinson, M.M.,1993. A hydrogeomorphic classification for wetlands. In: Technical
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Brinson, M.M., & Rheinhardt, R., 1996. The role of reference wetlands in functional
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Bruland, G.L., & Richardson, C.J., 2005. Spatial variability of soil properties in created,
restored, and paired natural wetlands. Soil Science Society of America Journal 69,
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Campbell, D.A., Cole, C.A., & Brooks, R.P., 2002. A comparison of created and natural
wetlands in Pennsylvania, USA. Wetland Ecology and Management 10, 41–49.
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Wetlands Mitigation Bank, North Ridgeville, Ohio. Davey Resource Group, Kent,
OH.
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Report, Trumbull Creek Wetlands Mitigation Bank Phase 2 North and South,
Thompson Township Geauga County and Trumbull Township Ashtabula County,
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Elifritz, B.F., & Fennessy, M.S.,1999. A comparison of natural and constructed wetlands using the floristic quality assessment index. In: Olentangy River Wetland
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69–73.
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Wetland Mitigation in the United States. Washington, DC, Environmental Law
Institute, www.eli.org/Program Areas/WMB.
Fennessey, S., & Roehrs, J., 1997. A Functional Assessment of Mitigation Wetlands
in Ohio: Comparisons With Natural Systems. Ohio Environmental Protection
Agency Division of Surface Waters, Columbus, OH.
Fraser, L.H., & Karnezis, J.P., 2005. A comparative assessment of seedling survival and
biomass accumulation for fourteen wetland plant species grown under minor
water-depth differences. Wetlands 25, 520–530.
Grime, J.P., 1979. Plant Strategies and Vegetation Processes. John Wiley and Sons,
New York, NY.
Gutrich, J.J., Taylor, K.J., & Fennessy, M.S., 2009. Restoration of vegetation
communities of created depressional marshes in Ohio and Colorado (USA): the
importance of initial effort for mitigation success. Ecological Engineering 35,
351–368.
Johnson, S.A., 1998. Effects of hydrology and plant introduction techniques on firstyear macrophyte growth in a newly created wetland. Thesis. The Ohio State
University, Columbus, OH.
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Keddy, P., Fraser, L.H., & Wisheu, I.C., 1998. A comparative approach to examine competitive response of 48 wetland plant species. Journal of Vegetation Science 9,
777–786.
Lenssen, J., Menting, F., van der Putten, W., & Blom, K., 1999. Control of plant species
richness and zonation of functional groups along a freshwater flooding gradient.
Oikos 86, 523–534.
LePage, B.A., 2011. Wetlands: A Multidisciplinary Perspective. Springer, Netherlands.
Leps, J., & Smilauer, P., 2003. Multivariate Analysis of Ecological Data using CANOCO.
Cambridge University Press, Cambridge, United Kingdom.
Lopez, R.D., & Fennessy, M.S., 2002. Testing the floristic quality assessment index as
an indicator of wetland condition. Ecological Applications 12, 487–497.
Matthews, J.W., 2008. Restoration progress and plant community development
in compensatory mitigation wetlands. Dissertation Abstracts International.
69, 272.
Matthews, J.W., & Endress, A.G., 2010. Rate of succession in restored wetlands and the
role of site context. Applied Vegetation Science 13, 346–355.
McCune, B., & Grace, J.B., 2002. Analysis of Ecological Communities. MJM Software
Design, Glenedon Beach, OR.
Mitsch, W.J., & Wilson, R.F., 1996. Improving the success of wetland creation and
restoration with know-how, time, and self-design. Ecological Applications 6, 77–
83.
Mitsch, W.J., Wu, X., Nairn, R.W., Weihe, P.E., Wang, N., Deal, R., & Boucher, C.E.,
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1998. Creating and restoring wetlands: a whole-ecosystem experiment in selfdesign. BioScience 48, 1019–1030.
Mitsch, W.J., Wang, N., Zhang, L., Deal, R., Wu, X., & Zuwerink, A., 2005a. Using ecological indicators in a whole-ecosystem wetland experiment. In: Handbook of
Ecological Indicators for Assessment of Ecosystem Health. CRC Press, Boca
Raton, FL, pp. 211–236.
Mitsch, W.J., Zhang, L., Anderson, C.J., Altor, A., & Hernandez, M., 2005b. Creating
riverine wetlands: ecological succession, nutrient retention, and pulsing effects.
Ecological Engineering 25, 510–527.
Mitsch, W.J., & Gosselink, J.G., 2007. Wetlands, 4th ed. John Wiley & Sons, Inc.,
Hoboken, NJ.
Mitsch, W.J., Zhang, L., Stefanik, K.C., Nahlik, A.M., Anderson, C.J., Bernal, B.,
Hernandez, M., & Song, K., 2012 Creating wetlands: primary succession, water
quality changes, and self-design over 15 years. BioScience 62, 237-250.
Muzika, R.M., Gladden, J.B., & Haddock, J.D., 1987. Structural and functional aspects
of succession in south-eastern floodplain forests following a major disturbance.
American Midland Naturalist 117, 1–9.
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Water Act, Committee on Mitigating Wetland Losses. National Academies Press,
Washington, DC.
Niswander, S.F., & Mitsch, W.J., 1995. Functional analysis of a two-year-old created
in-stream wetlands: hydrology, phosphorus retention, and vegetation survival
38
and growth. Wetlands 15, 212–225.
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Reiss, K.C., Hernandez, E., & Brown, M.T., 2009. Evaluation of permit success in
wetland mitigation banking: a Florida case study. Wetlands 29, 907–918.
Salzman, J., & Thompson Jr., B.H., 2007. Environmental Law and Policy. Foundation
Press, New York, NY.
Spieles, D.J., 2005. Vegetation development in created, restored, and enhanced mitigation
wetland banks of the United States. Wetlands 25, 51–63.
Spieles, D.J., Coneybeer, M., & Horn, J., 2006. Community structure and quality after
10 years in two central Ohio mitigation bank wetlands. Environmental
Management 38, 837–852.
ter Braak, C.J.F., & Smilauer, P., 2004. Canoco for Windows (Version 4.53). BiometricsPlant Research International, Wageningen, Netherlands.
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Plant Ecology. Blackwell Scientific, Boston, MA.
Tilman, D., Knops, J., Wedin, D., Reich, P., Ritchie, M., & Siemann, E., 1997. The
influence of functional diversity and composition on ecosystem processes.
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Thursby, G.B., Chintala, M.M., Stetson, D., Wigand, C., & Champlin, D.M., 2002. A
rapid, non destructive method for estimating aboveground biomass of salt marsh
grasses. Wetlands 22, 626–630.
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soil survey. http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey. aspx
(accessed 23.02.11).
van der Valk, A.G., Toth, L.A., Gibney, E.B., Mason, D.H., & Wetzel, P.R., 2009.
Potential propagule sources for reestablishing vegetation on the floodplain of the
Kissimmee river, Florida, USA. Wetlands 29, 976–987.
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Phragmites australis (common reed) and Spartina patens (salt hay grass) in
brackish tidal marshes of New Jersey, USA. Wetlands 21, 179–188.
40
Table 2.1 Year of creation, site and wetland areas, hydrology, hydrogeomorphic
classification, soil type, and location of Ohio wetland mitigation banks and reference
wetlands (Acton, 2004; Brinson, 1993; Davey, 2007a,b; United States Department of
Agriculture Natural Resources Conservation Service, 2011)). For hydrogeomorphic
classification (HGM), the first letter is the geomorphic settings, the second letter is the
water source, and third letter is the hydrodynamics. D: depressional, R: riverine, P:
precipitation, S: surface flow, G: groundwater, V: vertical fluctuation, and U:
unidirectional flow. All wetlands were dominated by emergent vegetation. It is unknown
how long current dominant vegetation at the natural site has been occurring at this site.
Hebron
Year
created
Total site
area (ha)
Total area
of
wetlands
investigated (ha)
Mitigation Bank Wetlands
Trumble
Sandy
Slate Run
Creek
Ridge
Phase I
Reference Wetlands
Trumble
Creek
Phase 2
Calamus
Swamp
Olentangy
Wetland Park
1992
1998
1999
2000
2005
Natural
1994
13.4
46.5
63.9
56.7
117.8
7.7
21
8.8
20.7
9.1
6.2
1.2
4.9
2
Wetland
hydrology
High
spring
low fall /
Continuously
flooded
Continuously
flooded
High
spring low
fall
Continuously
flooded
High
spring low
fall
High Spring
low fall
Pulsing
HGM
classification
DPV
DP/SV
DPV and
DP/GV
DPV
DPV
DPV
RSU
Dominant
soil type
Luray
Silty Clay
Loam
Fitchville
Silt Loam
Kokomo
Silty Clay
Loam
Platea and
Sheffield
Silt Loam
Sheffield
Silt Loam
Montgomery Silty
Clay Loam
Ross Silt Loam
Location
Licking
County
North
Ridgeville,
Lorain
County
Pickaway
County
GeaugaAshtabula
Counties
GeaugaAshtabula
Counties
Circleville,
Pickaway
County
Columbus,
Franklin
County
41
Table 2.2 Average ± standard error of structural and functional parameters at each
wetland site. Species richness, number of communities, Ohio floristic quality assessment
index (FQAI), Shannon–Wiener diversity index (H ), community diversity index (CDI),
and weighted ANPP (WANPP) of all wetland sites over 2008, 2009, and 2010. Calamus
Swamp data are for 2010 only. FQAI defined by Andreas et al. (2004). CDI defined by
Mitsch et al. (2005a,b). Functional group ratios of ruderal:interstitial:matrix categories
for the dominant species at each site were determined using Boutin and Keddy (1993),
Keddy et al. (1998), and Lenssen et al. (1999). P-values were determined using ANOVA.
All statically significant P-values (≤0.05) are in bold.
Reference
Wetlands
Mitigation Bank Wetlands
Hypothesis p-values
Mitigation
Sandy Slate
Cala
vs.
Hydro
Hebron Ridge Run TCPI TCPII -mus ORW reference Age -logy
Species
richness
S
Number of
t
communr
ities
u
c
t
FQAI
u
r
H
e
CDI
F
u
n
WANPP
c
t
i
o Functional
n group ratio
44
±2.3
44.6 88.7
68
±2.8 ±10.5 ±4.3
87
95.5
±1.2
<0.001
0.033
<0.00
1
0.011
11.3
5.3
8.3
7.8
±0.88 ±0.63 ±0.33 ±0.60
7
4.8
±0.54
0.254
0.72
0.367
<0.001
15.6
±0.54
22.8 17.1 27.1 20.5
21.7
±1.3 ±0.57 ±1.39 ±0.86 26.9 ±0.78
0.042
0.005
0.02
<0.001
1.20
±0.21
1.27 1.10 1.47 1.23
±0.27 ±0.11 ±0.19 ±0.22 1.75
0.212
0.209 0.794
0.409
1.10
±0.13
1.01 0.77 1.28 1.45
1.34
±0.15 ±0.09 ±0.11 ±0.14 0.84 ±0.10
0.165
0.187 0.209
0.02
<0.001
0.006 0.026
0.77
4
±1
91
±6.3
Location
441
±45
663
±16
440
±51
na
451
±52
452
±44
2:1:4
2:2:1 4:2:5 1:4:2 2:5:3 0:0:4 0:1:5
42
1093
682
±52
Table 2.3 Functional groups of dominant wetland plant species at the five mitigation
sites and two reference sites. Functional groups are defined by Boutin and Keddy (1993),
Keddy et al. (1998), and Lenssen et al. (1999).
Mitigation Bank Wetlands
Hebron
R
U
D
E
R
A
L
S
Bidens cernua
I
N
T
E
R
S
T
I
T
I
A
L
Alisma plantagoaquatica
Sandy
Ridge
X
Cyperus spp.
Echinochloa spp.
Eleocharis obtusa
Polygonum
pensylvanicum
X
X
X
Scirpus pungens
Sparganium
americanum
Sparganium
eurycarpum
Typha spp.
X
X
X
Calamus
Swamp
ORW
X
X
Leersia orizoides
M Polygonum
A hydropiperoides
T
Schenoplectus
R
tabernaemontani
I
X Scirpus fluviatilis
X
X
X
Decodon
verticallus
Phalaris
arundinacea
Phragmites
australlis
Reference Wetlands
Trumbull
Creek
Phase 2
X
Juncus effusus
Juncus torreyi
Pontederia
cordata
Scirpus cyperinus
Slate
Run
X
Trumbull
Creek
Phase 1
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
43
X
Figure 2.1 Location of wetland sites within Ohio, USA. Sandy Ridge, Trumbull Creek
Phase I and Trumbull Creek Phase II North and South are located in the northern
portion of the state, while Hebron, Slate Run, Olentangy River Wetland Research
Park (ORWRP), and Calamus Swamp are located in central Ohio.
44
Chapter 3: Vegetation productivity of riverine planted and unplanted wetlands
fifteen to seventeen years after creation
3.1 Summary
1. Many created wetlands are monitored for only five years after creation before
being deemed a success or failure. At the five-year mark, many wetlands are still
developing and the plant assemblages may not be an accurate representation of
what will be present once the ecosystem matures. Initial creation techniques, such
as planting, are likely to affect vegetation structure and function over time.
2. The goal of this study was to compare macrophyte structure and function between
a planted and unplanted wetland from fifteen to seventeen years after creation.
3. Samples for emergent macrophyte vegetation were collected at twenty-four 0.5 m2
plots in the two wetlands from April -September of 2008 - 2010. Transitional zone
vegetation was sampled on a monthly basis from April through September of
2008 and 2009. Data collected and parameters measured included, species
richness, Floristic Quality Assessment Index scores (FQAI), Community
Diversity Index scores, number of macrophytes species, above and belowground
net primary productivity, and macrophyte nutrient concentration.
4. The planted wetland had higher species richness and FQAI scores than the
unplanted wetland. The unplanted wetland had more area occupied by invasive
48
species than the planted wetland. Community diversity and number of
macrophytes were similar between wetlands.
5. Aboveground net primary productivity was higher for emergent vegetation in the
unplanted wetland (796 to 866 g DW/m2 year) than the planted wetland (673 to
712 g DW/m2 year)(p=0.006). Belowground net primary productivity was similar
between the two wetlands and there was no difference in total net primary
productivity.
6. Within the transitional zone, there was no difference in edge herbaceous
aboveground productivity, belowground productivity, woody net primary
productivity or litterfall.
7. Synthesis and applications. While planting of a created riverine wetland may not
be seen as being as important as planting isolated created wetland, this study did
find some differences in structural and functional parameters between the planted
and unplanted wetland. Planting in a riverine system may aid in reducing area
occupied by invasive species, and increase species richness and floristic quality in
the long term.
3.2 Introduction
The act of wetland creation is a high impact disturbance of an ecosystem in which
the initial creation process will likely have an effect on succession of the system. The
successional trajectory, or the direction of change in vegetation over time, of a system can
progress in a variety paths along a continuum from both low quality to high quality
49
ecosystems and from early successional to late successional types of vegetation (Suding
and Cross 2006). Created wetland systems can be thought of as highly disturbed, low
quality systems at the beginning of creation, but will develop over time, influenced by
such things as position in the landscape, availability of propagules, and soil compaction
at the site (Mitsch and Wilson 1996; Campbell et al. 2002; Bruland and Richardson 2005;
Bantilan-Smith et al. 2009; van der Valk et al. 2009; Ahn and Dee 2011).
Since the 1980s, wetland creation and restoration to mitigate the loss of natural
wetlands has been a growing practice. There has been evidence that over the years the
overall quality of mitigation wetlands has been increasing (NRC 2001). Unfortunately,
monitoring usually occurs for the first five years after creation to determine whether or
not the wetland is successful (Mitsch and Wilson 1996; NRC 2001). Once deemed
successful, mitigation wetlands may be managed, but additional monitoring is unlikely.
The trajectory of succession may not be fully visible after the first five years of creation
(Matthews et al. 2009). Studies have shown that there tends to be an initial peak in
structural parameters of a system, which then declines over time (Fennessey and Roehrs
1997; Campbell et al. 2002; Balcombe et al. 2005; Spieles 2006; Gutrich et al. 2009;
Stefanik and Mitsch 2012). It is difficult to determine the exact point where this initial
peak will occur, so it may be more accurate to either wait for the peak to occur before
trying to predict successional trajectories or to reexamine the successional trajectory
following the peak in structural parameters. Additionally, it may take a number of years
for a created wetland to develop soils that are similar to natural wetlands in terms of soil
organic matter, nutrient content, and seed bank, which can affect both the types of species
50
that colonize a wetland and the function of the species present (Choi 2004).
Future disturbances to the system (both natural and anthropogenic) can alter
successional trajectories. The frequency and severity of disturbance to a system plays a
roll in the length of time spent in a particular successional stage, as well as how often
ecosystems revert to early successional stages (Walker and del Moral 2008). It is also
possible that disturbances to the site may not allow the vegetation to progress beyond
early successional stages. Disturbances that affect dominant species instead of minor
species will likely have a greater impact on the entire ecosystem than disturbance that
affect minor species. If there are minor species in the ecosystem that functionally overlap
the dominant species, it is possible for the ecosystem to be somewhat resilient to
disturbances (White and Jentsch 2001). During creation and restoration projects, it is
important to take into account that future disturbances are inevitable and steps should be
taken to increase the resilience of the system. One way to increase resiliency may be to
plant a wide variety of species with overlapping functional traits. Mitsch and Wilson
(1996) suggested that with wetland creation and restoration, the best approach for
vegetation introduction may be to introduce as many species as possible and then allow
the system to self-design or allow nature to chose the species most appropriate for the
system.
For this study, an initial experiment was implemented in which two wetlands of
equal size and hydrologic regime were constructed and one was planted, while the other
was left to rely on natural colonization. The purpose of this project was to examine
vegetation development, both above- and belowground, of two created riverine wetlands
51
fifteen to seventeen years after establishment to determine how the trajectories taken by
both wetlands differ and to reexamine the initial experiment of planting versus not
planting on the success of the two wetlands. It was hypothesized that:
1. Structural characteristics, along with functional diversity will be greater (i.e.
higher diversity, species richness, floristic quality, etc.)within the planted created
wetland than for an unplanted created wetland.
2. The planted created wetland will have greater total net primary productivity,
aboveground net primary productivity, and belowground net primary productivity
than the unplanted created wetland.
3. Initial planting efforts will effect the structural and functional characteristics of
the surrounding transitional zone from wetland to upland since plant-soil
feedbacks began sooner in the planted wetland, likely causing soil characteristic
changes to occur sooner in the planted wetland.
From this study, we have shown that planting may be beneficial even in riverine
wetlands that receive frequent propagule inputs, particularly in terms of structural
characteristics.
3.3 Materials and Methods
3.3.1Study Site
The two experimental wetlands at the Olentangy River Wetland Research Park,
Ohio State University, Columbus, Ohio were used to examine vegetation dynamics in
created wetlands (Fig. 3.1). The two experimental wetlands are each 1-ha in size and
receive the majority of their hydrologic inputs via pumps from the Olentangy River
52
which boarders the 21-ha facility. Construction began in 1993, with river water input
beginning March 1994. In spring 1994, an initial vegetation succession experiment was
implemented in which wetland one was planted with 13 wetland species, while
vegetation was allowed to naturally colonize wetland two. No tree species were planted
along the edge of either wetland.
3.3.2 Structural Characteristics
Structural vegetation characteristics of the two wetlands were examined annually
from 2008-2010. Species richness, Ohio Floristic Quality Assessment Index (FQAI),
Community Diversity Index (CDI), and type of vegetation were determined for each
wetland. FQAI was determined using the equation:
FQAI= ∑ ( CofC i )/ √(N )
where C of Ci is the coefficient of conservatism for each species as determined by
Andreas et al. (2004). N is the number of native species. C of Ci values range from 0 to
10 based on degree of fidelity in relation to other species in Ohio (Andreas et al. 2004),
with lower values being characteristic of species found in highly disturbed areas, while
species with higher values tend to have narrow ecological niches (Lopez and Fennessy
2002; Ahn and Dee 2011).
CDI was determined using the equation:
CDI=−∑ (Ci ln C i )
where C is the relative area of each wetland community (Mitsch et al. 2005a,b). This
equation is similar to that used for the Shannon-Wiener Diversity index commonly used
in biological studies. CDI uses community area to estimate richness and evenness based
53
on the spatial patterns of the vegetation communities.
Plant species can be separated into different categories based on habitat type.
Reed (1998) categorizes wetland plants based on the probability that they are found in
wetlands including statuses of obligate, facultative wet, facultative, facultative upland, or
upland species. Obligate and facultative wet are considered to be wetland species in this
study. All plant species found within the two wetlands were categorized using wetland
indicator status.
3.3.3 Functional Characteristics
Aboveground plant biomass was examined on a monthly basis over the course of
the growing season (April – September) from 2008-2010. The sequential harvest method
for aboveground net primary productivity (ANPP) was employed (Fahey and Knapp
2007). Live vegetation samples were harvested from within a 0.5m² sampling frame at
ground level from 12 plots in each wetland. Plots were located in the dominant plant
communities and were selected to be representative of the average within the community.
Wet weight was determined in the lab shortly after harvesting. Harvested biomass was
separated by species and dried at 105°C for 48 hours or until constant weight (Mitsch et
al. 2006). Biomass measurements were weighted by community area using percent
community area found using GPS data and aerial photographs multiplied by sample plots.
The equation:
ANPP=(b1-b0)+(b2-b1)+(b3-b2)+.....
was used to estimate annual ANPP. b represents the biomass for a specific sampling
event. Only positive accumulation from one month to the next was included (Fahey and
54
Knapp 2007). b0 is assumed to have a biomass of 0 g DW/m2 due to lack of production in
winter months .
Belowground biomass was sampled by taking 30 cm deep soil cores within each
of the twenty-four areas sampled for aboveground biomass in the two wetlands. These
samples were taken with a golf course hole cutter (10 cm diameter and approximate
volume of 2356 cm³). Core samples were sectioned into 0-10 cm, 10-20 cm, and 20-30
cm depths and taken back to the lab. Samples were collected on a monthly basis from
April through September for all three years of the study. Once in the lab, sectioned soil
cores were passed through a sieve. All roots and rhizomes were oven dried at 105°C for
48 hours and then weighed to estimate belowground net primary productivity (g DM/
m2). Biomass measurements were weighted by community area using GPS data.
Belowground net primary productivity (BNPP) on a monthly basis was estimated from
belowground biomass samples using the equation:
BNPPmonthly=(b2-b1)/(t2-t1)
where b represents the biomass for a given sampling period and t represents the time of
sampling. Annual BNPP was estimated using the equation:
BNPPannual=bmax–bmin
with bmax representing the last biomass sample and bmin representing the first biomass
sample (Sala et al., 2000). Above and belowground net primary productivity for each
sampling area was summed to determine total net primary productivity (TNPP). TNPP
for all twelve area was then averaged to obtained total biomass in g DW/ m2 year.
Functional group composition of the dominant plant communities was determined
55
for each wetland annually. Dominant species were categorized as either ruderal,
interstitial, or matrix species using Boutin and Keddy's (1993) functional classification
system of wetland macrophytes. Ruderals included obligate and facultative annuals that
are found in highly disturbed areas and act as colonizing species. Interstitial species
included reeds, clonal plants and tussocks that are seen during intermediate successional
stages, while matrix species are clonal stress-tolerators and clonal dominants that tend to
be found in natural wetlands and during later successional stages (Matthews and Endress
2010).
The dominant macrophyte species were analyzed for nutrient composition in
August 2010. Seventy-nine samples were collected from both experimental wetlands.
Samples were dried at 65ºC for forty-eight hours. After drying, samples were cut into
approximately 2.5 cm sections, put into a paper bag, and shaken to randomly distribute
pieces of stems, leaves, and reproductive tissue. The samples were ground to a powder
using a Cianflone Pica Blender-Mill model 2601. Analysis of the samples for N, P, K,
Ca, Al, Fe, Mn, and Na was preformed by Service Testing and Research (STAR)
Laboratory, Wooster, Ohio. Percent N was determined using the Dumas method of
combustion analysis (AOAC International 2002) run on an Elementar America's
VarioMax C-N combustion analyzer (OARDC 2012). Percent N was compared to 2004
and 2005 data collected in the same wetlands (Hernandez 2006). All other elements were
ashed at 500°C and dissolved in 10% nitric acid. After dissolution, the samples were run
on an Inductively Coupled Plasma spectrophotometer (Teledyne Leeman Labs Prodigy
Dual View IPC) (OARDC 2012).
56
In addition to biomass within the wetlands, above and below ground biomass was
measured in the surrounding edge. Woody and herbaceous edge vegetation were sampled
in six transects along the inner wetland edge forest for each wetland. Each transect was
2-m wide and extended from the wetland edge to the upland forest edge. Length of
transect varied from 8 to 27 m. Wetland edge boundary is defined as the point between
macrophyte species and woody vegetation. Upland forest edge is defined as the
boundary between wetland woody and herbaceous vegetation and upland woody and
herbaceous vegetation. Both wetland edge and upland edge were determined via visual
inspect of species present. Sampling occurred on a monthly basis from April - September
2008 and 2009. A 0.25m2 PVC sampling frame was used to sample herbaceous
vegetation. All herbaceous material within the frame was harvested and dried in the same
manner as vegetation within the wetlands. Belowground biomass was collected with a
modified golf course hole cutter (diameter 5 cm). Samples were taken to a depth of 30
cm and have a volume of 1178 cm³. Cores were sectioned into 10 cm, 10-20 cm, and 2030cm depths. In the lab, roots and rhizomes were separated and dried using the methods
explained above for macrophyte vegetation. ANPP and BNPP were estimated using the
same equations used for macrophyte species.
Total above ground biomass for woody vegetation of the edge was estimated from
equations created by the USDA Forest Service (Jenkins et al. 2003) in 2008 and 2009.
Diameter at breast height (dbh) was measure and species was determined for all trees
within each transect. These measurements were put into the equations to determine
biomass in kg DW/ m2. To determine aboveground net primary productivity of woody
57
vegetation, total aboveground biomass for 2008 was subtracted from total aboveground
biomass for 2009. Twelve 0.25 m2 litter traps were placed within the transects to estimate
litterfall from October 2008 through October 2009. Litter was collected from the traps on
a bi-weekly basis. Litter was taken back to the lab and sorted into leaf, woody, and
reproductive material and then dried for 48-hr at 105°C and used to determine net
productivity from dry weight.
3.3.4 Statistical Analysis
ANOVA and two-way ANOVA were used to test the structural and functional
parameters. The statistical software program R was used to test assumptions and
perform the statistical tests (determined assumptions were met using D’agostino
skewness test, Anscomb-Glynn kurtosis test, Shapiro-Wilks normality test all at α= 0.1,
normal quantile-quantile plots, and scatterplots) (The R Foundation for Statistical
Computing 2009). Any non-normal data was either transformed or outliers were
removed from the data set. Beaver herbivory of tree species within some of the edge
transects was an issue throughout the study. Transects where large amounts of beaver
herbivory occurred were discarded from tree primary productivity calculations and
statistical analysis. Four transects were removed from both 2010 and 2011. Two-way
ANOVA with independent variables of planted or unplanted and year of sampling was
used to determine if there were any significant differences for structural characteristics
and productivity measurements on both an annual and monthly basis over the three
sampling years. ANOVA was used to examine vegetation nutrient content of the two
wetlands. To deal with pseudoreplication issues due to the limited number of wetlands,
58
structural and annual functional characteristics were examined using one value per
sampling year per wetland. Edge woody vegetation and litter trap data were examined on
a transect and annual basis per wetland. Statistical analysis of nutrient composition was
based on the total number of samples for nutrient analysis collected in 2010. Statistical
tables can be found in Appendix F.
3.4 Results
3.4.1 Experimental Wetlands
Within the two created wetlands, there was no statistical difference between year
sampled for any of the structural parameters over the three years. There was also no
difference for community diversity index (CDI) or number of macrophytes based on
wetland (ANOVA p=0.512, F=0.624, df=1,2; ANOVA p=0.235, F=2.847, df=1,2
respectively). Ohio FQAI scores and species richness were both higher in the planted
wetland (ANOVA p=0.002, F=540.8, df=1,2; ANOVA p=0.019, F=50, df=1,2) (Table
3.1). Dominant species differed between the two wetlands, with the planted wetland
dominated by Scirpus fluviatilis, Sparganium eurycarpum, Typha spp. and the unplanted
wetland dominated by Leersia oryzoides, Phragmites australis, Schoenoplectus
tabernaemontani, and Typha spp (see Appendix D for community vegetation maps). All
dominant species in the planted wetland were matrix species. In the unplanted wetland,
three of the dominants species were matrix species, while the fourth was an interstitial
species (Table 3.2). The unplanted wetland had more area occupied by invasive species
(Phragmites australis 1.8%, 2.1%, and 3.4% for 2008, 2009, and 2010) than did the
planted (0% all years). Nine of the thirteen originally planted species (Mitsch et al. 1998)
59
were present in the planted wetland.
Weighted ANPP peaked in July for the planted wetland all three years and for the
unplanted wetland in 2008 and 2009. In 2010, peak biomass occurred in August for the
unplanted wetland (Fig. 3.2). ANPP was statistically different between the two wetlands
(ANOVA p=0.006, F=158.62, df=1,2) and ranged from 673 to 712 g DW/m2 year in the
planted wetland and 796 to 866 g DW/m2 year in the unplanted wetland. ANPP
accumulation tended to be greatest in May, with accumulation rates ranging from 223 to
360 g DW/m2 month in the planted wetland and 285 to 555 g DW/m2 month in the
unplanted wetland (Fig. 3.3).
BNPP was about the same for the unplanted wetland (2132 to 2362 g DW/m2
year) and the planted wetland (1832 to 2170 g DW/m2 year) (ANOVA p=0.234, F=2.832,
df=1,2). Monthly BNPP was highest in September (planted: 786 to 960g DW/m2 month;
unplanted: 1108 to 1407 g DW/m2 month). Initial biomass readings in April suggest a
stable rootstock of about 1000 to 1500 g DW/m2 within both wetlands (Fig. 3.4).
Examining the depth profiles, the planted wetland had an average of 54% of root biomass
within the 0-10 cm depth, 40% of root biomass within the 10-20cm depth, and 3% of root
biomass within the 20-30 cm depth. The unplanted wetland had an average of 66% root
biomass within the 0-10 cm depth, 25% root biomass within the 10-20 cm depth, and 5%
root biomass within the 20-30 cm depth.
Total net primary productivity (TNPP), ranged from 2505 to 2865 g DW/m2 year
for the planted wetland and 2944 to 3041 g DW/m2 year for the unplanted wetland.
There was no statistical difference between the two wetlands (ANOVA p=0.113, F=7.355,
60
df=1,2) or from one year to the next in each wetland (ANOVA p=0.207, F=3.385,
df=1,2).
The aboveground biomass within the planted and unplanted wetlands had
nitrogen contents of 14.3±0.7 and 14.7±0.6 mg N/g respectively. Phosphorus content of
the aboveground biomass was 2.1±0.22 mg/g in the planted wetland and 1.9±0.11 mg/g
in unplanted wetland (Table 3.3). There was no statistically significant difference
between the two wetlands for N or P (ANOVA p=0.445, F=0.59, df=1,77 and ANOVA
p=0.953, F=0.004, df=1,77 respectively), nor was there a significant difference between
2010 and 2004/2005 N concentrations (ANOVA p=0.115, F=7.209, df=1,77). The
planted wetland had higher concentrations of K, Ca, Al, and Fe (ANOVA p=0.003,
F=9.652, df=1,77; ANOVA p=0.0499, F=3.969, df=1,77; ANOVA p=0.007, F=7.642
df=1,77; and ANOVA p=0.0351, F=4.603, df=1,77 respectively). Nutrient content
differed by species for K, Ca, Al, and Fe (ANOVA p<0.001, F= 15.37, df=8,70; p<0.001,
F=32.38, df=8,70; p=0.001, F=3.719, df=8,70; p=0.002, F=3.529, df=8,70, respectively).
3.4.2 Edge Vegetation
Herbaceous vegetation peaked in August for both wetlands. Edge herbaceous
aboveground net primary productivity (HANPP) averaged 111±10.1 g DW/m2 year for
the planted wetland and 88±8.2 g DW/m2 year for the unplanted wetland. There was no
statistical difference between the two wetlands or from one year to another (ANOVA
p=0.368, F=2.351, df=1,1; ANOVA p=0.874, F=0.04, df=1,1 respectively) (Table 3.1).
Edge belowground net primary productivity (EBNPP) was not significantly different
between the two wetlands (ANOVA p=0.188, F=10.8, df=1,1), with the planted wetland
61
averaging 1602±598 g DW/m2 year and the unplanted wetland averaging 2143±1010 g
DW/m2 year. EBNPP averages included all roots to a depth of 30 cm due to the difficulty
in differentiating between herbaceous and woody fine roots.
Tree aboveground net primary productivity was similar between the two wetlands
(planted 1.25±0.23 kg DW/m2 year; unplanted 1.12±0.18 kg DW/m2 year) (ANOVA
p=0.696, F=0.177, df=1,4). Litterfall around the edge of the wetlands was also similar;
litterfall of the planted wetland was 528±42.8 g DW/m2 year, while litterfall of the
unplanted wetland was 572±41.5 g DW/m2 year (ANOVA p=0.861, F=0.032, df=1,34).
The majority of litterfall was from leaf litter, which made up 88% of litter around the
planted wetland and 80% of litter around the unplanted wetland. Woody material made
up the next largest portion, with 10% of litter around the planted wetland and 18% of
litter around the unplanted wetland. Reproductive litter made up only 2% of the total
litterfall for both wetlands (Fig 3.5). There were higher tree stem densities around the
planted wetland (planted 2008: 1.10±0.16 stems/m2 and 2009: 0.99±0.14 stems/m2;
unplanted 2008: 0.57±0.05 stems/m2 and 2009: 0.60±0.07 stems/m2)(ANOVA p<0.001,
F=15.99, df=1,22). Dominant tree species in the edges of both wetlands included Acer
saccharinum, Populus deltoides, and Salix nigra.
3.5 Discussion
3.5.1 Structure
Of the two wetlands, the planted wetland had higher species richness, higher
floristic quality, and a smaller amount of area occupied by invasive species. Initial
creation and restoration techniques, such as planting, can have a significant impact on
62
vegetation structure at a site (Reinartz and Warne 1993; Gutrich et al. 2009). Gutrich et
al. (2009) found that created wetlands in Ohio, where great care (i.e. planting and
contouring of basins) was taken during initial restoration efforts, have been shown to
have greater species richness and native species present than wetlands where less care
was given to restoration. By planting not only a species rich assemblage of vegetation,
but also including species with a variety of functional traits and niche requirements, it
may be possible to create resilient communities that are resistant to future disturbances
and invasion (Funk et al. 2008; Young et al. 2009).
Early colonization by invasive species is of concern regarding long-term
succession. Newly created ecosystems, i.e. highly disturbed systems, are more
susceptible to invasion than less disturbed systems due to the high availability of
resources, colonization opportunities, and open niches in these new ecosystems. Highly
invasive species have the potential to persist in an ecosystem through later successional
stages, which can interfere with vegetation succession and may lead to low diversity
within communities (Zedler 2000; Catford et al., 2012). One way to discourage invasive
species colonization is through planting (Middleton et al. 2010).
The number of wetland species and Community Diversity Index did not differ
between the two wetlands. The richness and evenness of vegetation communities was
similar in the two wetlands, even though the dominant species differed. When trying to
understand the function of a community, the exact species of a system and species
richness may not be as important to ecosystem processes as the functional composition
and traits of vegetation within the system (Diaz and Cabido 2001; McGill et al. 2006).
63
While two wetlands may have different species, as long as similar vegetation functional
composition and traits are present, it should be possible for the two wetlands to have
comparable function.
3.5.2 Function
Of the functional characteristics examined, aboveground primary productivity was
higher in the unplanted wetland, while some nutrient concentrations were higher in the
planted wetland. All other functional parameters, including belowground net primary
productivity and total net primary productivity, were similar between the planted and
unplanted wetland. Functional group makeup of the dominant species was also similar
between the two wetlands.
Both productivity and nutrient concentration are related to the species present
within the wetland. High productivity is not always considered to be a desirable
characteristic because it may be an indication that invasive species are present at a site.
In monocultures of Phragmites australis, ANPP can be as high as almost 2000 g DW/m2
year (Tanner 1996; Windham 2001), approximately double the productivity of the
wetlands in this study. However, high primary productivity, when coupled with slow
decomposition rates of organic matter, results in carbon being sequestered in the soil
(Hossler and Bouchard 2010). Carbon sequestration is a desirable wetland function that
aids in the reduction of greenhouse gases from the atmosphere, a function that should be
given consideration when constructing a wetland (NRC 2001). Both wetlands had high
total and belowground net primary productivity. While most soil carbon from vegetation
is the result of plant tissue decomposition, exudates from belowground plant material is
64
also an important source of carbon to the soil (De Deyn et al. 2008). Similar
belowground net primary productivity suggests that carbon exudates into the soil may be
similar in the two wetlands, while similar total net primary productivity suggests that soil
carbon from plant decomposition may also be similar in the two wetlands.
Another goal of created wetlands is often to reduce the nutrient content, primarily
N and P, of water passing through the wetlands. In the Midwest, N is of particular
concern due to widespread fertilizer application on agricultural fields. This results in
nutrient run-off from the fields, introducing a large amount of N into adjacent and
downstream water bodies (Mitsch et al. 2001: Mitsch and Day 2006). In the
experimental wetlands, there was no difference between the two wetlands for vegetation
N or P concentration nor was there any difference generally between N and P retention by
the wetlands (Mitsch et al. 2012), suggesting that planting does not have an effect on
plant N or P uptake.
Other elements are important in N and P cycles in wetlands and can effect their
removal rates from water. Availability of Fe in wetlands is essential in N2 fixation, the
process of converting atmospheric N2 to biologically available N, thus increasing N
within the system. Increased Fe availability has a positive effect on N2 fixation (Reddy
and DeLaune 2008). In contrast, Ca, Fe, and Al are capable of forming precipitates with
available phosphates in both the water and soil. These precipitates are insoluble and thus
biologically unavailable. The formation of precipitates is important in the sedimentation
and storage of P within the soil (Mitsch and Gosselink 2007). Since different species
have different nutrient uptake abilities (Tanner 1996; Jampeetong et al. 2012), if planting
65
is to occur, it may be beneficial to consider how nutrient uptake of the plant species may
alter levels of available nutrients within the system in order to maximize removal rates of
nutrients such as N and P.
3.5.3 Edge vegetation
Planting seems to have no effect on the structure and function of vegetation in the
edge area. From a successional standpoint, marsh habitat is not an endpoint system in
Ohio, only a stepping stone to swamp habitat. As is, the two edge areas seem to be on a
similar successional trajectory, which would suggest that when these wetlands transition
to swamp habitat they may have similar structural and functional characteristics.
However, future disturbances and the presence of ecosystem engineers (beavers and
muskrats) have the potential to alter current trajectories (Wright and Jones 2004; Walker
and del Moral 2008). Beaver herbivory is already occurring in the edge areas of the two
wetlands. Studies have shown that when ecosystem engineers alter productivity of a
system they also alter species richness (Wright and Jones 2004). The removal of trees
from the edge area opens habitat for both herbaceous and woody species, i.e. the
opportunity to increase species richness of the area while productivity has been
decreased. The duration and degree of beaver herbivory of both edge areas will likely
determine how the successional trajectories of the two wetlands will differ in the future.
3.5.4 Conclusions
The decision to plant a wetland may come down to the of goals of the creation or
restoration project. If vegetation structure is a priority, planting seems to be beneficial.
However, if vegetation function is a priority, planting is not a necessity where propagules
66
are readily available. Even when propagules are introduced to an ecosystem via natural
means, planting of a created wetland may still be a beneficial practice. Planting may help
to improve the structural vegetation characteristics of a site by increasing species
diversity and quality, which may help limit invasive species in the system. There is no
way to control the species entering the system, so while some propagules species may be
desirable, it is likely that invasive species are also entering the system. If space and
nutrients are already being utilized by vegetation, as opposed to a bare mud flat, there is
less chance that invasive species will be capable of becoming established in the system or
at the very least taking over the system. The best approach may be to plant as many
species as possible in the system and allow the wetland to “self-design”, instead of
implementing rigid planting protocols with specific spatial composition (Mitsch and
Wilson 1996).
While this experiment provides a unique opportunity to observe the effects of
planting on wetland vegetation development of two created wetlands where hydrologic,
nutrient and location variables are controlled for, it is important to note that only two
wetlands were studied. Both wetlands are riverine, surface flow wetlands with
unidirectional flow, so extrapolating the results to other wetland types or similar wetlands
in different landscapes should be done with caution.
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74
Table 3.1 Average ± standard error of structural and functional characteristics of the two
experimental wetlands and edge area. - indicates sampling did not occur for that year.
Wetland 1 (W1) was planted in 1994 and Wetland 2 (W2) was left unplanted. ANOVA
was used to examine significant differences. Pseudoreplication was an issue with this
study. Each structural and annual functional characteristic is represented by one number
per wetland per year. Significant p-values are in bold.
2008
2009
2010
P-values
W1
W2
W1
W2
W1
W2
Wetland
Year
97
92
98
92
99
95
0.019
0.102
FQAI
23.8
19.9
23.2
20
23.2
19.9
0.002
0.242
CDI
1.71
1.26
1.16
1.45
1.45
1.03
0.512
0.50
55
52
52
46
51
49
0.235
0.319
Aboveground NPP
(g DW/m2 year)
712
±102
796
±99
703
±98
812
±20
673
±87
866
±108
0.006
0.34
Belowground NPP
(g DW/m2 year)
2170
±303
2362
±336
2163
±312
2132
±408
1832
±307
2175
±334
0.234
0.165
Total NPP
(g DW/m2 year)
2882
±334
3158
±357
2865
±323
2944
±391
2505
±462
3041
±384
0.113
0.207
Edge herbaceous
aboveground NPP
(g DW/m2 year)
117
±5
105
±6
79
±14
97
±15
-
-
0.368
0.875
Edge belowground
NPP
(g DW/m2 year)
2200
±434
1004
±572
3153
±181
1133
±396
-
-
0.188
0.205
Litterfall
(g DW/m² year)
-
-
528
±42
572
±41
-
-
0.860
-
Tree aboveground
NPP
(g DW/m2 year)
-
-
1250
±230
1120
±180
-
-
0.696
-
Species richness
# macrophytes
75
Table 3.2 Functional classification and percent cover of the dominant plant communities
in the planted and unplanted wetlands. * denotes invasive species.
% Cover 2008
% Cover 2009
% Cover 2010
Functional
Classification
Planted
Unplanted
Planted
Unplanted
Planted
Unplanted
Interstitial
0.5%
29%
0%
6.7%
0%
0%
Phragmites
australlis *
Matrix
0%
1.8%
0%
2.1%
0%
3.4%
Schenoplectus
tabernaemontani
Matrix
2%
2.9%
0%
13.2%
3%
9%
Matrix
3%
0%
4%
0%
3.5%
0%
Sparganium
eurycarpum
Matrix
19%
0%
18%
0%
16.4%
0%
Typha spp.
Matrix
27%
33%
45%
34%
40%
55%
Species
Leersia orizoides
Scirpus fluviatilis
76
Table 3.3 Average nutrient content of aboveground biomass from August 2010. Nutrient
content is shown for each dominant species, as well as each wetland. (N) indicates
number of samples.
Average mg/g DW ± SE
N
P
K
Ca
Al
15.4
2
20.1
6
0.3
±1.1
±0.27
±2.0
±0.45
±0.10
13.5
1.4
15.5
11.8
0.2
Typha spp. (19)
±1.0
±0.11
±1.2
±0.87
±0.05
Leersia oryzoides
13.5
1.8
9.7
3.5
0.4
(22)
±0.6
±0.09
±0.6
±0.15
±0.08
15.2
3
15.9
5.3
0.3
Eleocharis obtusa (2) ±0.1
±0.21
±1.2
±0.40
±0.03
Sparganium
15.2
2.1
20.8
13.8
0.6
eurycarpum* (9)
±1.3
±0.34
±1.6
±0.69
±0.37
Sciprus fluviatilis*
12.8
1.7
18.7
2.5
0.4
(6)
±2.8
±0.36
±2.1
±0.36
±0.08
Sagittaria latifolia*
19.6
4.9
39.8
6.9
0.7
(5)
±0.7
±0.97
±4.5
±1.20
±0.25
Nelumbo lutea* (1)
22.9
3.1
27.8
12.4
0.1
Phragmites
13.4
1.1
7
3.2
0.01
australis† (3)
±1.9
±0.16
±0.3
±0.29
±<0.001
14.3
2.1
19.5
8.3
0.5
Planted (42)
±0.7
±0.22
±1.6
±0.73
±0.10
14.7
1.9
13.6
6.2
0.2
Unplanted (37)
±0.6
±0.11
±1.1
±0.72
±0.03
*Species present only in planted wetland
†Species present only in unplanted wetland
Species (N)
Schoenoplectus
tabernaemontani (11)
77
Fe
0.37
±0.08
0.23
±0.04
0.39
±0.07
0.56
±0.05
0.62
±0.32
0.38
±0.07
0.65
±0.22
0.14
0.05
±0.004
0.48
±0.08
0.26
±0.03
Mn
0.21
± 0.021
0.17
±0.008
0.1
±0.009
0.33
±0.005
0.21
±0.018
0.16
±0.036
0.1
±0.032
0.24
0.16
±0.019
0.16
±0.010
0.16
±0.013
Na
0.9
±0.13
3.5
±0.40
0.1
±0.01
0.6
±0.29
1
±0.13
0.8
±0.19
0.9
±0.36
1.5
0.1
±0.01
1.2
±0.15
1.4
±0.34
Figure 3.1 This study was conducted in the two 1-ha experimental wetlands at the
Olentangy River Wetland Research Park. The western basin was planted with 2500
individual propagules representing 13 wetland species, while the eastern basin was left to
colonize naturally.
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Figure 3.2 Monthly weighted aboveground biomass of the two wetlands with standard
error. Overall, the unplanted wetland was more productive than the planted wetland.
Peak biomass occurred in July of all three years for the planted wetland, while peak
biomass occurred in July 2008/2009 and August 2010 for the unplanted wetland. Peak
biomass is a rough approximation of annual aboveground net primary productivity.
79
Figure 3.3 Accumulation of monthly weighted aboveground net primary productivity of
the wetlands from 2008 to 2010. Accumulation was highest from April to May for all six
sample events. After July, for all sampling events except W2-2010, productivity
accumulation switched to senescence. W# indicates the wetland and -## indicates the
year sampled. Wetland 1 (W1) was planted and Wetland 2 (W2) was unplanted.
80
Figure 3.4 Monthly belowground biomass of the two wetlands with standard error bars.
Belowground net primary productivity (BNPP) was estimated by subtracting April
measurements from September measurements. There was a continuous overwinter
rootstock ranging from 1000 to 1500 g DW/m2 year as evident by the April
measurements. BNPP ranged from 1800 to 2400 g DW/m2 year.
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Figure 3.5 Tree aboveground net primary productivity (ANPP) and standard error of the
interior edge of each wetland. Equations from Jenkins et al. (2003) were used to estimate
tree ANPP. Litter traps were used to determine the amount of foliage and reproductive
material contributing to tree ANPP. There was no difference in tree ANPP or litterfall
between the two wetlands.
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Chapter 4: Metabolism and methane flux of dominant macrophyte communities in
created riverine wetlands using open system flow through chambers
4.1 Abstract
Estimating net primary productivity of macrophytes is a common practice in
wetland research, but much less is done regarding estimating gross primary productivity
(GPP) and respiration (R) of wetland macrophyte communities. The purpose of this
project was to estimate both the metabolism (GPP and R) and greenhouse gas emissions
(methane) of selected wetland macrophyte communities using an open system flowthrough chamber technique to determine the gaseous carbon budget of different plant
communities. Large (0.5 m2 sample area, 1.6 and 2.6 m tall) flow through chambers were
placed over dominant macrophytes species (2010: Typha spp., Scirpus fluviatilis,
Sparganium eurycarpum, and Phragmites australis; 2011: Typha spp., Scirpus fluviatilis,
Phragmites australis, and open water) in the two experimental wetlands at the Olentangy
River Wetland Research Park in central Ohio, USA. Gas samples were collected over a
48-hr period monthly from April through September. Samples were collected using a vial
and syringe method from the chambers every odd hour between sunrise and sunset to
estimate photosynthesis, along with two nightly samples to estimate respiration. Overall
metabolism measurements were similar in the two years: 2010, GPP = 13.9 ± 1.2 g CO2C/m2 day; R
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= 12.1 ± 1.0 g CO2-C/m2 day; 2011, GPP = 13.9 ± 1.1 g CO2-C/m2 day; R = 12.9 ± 0.6 g
CO2-C/m2 day. GPP peaked in June 2010 and in July 2011 and overall was approximately
3.7% of solar radiation. GPP differed by both month sampled and plant community
(p<0.001 and p=0.002, respectively). Phragmites australis and Typha spp. had higher
average GPP than did open water and Phragmites australis had higher GPP than Scirpus
fluviatilis. Median methane emissions from the sample plots were 12.8 mg CH4-C/m2
day and differed by month (p<0.001) and soil temperature (p=0.049). There was a net
retention of carbon in the two experimental wetlands ranging from 160 to 195 g C/m2
year in 2010 and from 164 to 171 g C/m2 year in 2011.
4.2 Introduction
The gaseous carbon budget of a wetland depends on the amount of carbon taken
up as carbon dioxide during photosynthesis and the amount of carbon lost as either
carbon dioxide from plant and soil respiration or as methane emitted by methanogenesis
(Whiting and Chanton 2001; Cornell et al 2007; Yvon-Durocher et al. 2007). The overall
limit of heterotrophic activity within an ecosystem is established mainly by the amount of
carbon acquired by a system through gross primary productivity (Cornell et al. 2007).
Gross primary productivity is dependent on the plant species present, available
solar radiation, available water and nutrients, and temperature (Running et al. 2000).
Many species have an optimum temperature for photosynthesis that tends to be near the
mean daytime temperature of an ecosystem. If temperature is drastically different from
the optimum, either low or high, gross primary productivity measurements will be low.
84
Limitation in available solar radiation will also lead to low relative gross primary
productivity. Respiration, unlike gross primary productivity, tends to increase
exponentially with temperature, so under high temperature conditions it is possible for
respiration to exceed gross primary productivity (Aber and Milillo 2001; Yvon-Durocher
et al 2011).
Methanogensis occurs under reduced, anaerobic condition by methanogen
bacteria. These conditions are likely to form under prolonged periods of hydrologic
inundation within wetland ecosystems (Mitsch and Gosselink 2007; Altor and Mitsch
2008). Methanogens utilize organic matter within a system to produce methane (Mitsch
and Gosselink 2007). Methane emissions can be reduced by methanotrophic bacteria that
oxidize methane within the soil and water, while producing carbon dioxide (Segers 1998;
Mitsch and Gosselink 2007; Altor and Mitsch 2008). Methanogenesis is likely linked to
soil temperature, either directly or in combination with other environmental factors
(Schutz et al. 1990; Gedney et al. 2004). A positive correlation has been found between
soil temperature and methane emissions in temperate freshwater wetlands (Altor and
Mitsch 2008; Nahlik and Mitsch 2010; Sha et al. 2011). Soil carbon content and
hydrologic gradient (slope of the water table) have also been found to have a positive
relationship with methane emissions (Altor and Mitsch 2008; Koh et al. 2009; Sha et al.
2011).
Gross primary productivity may influence the amount of methane production
within an ecosystem (Joabsson et al. 1999), with up to 15% of carbon fixed possibly
being released as methane (Brix et al. 2001). Root carbon exudates and litter production
85
act as a source of organic matter for methanogen bacteria (Chanton et al 1995; Whalen
2005; Joabsson et al. 1999). High primary productivity leads to an increase in root
exudates and litter production of a system, thereby increasing the amount of raw material
for methanogens to utilize. However, oxygen released from roots can decrease methane
emissions by facilitating methanotrophy (Whalen 2005).
Due to the anaerobic conditions found in wetlands, methane emissions compared
to other types of ecosystems tend to be somewhat high, with wetlands and areas with
very high water tables being methane sources while most dry ecosystems are methane
sinks (Smith et al 2000; Whiting and Chanton 2001). Wetland methane emissions
account for approximately 20 – 25% of global methane emissions, with the remainder
coming from anthropogenic sources (Mitsch and Gosselink 2007; Nahlik and Mitsch
2011). The balance of carbon released and carbon taken up by the system will determine
if a wetland is acting as a sink or a source of carbon (Whiting and Chanton 2001).
Understanding carbon flux in terms of both carbon dioxide and methane is of
particular importance due to the role these gases play in global climate change. Of these
two gases, methane is considered to be of great concern since it has a global warming
potential of 25 compared to carbon dioxide's global warming potential of 1 (IPCC 2007).
Thus, small changes in levels of methane will have a greater impact on global climate
change than small changes in level of carbon dioxide. There has been debate as to the
role of wetlands as carbon sinks or sources. It has been suggested that in freshwater
wetlands in North America, wetland carbon sequestration is offset by the amount of
methane produced (Bridgham et al 2006). Other studies have suggested that over the
86
long term (100 to 500 year time frame), wetlands act as carbon sinks and thus are capable
of alleviating the effects of global climate change regardless of whether they emit
methane (Brix et al 2001; Whiting and Chanton 2001; Mitsch et al. in final review).
Newly created and restored wetlands are likely to act as carbon sinks, due to the rate of
organic soil carbon increase, compared to natural wetlands, outweighing lower rates of
methane production (Badiou et al. 2011).
Measurements of gross primary productivity, respiration, and methane emissions
can be done on many different scales, including individual leaf analysis, chambers, large
scale enclosures covering a portion of an ecosystem, and eddy flux towers (Ruimy et al.
1995). With chamber methods, these can either be open or closed systems, both of which
have their pros and cons. Open system chambers can be used for extended periods of
time, but require controlled air movement through the chamber to maintain chamber
temperature within range of ambient (Drake and Read 1981; Streever et al 1998). Closed
system chamber are simple, non-mechanical structures, but can only remain in place for a
short period of time due to increased temperature within the chamber which can alter
ecosystem processes (Streever et al 1998).
This paper describes gross primary productivity and respiration of the dominant
macrophyte communities in 18-year-old created marshes in central Ohio, as well as
methane emissions from those communities, using open system flow through chambers
over the course of the growing season. It is hypothesized that there will be a significant
difference between the planted and unplanted dominant species in the two wetlands, with
the unplanted naturally occurring species having higher gross primary productivity and
87
respiration rates than the planted species in the wetlands due to the higher net primary
productivity of the unplanted dominant species compared to the planted dominant
species. It is also hypothesized that there will be an overall net retention of carbon in the
wetland communities, i.e. the amount of carbon taken up through gross primary
productivity will be larger than the amount of carbon lost through respiration and
methane emissions.
4.3 Methods
4.3.1 Study Site
The two experimental wetlands at the Olentangy River Wetland Research Park,
Ohio State University, Columbus, Ohio were used to examine ecosystem function of
created riverine wetlands. The two experimental wetlands are each one ha in size and
receive the majority of their water inputs via pumps from the Olentangy River which runs
along the north and east boarder of the 52-acre facility. Construction began in 1993 and
water began flowing into the wetlands in March 1994. In the spring of 1994, an initial
vegetation succession experiment was implemented in which one wetland (Wetland 1)
was planted with 13 wetland species, while vegetation was allowed to colonize Wetland 2
naturally (Mitsch et al. 1998, 2012). The dominant plant communities in these two
wetlands include Typha spp. (Typha latifolia, Typha angustifolia, and Typha X glauca),
Sparganium eurycarpum, Scirpus fluviatilis, and Phragmites australis. Sparganium
eurycarpum and Scirpus fluviatilis were both planted in Wetland 1 during initial
construction. Typha spp. and Phragmites australis colonized the wetlands naturally.
88
Phragmites australis is only present in the unplanted Wetland 2. All four species are
perennials.
4.3.2 Chamber Design
Metabolism of the herbaceous vegetation was measured for the major plant
communities for each of the experimental wetlands at the ORW on a monthly basis from
April – September of 2010 and 2011. Chambers were set up in four dominant plant
communities in 2010 and set up in three dominant plant communities and one open water
site in 2011. Each community was monitored with two chambers per month, which were
run for approximately 48 hours at a time. Sampling occurred every odd hour between
sunrise and sunset to determine GPP, while sampling to measure R occurred once a night
(after sunset) in 2010 and twice during the night (after sunset and before sunrise) in 2011.
Each chamber consisted of a plastic bag fitted over a PVC pipe frame that sat on a
wood and plexi-glass base (Fig. 4.1). Chamber bases covered a 0.5 m2 area of soil and
were pushed 10 cm into the soil surface. The plastic bag and PVC pipe frame were
sealed to the chamber base using all-weather tape. Two different frame heights were
used, 1.4 m and 2.4 m, depending on the height of the vegetation to be measured. Each
chamber had an inflow pipe near the base of the chamber and an outflow pipe coming out
of the top of the chamber. The inflow pipe was made of pvc pipe and semi-rigid
aluminum pipe. The 1.83 m pvc pipe had a fan with a range of 40 to 250 cfm air flow at
one end, a pitot tube near the middle, and a semi-rigid aluminum pipe at the other end
that connected to the chamber. A manometer was connected to the pitot tube to measure
air flow through the chamber. Sampling ports were located on the inflow pvc pipe and
89
the outflow pipe (Fig. 4.1). The fan was used to force air through the chamber (Oechel
et al. 2000). A 0.7-m outflow pipe was positioned at the top of the chamber. In 2010, the
outflow pipe was positioned horizontally at the top of the chamber, but in 2011 was
moved to a vertical position to help reduce heat build up in the chamber. Air flow was
maintained at a rate to keep the air in the chamber within approximately 2 ºC of the
ambient temperature since temperature affects vegetation metabolism (Drake and Read
1981; Rasse et al. 2002) yet not so rapid as to eliminate any change in gas concentrations
from inflow to outflow.
4.3.3 Sampling
Gas samples were collect from the inflow and outflow sampling ports using a vial
and syringe method. Thirty ml of air was taken at each sampling event from both the
inflow and outflow pipes and stored in 10 ml glass vials. The vials were stored in a dark,
room temperature cabinet until they could be analyzed. Previous studies have seen CO2
concentrations in the range of 200 to 500 ppm or µl/l (Drake and Read 1981; Rasse et al.
2002). A Shimadzo GC-2014 gas chromatograph, set up to run greenhouse gases (CO2,
CH4, and N2O) was used to analyze samples. Additionally, temperature (chamber,
ambient, soil, and water), photosynthetically active radiation (PAR) (μmol/m2 s), and total
solar radiation (W/m2) were measured at time of sampling. PAR was measured with a LICOR LI-190 quantum sensor and solar radiation was measured with a LI-COR LI-200
pyranometer. Both sensors were connected to a hand held LI-COR LI-250A Light meter
with a digital display. Total solar radiation measurements from the hand held reader were
compared to a permanently installed pyranometer at the weather station located between
90
the two wetlands.
Change in carbon for each sampling period was determined by subtracting the
inflow CO2 concentrations from the outflow CO2 concentrations. The ideal gas equation
was used to convert from ppm CO2 to mg CO2-C/m3. These values were then multiplied
by the rate of air flow through the chamber and then divided by the land surface area (0.5
m2) within the chamber to find the rate of CO2-C assimilated or respired (see Appendix E
for equations). Rates were then weighted by time and adjusted for respiration. These
rates were then summed over the course of a day to determine daily GPP for each
chamber (Aber and Melillo 2001). Respiration measurements were determined by
averaging night readings. These numbers were used to determine the amount of CO2
assimilation/respiration for each major plant community. Methane emission rates were
found using the same method for determining GPP and respiration. In the 2011 growing
season, the increase in water vapor from inflow to outflow was estimated to be <1 to 8%
based on evapotranspiration estimates. Since there was minimal difference between
inflow and outflow, humidity was not taken into account for the calculations of GPP,
respiration, and methane emissions.
Methane and respiration values were subtracted from GPP measurements to
determine net ecosystem exchange of gaseous carbon (NEEC) and to determine if there is
a net gain or loss in carbon within the sample plots.
4.3.4 Statistical Analysis
The statistical software program R was used to test assumptions and perform the
statistical tests (determined assumptions were met using D’agostino skewness test,
91
Anscomb-Glynn kurtosis test, Shapiro-Wilks normality test all at α= 0.1, normal quantilequantile plots, and scatterplots) (The R Foundation for Statistical Computing 2009).
ANOVA was used to examine GPP and respiration measurements by year, month,
species, and other environmental variables. Tukey multiple comparison of means test
was used to examine differences between months and between species. Linear regression
was used to determine if the relationship between the environmental variables and the
dependent variables was positive or negative. Methane data was not normal and could
not be normalized using standard transformations (data exhibited a positive skew). Since
the data could not be normalized, the non-parametric Kruskal-Wallis test was used. A
multiple comparison test was then used to examine differences between months. To
alleviate issues relating to pseudoreplication, one point for GPP, R, and methane
emissions was calculated per chamber per month. Statistical tables can be found in
Appendix F.
4.4 Results
Solar radiation averaged 3462 ± 140 kcal/m2 day in 2010 (at the weather station)
and 4172 ± 170 kcal/m2 day in 2011 (at the study plots) over the course of the growing
season. Average ambient temperature around the chambers was 23.1 ± 0.32 ºC and 23.9
± 0.25 ºC for 2010 and 2011, respectively. In 2011, average water temperature and soil
temperature were 22.0 ± 0.20 ºC and 20.8 ± 0.14 ºC. Water depth was approximately
38.10 ± 0.64 cm in the wetlands (Table 4.1).
GPP and respiration over the course of the growing was not different between the
two years (ANOVA p=0.997, F=0, df=1,38 and p=0.496 F=0.472, df=1,38) (Table 4.2).
92
Average GPP for 2010 was 13.93 ± 1.17g CO2-C/m2 day, while respiration was 12.08 ±
1.02 g CO2-C/m2 day. Average GPP for 2011 was 13.95 ± 1.08 g CO2-C/m2 day, while
respiration was 12.86 ± 0.62 g CO2-C/m2 day. GPP was approximately 4.1% of solar
radiation for Phragmites australis, 3.8% for Typha spp., 3.4% for Sparganium
eurycarpum, 2.9% for Scirpus fluviatilis, and 1.9% for open water. GPP differed by both
month and species (ANOVA p<0.001, F=6.364, df=5,34 and ANOVA p=0.003,F=4.965,
df=1,35 respectively), while respiration differed by month (p=0.026) (Fig. 4.3). GPP
peaked in June of 2010 and July of 2011. Comparing GPP to the environmental
variables, there was a significant difference in GPP based on solar radiation (Linear
regression p<0.001, F=15.71, df=1,38), ambient temperature (Linear regression p=0.002,
F=11.69, df=1,32), and soil temperature (Linear regression p=0.001, F=13.02, df=1,32).
There was a positive relationship between GPP and the three significant environmental
variables. The only environmental variable that had a significant effect on respiration
was ambient temperature (Linear regression p=0.038, F=4.71, df=1,32). Respiration
increased with increasing ambient temperature.
GPP was significantly higher for Phragmites australis (17.31 ± 1.43 g CO2-C/m2
day) and Typha spp. (16.08 ± 1.27 g CO2-C/m2 day) than for open water (9.85 ± 1.68 g
CO2-C/m2 day) (Tukey Multiple Comparison of Means p=0.012, difference=7.40,
lower=1.26, upper=13.53; and p=0.050 difference=6.23, lower=-0.015, and upper=12.47,
respectively). Phragmites australis GPP was also significantly higher than Scirpus
fluviatilis GPP (11.39 ± 0.98 g CO2-C/m2 day) (Tukey Multiple Comparison of Means
p=0.029, difference=-5.86, lower=-11.29, and upper=-0.42). There was no difference
93
between Sparganium eurycarpum (11.37 ± 2.00 g CO2-C/m2 day) and any of the other
species. Carbon use efficiency, the ratio of net primary productivity to gross primary
productivity, is particularly high for Phragmites australis at 0.75. Carbon use efficiency
for the remaining species was 0.47 for Typha spp., 0.37 for Sparganium eurycarpum, and
0.34 for Scirpus fluviatilis. Average carbon use efficiency of the four species was 0.56.
Comparing GPP by month, June was statistical different from April (Tukey
Multiple Comparison of Means p=0.0075, difference=9.35, lower=1.87, and
upper=16.83) and September (Tukey Multiple Comparison of Means p=0.036,
difference=-6.99, lower=-13.68, and upper=-0.30), while July was statistical different
from April (Tukey Multiple Comparison of Means p=0.002, difference=10.12,
lower=3.03, and upper=17.22), May (Tukey Multiple Comparison of Means p=0.043,
difference=-5.91, lower=-11.70, and upper=-0.11), and September (Tukey Multiple
Comparison of Means p=0.008, difference=-7.76, lower=-14.02, and upper=-1.51). As
for respiration, May was statistically different than July (Tukey Multiple Comparison of
Means p=0.05, difference=4.54, lower=-0.12, and upper=9.20).
Median methane measurements for 2010 and 2011 were 10.76 mg CH4-C/m2 hr
(min = -4.22 and max = 49.94mg CH4-C/m2 hr) and 14.96 mg CH4-C/m2 hr (min = -4.12
and max = 70.93 mg CH4-C/m2 hr). Methane measurements differed by month sampled
(Kruskal-Wallis p<0.001, chi2=21.349, df=5), but did not differ by species or year
sampled (p=0.3189 and Kruskal-Wallis p=0.123, chi2=2.3775, df=1) (Fig. 4.4). June was
statistically different from April (Multiple Comparison Test after Kruskal-Wallis = True;
Wilcoxon rank sum test with continuity correction p<0.001, w=5, 95% CI -28.88 to
94
-10.53) and September (Multiple Comparison Test after Kruskal-Wallis = True;
Wilcoxon rank sum test with continuity correction p=0.004, w=213, 95% CI 6.11 to
21.98), while July was statistically different from April (Multiple Comparison Test after
Kruskal-Wallis = True; Wilcoxon rank sum test with continuity correction p=0.001,
w=16, 95% CI -36.16 to -6.60). Water depth and soil temperature produced significant
differences in methane emissions (Kruskal-Wallis p=0.022, chi2=44.87, df=28 and
Kruskal-Wallace p=0.049, F=48.732, df=34).
Taking into account carbon losses from both respiration and methane emissions at
the four vegetation communities and open water site, carbon uptake from GPP averaged
13.9 g C/m2 day over the two growing seasons, while carbon release from respiration was
12.5 g C/m2 day and from methane emissions was 0.3 g C/m2 day. NEEC was 1.1 g C/m2
day or approximately 200 g C/m2 over the growing season from April through
September. Examining each community, average NEEc was 3.5 g C/m2 day for Typha
spp., 3.9 g C/m2 day for Phragmites australis, 1.01 g C/m2 day for Sparganium
eurycarpum, -0.22 g C/m2 day for Scirpus fluviatilis, and -2.5 g C/m2 day for open water.
Extrapolating to the two experimental wetlands by weighting GPP, respiration, and
methane measurements by community area (see Appendix D for vegetation community
maps for 2010 and 2011), NEEc for the planted wetland was 160 g C/m2 year in 2010 and
164 g C/m2 year in 2011. NEEc for the unplanted wetland was 195 g C/m2 year in 2010
and 171 g C/m2 year in 2011.
95
4.5 Discussion
4.5.1 Gross Primary Production & Respiration
As hypothesized, the two unplanted species, Phragmites australis and Typha spp.,
had higher gross primary productivity rates than one of the planted species, Scirpus
fluviatilis. However, there was no difference between the two unplanted species and the
other dominant planted species, Sparganium eurycarpum. These differences and lack
there of are likely due to the physical and physiological traits of the species (i.e. height,
area, and biomass of the vegetation). In 2010, total (above and belowground) net primary
productivity of the four species sampled were 4300 g DW/m2 year and 3000 g DW/m2
year for the naturally colonizing vegetation P. australis and Typha spp. respectively, and
1600 g DW S. eurycarpum/m2 year, and 1000 g DW S. fluviatilis/m2 year for the planted
vegetation S. eurycarpum and S. fluviatilis, respectively. Carbon use efficiency was
somewhat higher in the unplanted species (Phragmites australis and Typha spp.) than the
planted species. Average carbon use efficiency of the four species was 0.56, which is
near carbon use efficiencies of 0.60-0.65 found in Typha marshes (Bonneville et al.
2008; Rocha and Goulden 2009). The high carbon use efficiency of Phramites australis
likely plays a role in the ability of this invasive to out-compete native species. Invasives
have been found to have higher resource-use efficiency rates (carbon assimilation per unit
of resource) than native species in some ecosystems (Funk and Vitousek 2007).
Cornell et al. (2007), who used static chambers to examine GPP and respiration
over a chronosequence in created salt marshes compared to natural marshes, found
macrophyte GPP and respiration rates similar to what was found in this study. GPP at
96
Cornell et al.'s sites ranged from approximately 10-24 umol CO2/s m2 (5-13 g C/m2 day
assuming 12-hr day)and respiration rates of 5-17 umol CO2/s m2 (5-18 g C/m2 day). GPP
in this study averaged 13.9 g C/m2 day and respiration of 12.5 g C/m2 day. A study of a
Typha marsh in California from 1999-2007 using an eddy covariance tower found
average GPP measurements of 1428 g C/m2 year where macrophyte average total net
primary productivity (NPP) was 867 g C/m2 year (Rocha and Goulden 2009). The plots
in this study had average GPP of approximately 2340 g C/m2 year and average NPP of
1045 g C/ m2 year, higher that what was found in the study by Rocha and Goulden
(2009). Another study of temperate Typha marshes examining net ecosystem exchange
found average monthly uptake values ranging from 0.1 to 5.1 g C/m2 day from June
through September (Bonneville et al. 2008), with the average numbers for this study from
all plots and most of the macrophyte communities falling within this range.
The productivity and respiration measured over open water in this study (9.85 g
C/m2 day GPP and 12 g C/m2 day R) were approximately ten times those measured and
estimated by Tuttle et al (2007) (GPP averaged 0.54 g C/m2 day 2004, 1.08 g C/m2 day
2005; R averaged 0.54 g C/m2 day 2004 and 1.11 g C/m2 day 2005) by a dissolved
oxygen diurnal method in the same wetlands six years previously. However, Tuttle at al.
(2007) measurements were from April through June of each year, so peak production was
not included in these averages. Another issue is that samples from the first year of the
Tuttle et a. study occurred after pulsing hydrologic conditions (as opposed to stead flow
conditions; can result in large water inflow rates due to storm events), which can decrease
the productivity of the system through washout of algae and plankton (Altor and Mitsch
97
2008). Some of the discrepancy between studies may also be due to the method of
measurement. With open system chambers, the temperature should be kept within ±2°C
to minimize changes in the rates of photosynthesis and respiration within the chamber.
The flow of air through the chamber can also alter rates of photosynthesis and respiration
due to pressure gradients and if the air is moving through the chamber too quickly it may
be difficult to detect differences between inflow and outflow readings (Drake and Read
1981; Streever et al. 1998; Dash and Dash 2009). When using oxygen sensors in the
water column to measure productivity, the diffusion of oxygen out of the water column
can cause an underestimate of gross primary productivity (Dash and Dash 2009). A lab
study of different methods of measuring GPP and R from microalgae in sediments found
that this method grossly underestimated GPP, primarily under higher light intensity
conditions where oxygen would bubble out of the sediment and water column (Revsbech
et al. 1981). Additional problems with bubbling can occur when sediments are disturbed.
4.5.2 Carbon Balance and Global Climate Change
Examining the second hypothesis, there was an overall uptake of carbon of
approximately 160 to 164 g C/m2 year for the planted wetland and 195 to 171 g C/m2 year
for the unplanted wetland in 2010 and 2011, suggesting that these sites are acting as
carbon sinks. Soil carbon sequestration rates measured in the two wetlands were 181193 g C/m2 year (with organic carbon accounting for 153-166 g C/m2 year) 10-years after
creation and 219-266 g C/m2 year 15-years after creation, suggesting that this study's
results with mesocosm chambers are reasonably accurate (Anderson and Mitsch 2006;
Mitsch et al. 2012). Both of these wetlands were acting as carbon sinks. Examining the
98
ratio of the rate of carbon sequestration to the rate of methane emissions, however, a
simple calculation shows that the planted wetland is acting as a sink of climate radiative
forcing and the unplanted wetland is acting as a source of climate radiative forcing
(Mitsch et al. 2012). When the decay of methane is accounted in the atmosphere, both
wetlands were shown to be net radiative sinks (Mitsch et al. in press). A study of a
natural wetland in Ohio showed lower carbon sequestration rates, with an average of 140
± 16 g C/m2 year in riverine wetlands (Bernal and Mitsch 2012).
Whiting and Chanton (2001) and Mitsch et al. (in press) have found that the
wetlands they studied acted as carbon sinks, but taking into account the greenhouse
warming potential of CO2 and CH4, the wetlands were still contributing to the greenhouse
effect. However, it is predicted that over the long term (approximately 100 to 500 years
for Whiting and Chanton 2001; 300 years or less for Mitsch et al. in press), the wetlands
will act as carbon sinks and will mitigating the greenhouse effect. Future increases in
temperature are likely to have an impact on the balance of gaseous carbon emissions
within wetlands and may even turn previous carbon sinks into carbon sources. YvonDurocher et al. (2011) found that with an increase in 4 ºC, the fraction of gross primary
productivity being released as CH4 increased by approximately 20%.
In the short term, wetland creation may be a means of mitigating global climate
change (Burkett and Kusler 2000; Badiou et al. 2011). Created wetlands have low soil
organic matter after creation, compared to natural wetlands (Hossler and Bouchard 2010).
The two experimental wetlands at the ORW have seen an increase in soil carbon from 16
g/kg of soil in 1993 pre-wetland creation to 36 g/kg of soil in the planted wetland and 46
99
g/kg of soil in the unplanted wetland in 2009 within the upper soil layers (Mitsch et al.
2012). As time passes, soil organic matter increases, with soil development proceeding
faster under increased inundation (Craft et al. 2002). Carbon sequestration rates in newly
created wetlands have been shown to be higher than natural wetlands (Mitra et al. 2005;
Euliss et al. 2006; Mitsch et al 2012). Badiou et al. (2011) suggests that newly created
prairie pothole wetlands may act as carbon sinks even when accounting for methane
emissions due to the high rate of carbon sequestration seen after wetland creation.
The type of wetland created will likely determine the ability of the wetland to
sequester carbon. Peatlands exhibit slow carbon sequestration after creation and show
net emissions of greenhouse gases due to the rate of decomposition of new peat, while
coastal wetlands are likely to have high rates of carbon sequestration due to the constant
burial of sediments (Zedler and Kercher 2005) and almost no methane emissions. The
carbon balance in temperate freshwater wetlands likely falls somewhere between that of
peatlands and coastal marshes due to both high carbon sequestration and methane
emissions in temperate freshwater wetlands.
When creating or restoring a wetland, it may be possible to enhance the ability of
a wetland to act as a carbon sink through a combination of controlling the amount of
open water and species present. Since there was no difference in methane emissions
based on plots, yet there was higher gross primary productivity rates for plots with
species that had high net primary productivity, a wetland composed of highly productive
species will likely have higher amounts of overall carbon uptake. Additionally, open
water plots had lower gross primary productivity rates, yet similar rates of daily
100
respiration compared to the vegetated plots, so reducing the amount of open water in a
wetland may also increase overall carbon uptake. There was little variability in
hydrology during this study, so differences in gross primary productivity, respiration, and
methane emissions relating to hydrology was not examined. Future studies examining
the effects of variations in water depth and flow rate through the wetland on carbon
uptake and emissions may provide valuable insights into ecosystem processes in a wider
range of wetlands, as well as under future climatic conditions that may have altered
precipitation rates due to global climate change.
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Table 4.1 Monthly average ± SE of environment variables over the course of the growing
season in 2010 and 2011.
Year Month
Solar
radiation
(kcal/m2 day)
Ambient
temperature
(°C)
Water
temperature
(°C)
Soil
temperature
(°C)
Water depth
(cm)
2010
May
3036 ± 420
21.7 ± 0.45
-
-
-
June
4604 ± 224
27.3 ± 0.49
-
-
-
July
3776 ± 167
30.5 ± 0.37
26.0 ± 0.31
24.8 ± 0.30
35.26 ± 0.12
August
3673 ± 117
22.6 ± 0.58
19.5 ± 0.31
20.4 ± 0.21
34.90 ± 0.02
September
2218 ± 186
12.4 ± 0.51
12.1 ± 0.47
13.6 ± 0.18
32.34 ± 0.44
April
2356 ± 415
15.8 ± 0.46
13.3 ± 0.28
13.9 ± 0.09
43.43 ± 0.89
May
4952 ± 274
25.4 ± 0.51
21.3 ± 0.33
18.9 ± 0.18
42.36 ± 0.63
June
4967 ± 430
25.4 ± 0.46
24.3 ± 0.32
22.5 ± 0.11
40.64 ± 3.62
July
5051 ± 270
30.3 ± 0.40
29.0 ± 0.23
26.7 ± 0.04
35.90 ± 0.05
August
4830 ± 153
25.9 ± 0.54
24.3 ± 0.26
23.1 ± 0.11
31.35 ± 0.15
September
2876 ± 262
20.1 ± 0.56
19.8 ± 0.29
20.1 ± 0.15
34.63 ± 0.23
2011
107
Table 4.2 p-values from ANOVA, linear regression models, and Kruskal-Wallis nonparametric test for the dependent and environmental variables. Significant p-values are in
bold.
Dependent
variable
Year
Month
Plant
species
Solar
radiation
Ambient
temperature
Soil
temperature
Water
depth
Gross
Primary
Productivity
0.997
<0.001
0.003
<0.001
0.002
0.001
0.408
Respiration
0.496
0.026
0.300
0.231
0.038
0.103
0.476
Methane
0.123
<0.001
0.319
0.181
0.4451
0.049
0.022
108
Figure 4.1 Diagram of flow-through metabolism chamber (FTMC) setup. The chamber
consists of a base driven 10 cm into the soil, a PVC pipe frame, a plastic cover, an inflow
pipe, and an outflow pipe. The large chamber stands 2.6 m tall including the base. The
small chamber stands 1.6 m tall including the base. The inflow pipe has a multi-speed
fan that forces air through the chamber, a pitot tube to measure air speed, and a sampling
port. The inflow pipe is connected near the bottom of the clear chamber. Air is forced
through the chamber and out of the outflow pipe at the top of the chamber. A second
sampling port is located on the outflow pipe.
109
Figure 4.2 Average gross primary productivity for five different plant communities. The
groups were open water, Phragmites australis, Scirpus fluviatilis, Sparganium
eurycarpum, and Typha spp. Phragmites australis and Typha spp. were statistical
different from the open water (p=0.012 and p=0.05, respectively). Phragmites australis
was also statistical different from Scirpus fluviatilis (p=0.029). The lower end of each
box represents the 25th percentile (lower quartile), while the upper end of each box
represents to upper 75th percentile (upper quartile). The bold bar in each box is the
median. The lines extending from each box indicate the minimum and maximum values
that are within 1.5*interquartile range (upper minus lower quartile) of the upper or lower
quartile. Outliers are represented by circles.
110
Figure 4.3 Monthly average and standard error of CO2 carbon fluxes of the five
vegetation communities. Gross primary productivity (GPP) is represented by positive
numbers indicating carbon uptake, while respiration is represented by negative numbers
indicating carbon release. Peak gross primary productivity occurred in June of 2010 and
in July of 2011.
111
Figure 4.4 Monthly median methane emissions from all five vegetation communities.
April was statistically different than June (p<0.001) and July (p=0.001), while September
was statistical different than June (p=0.004). Error bars represent the first and third
quantiles of the data for each month.
112
Figure 4.5 Carbon uptake and emission as CO2 in relation to solar radiation and
temperature. There was a significant difference in gross primary productivity due to solar
radiation (p<0.001), with increased solar radiation causing an increase in gross primary
productivity. Ambient temperature caused significant changes in respiration, with
respiration increasing with ambient temperature (p=0.038).
113
Chapter 5: Conclusions
1) Vegetation structure in created and restored wetlands is influenced by time and
initial creation techniques such as planting. Structural characteristics, such as
species richness and floristic quality assessment index scores tend to decrease
with age of the wetland. Planting was beneficial in regards to structural
characteristics, with the planted wetland having higher species richness, floristic
quality, and a smaller area occupied by invasive species than the unplanted
wetland.
2) Vegetation function in created and restored wetlands also seems to be influenced
by time and initial creation techniques such as planting, but to a lesser extent than
was seen for vegetation structure. Aboveground net primary productivity tended
to increase with age and the unplanted wetland had higher aboveground net
primary productivity than the planted wetland. Belowground net primary
productivity, total net primary productivity, and functional group composition
were not affected by planting.
114
3) Vegetation community type plays an important role in the gross primary
productivity and the net ecosystem exchange of carbon in wetlands. Species with
higher net primary productivity tended to have higher gross primary productivity
measurements than open water habitat and a macrophyte species with lower net
primary productivity. Respiration and methane emissions were not influenced by
the species present.
Recommendations
Based on the findings from the three chapters, six recommendations are given for
wetland creation and restoration projects to help improve monitoring techniques, success
rates, and carbon sequestration of created and restored wetlands.
 Extend monitoring time frames to 10-15 years to allow the structural
characteristics of the wetland to stabilize before determining success.
 Monitor trends in the succession of functional groups to help determine if the
mitigation wetlands are functionally equivalent to natural wetlands.
 Aim for median levels of aboveground net primary productivity within emergent
zones for enhanced diversity.
 Vegetation planting may be beneficial even in riverine wetlands where outside
propagules are introduced to the system via hydrochory and atmospheric fluxes.
This may improve the structural vegetation characteristics of the site by
increasing species diversity and quality and may help limit invasives in the
system.
115
 Introduce vegetation that has high productivity and high carbon use efficiency to
increase the amount of atmospheric carbon fixation and increase the amount of
carbon available for sequestration in the system.
 Reduce the amount of open water if increased carbon fixation is a desired goal.
116
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Appendix A: In-depth Site Descriptions
Hebron
Hebron mitigation bank is located at the Hebron State Fish Hatchery in Hebron,
Licking County, Ohio and consists of two created/restored wetlands. The mitigation bank
is located in the Bell Run sub-watershed, code 050400060406. The larger of the two
wetlands is located at 39° 56’25”N, 82° 30’19”W and is currently dominated by marsh
vegetation. Encroachment of shrub and tree species is evident around the perimeter of
the wetland. The original mitigation goal of this wetland was for it to develop into a
swamp. The smaller wetland is located at 39° 56’28”N, 82° 30’33”W and is a Typha spp.
marsh. According to Spieles et al. (2006) the wetlands were constructed on a Luray silty
clay loam soil, with plantings at the site consisting of only shrub and tree species; no
herbaceous vegetation plantings occurred at this site. Both wetlands are depressional
wetlands whose main water input is precipitation and thus experiences vertical
fluctuation of the water level (Brinson 1993). The site is managed by the Ohio
Department of Natural Resources, Division of wildlife. Little physical management of
the smaller wetland occurs, however, herbicides are sprayed within the larger wetland to
reduce the amount of the invasive species Phalaris arundinacae. A section of the larger
wetland is flooded during early September to attract water fowl for hunting purposes.
133
Sandy Ridge
Sandy Ridge mitigation bank is located at Sandy Ridge Reservation Lorain
County Metropark, North Ridgeville, Ohio. The site is located in the Black River and
French Creek subwatersheds, codes 041100010602 and 041100010601 respectively.
Three connected wetland cells make up the site. Only one of the cells, coordinates 41°
23’ 32.94” N 82° 03’ 3.99” W, was included in the study due to restricted access to
prevent the disruption of a nesting bald eagle pair. This cell was dominated by
herbaceous vegetation with a few trees located in upland islands within the wetland cell.
The main soil type at this site is Fitchville silt loam. The wetland at this site is
depressional and experiences water input from both precipitation and surface flow. The
water level mainly undergoes vertical fluctuation, but is designed to allow for some
unidirectional flow when desired (Brinson 1993). The site is maintained by the Lorain
County Metroparks. Management at the site includes mowing and spraying of Phalaris
arundinacea and lowering the water level of the wetland during the spring to prevent it
from spilling over the banks and onto the public walking trail.
Slate Run
Slate Run mitigation bank is located at Slate Run Wetlands Wildlife Refuge Slate
Run Metropark, just south of Canal Winchester in Pickaway County, Ohio. The site is
located in the Big Run and Little Walnut Creek sub-watersheds, codes 050600011805 and
050600011804 respectively. Approximately a dozen wetlands are located at this site.
Ultimately, four wetlands were sampled for inclusion in this study. Coordinates for these
134
sites were 39° 45’46”N 82° 51’59”W, 39° 45’41”N 82° 52’6”W, 39° 45’28”N 82°
51’59”W, and 39° 45’49”N 82° 51’49”W. All of these wetlands were dominated by
herbaceous vegetation with some encroachment of shrub and trees species around the
wetland borders. The main soil type at the site is Kokomo silty clay loam. All of the
wetlands at this site are depressional. Two of the four wetlands examined experience
mainly precipitation hydrologic inputs, while the remaining two experience both
precipitation and groundwater inputs. The water levels within these wetlands undergo
vertical fluctuation (Brinson 1993). The site is owned and managed by the Columbus
and Franklin County Metropolitan Park District. Management of the site includes
spraying and mowing for invasive species, as well as spraying/cutting of willow saplings
within the wetlands. Five wetlands were initially to be included in the study, but
chemical and physical removal of species (mowing) within the fifth wetland destroyed
the majority of vegetation therein.
Trumbull Creek Phase I
Trumbull Creek mitigation bank Phase I is Located in Geauga and Ashtabula
Counties within the Mill Creek subwatershed, code 041100040602. Three large wetland
cells are located at this site, with the middle cell (coordinates 41°39’ 46.13” N 81° 00’
33.00” W) being used for this study. The main soil types are Platea and Sheffield silt
loam. The wetland is depressinal with mostly precipitation hydrologic inputs and
experiences both vertical water fluctuation and during the rainy season, unidirectional
flow as well (Brinson 1993). Seeding was done post construction in 2007. Adjustment
135
of water level and spraying has been used at the site to control invasive species and
amount of open water at the site.
Trumbull Creek Phase II
Trumbull Creek Phase II is split up into a northern (coordinates 41° 39’ 53” N and
80° 59’ 48” W) and southern (coordinates 41° 39’ 48” N and 81° 00’ 0” W) section by
state route 166 with little to no connectivity between the two sections. This site is located
in Geauga and Ashtabula Counties, adjacent to Trumbull Creek Phase I. Trumbull Creek
Phase II is located within Mill Creek (code: 041100040602) and Bronson Creek (code:
041100040502) subwatersheds. The site consists of over 100 small constructed basins
that were designed to allow for water connectivity during the wet season. The soil at this
site consists of Sheffield soils. All of the wetlands are depressional, mainly precipitation
fed, and experience vertical fluctuation in the water levels (Brinson 1993). The site was
planted with tree seedlings in 2005. Spraying has occurred at the site to reduce the
amount of invasive vegetation, primarily Phalaris arundinacea.
Olentangy River Wetland Research Park
Two experimental wetlands, both one hectare in size, at the Olentangy River
Wetland Research Park, Ohio State University, Columbus Ohio (coordinates 40º 01’
12.33”N 83º 01’ 5.55”W) were used for this study. These wetlands were constructed in
1994 to be used for research and education. The site receives the majority of its
hydrologic inputs from the Olentangy river via pump, which is set so that the flow rate
136
into the wetlands is proportional to the water depth of the river. This site is within the
Mouth Olentangy River subwatershed (code: 050600011103). The soil type at this site is
mostly Ross silt loam with some Eldean silt loam (Anderson et al. 2002). The wetlands
used for this study are riverine wetlands whose main water source is unidirectional
surface flow (Brinson 1993). As far as management goes, the largest controlled variable
is the hydrology of the site. Over the course of this study, the water level has been
maintained in accordance with water depth in the river. Little is done in terms of invasive
species management; only occasional manual removal of Lythrum salicaria (purple
loosestrife) occurs within the two experimental wetlands. Phragmites australis (common
reed grass) is present in one of the experimental wetlands, but is not removed in order to
maintain the integrity of an ongoing experiment dealing with wetland planting and
succession.
Calamus Swamp
Calamus swamps is a natural kettle wetland west of Circleville in Pickaway
County, Ohio. This wetland is surrounded by agricultural fields within the Lick River
subwatershed (code: 050600020403). The site is owned by the Columbus Audubon
Society and is open to the public. The site is used for bird watching and the walking
trails. The soil at Calamus swamp is mainly Montgomery silty clay loam (USDA NRCS
2011). The wetland is a depressional wetland that is precipitation fed and experiences
vertical water level fluctuation (Brinson 1993). No wetland maintenance occurs at the
site.
137
Appendix B: Regression Equations
Table B.1 Regression equations created for dominant species. y-biomass per stem, xaverage height of the stems within a sampling plot. After using the equation to find y, y
is then multiplied by the number of stems in a plot to estimate ANPP of the species.
Other equations were obtained from Johnson (1998) and Muzika et a. (1987).
Species
Biddens cernua
Decodon verticallus
Eleocharis a.
Eleocharis obtusa
Hypericum m.
Juncus effusus
Penthorum sedoides
Phalaris arundinacae
Phyla lanceolata
Scirpus cyperinus
Scirpus pungens
Typha spp.
Equation
y = 0.0084x + 0.0139
y = 0.886x - 71.968
y = 0.0065x - 0.2201
y = 0.001x - 0.0025
y = 0.0136x - 0.4045
y = 0.0038x
y = 0.0082x - 0.002
y = 0.0103x - 0.0197
y = 0.0096x - 0.013
y = 0.0933x - 2.0333
y = 0.0034x + 0.1135
y = 0.0472x + 9.2366
138
R2
R = 0.86
R2 = 0.85
R2 = 0.77
R2 = 0.67
R2 = 0.77
R2 = 0.73
R2 = 0.98
R2 = 0.97
R2 = 0.93
R2 = 0.73
R2 = 0.82
R2 = 0.75
2
Appendix C: Mitigation Bank and Reference Wetland Vegetation
139
Table C.1 Plant species found at each site over the course of the study from 2008-2010.
Includes coefficient of conservatism and wetland indicator status for each species.
Species
Wetland
Indicator
Status
C
of
C
Hebron
'08
'09
ORW
'10
Abies
balsamea
Abutilon
theophrasti
(Medik.)
UPL
*
Acalypha
rhomboidea
(Raf.)
FAC-
0
Acalypha
virginica (L.)
FACU-
0
Acer negundo
(L.)
FAC+
3
Acer rubrum
(L.)
FAC
2
Acer
saccharinum
(L.)
FAC
3
Acer
saccharum
(Marshall)
FACU-
5
Acer sp.
OBL
*
Actaea alba
(L.)
UPL
7
Agrostis
tenuis (Sibth.)
FACU-
*
Alisma
plantagoaquatica (L.)
OBL
2
Alliaria
petiolata
FACU
*
Allium
canadense
FACU
2
'08
'09
Slate Run
'08
'09
'10
X
X
X
X
X
X
X
X
'10
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
'08
'09
'10
X
X
X
X
X
X
X
X
X
X
X
na
Acorus
calamus (L.)
Sandy Ridge
X
X
X
X
X
X
X
X
X
X
X
X
X
X
140
X
X
X
X
X
X
X
X
X
(L.)
Ambrosia
artemisiifolia
(L.)
FACU
0
X
X
X
Ambrosia
trifida (L.)
FAC
0
X
X
X
Andropogon
gerardii
(Vitman)
FAC
5
Apocynum
cannabinum
(L.)
FACU
1
Aristolochia
serpentaria
(L.)
UPL
7
Artemisia
vulgaris (L.)
FACU-
*
Asclepias
incarnate (L.)
OBL
4
X
X
X
Asclepias
syriaca (L.)
FACU-
1
X
X
X
Asclepias
tuberosa (L.)
UPL
4
Asimina
triloba ((L.)
Dunal)
FACU+
6
Aster
ericoides (L.)
FACU
2
Aster
lanceolatus
(Willd.)
FACW
3
Aster novaeangliae (L.)
FACW-
2
Aster pilosus
(Willd.)
UPL
1
Aster
praealtus
(Poir.)
FACW
6
Aster
racemosus
(Elliott)
FACW
2
Aster sp.
Aster
umbellatus
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
3
141
X
X
X
X
X
X
X
X
X
X
X
na
FACW
X
X
X
X
X
(Mill.)
Betula sp.
na
Bidens
aristota
((Michx.)Brit
ton)
FACW-
4
Bidens
cernua (L.)
OBL
2
X
X
Bidens
connata
(Muhl. ex
Willd.)
FACW+
3
X
X
Bidens
coronata
((L.) Britton)
OBL
3
X
Bidens
frondosa (L.)
FACW
2
Bidens sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Bidens
vulgata
(Greene)
FACW
2
Boehmeria
cylindrica
((L.) Sw.)
FACW+
4
Brasenia
schreberi
(J.F. Gmel.)
OBL
7
Campsis
radicans (L.)
FAC
1
Carex
bromoides
(Schkuhr ex
Willd.)
FACW
7
Carex frankii
(Kunth)
OBL
2
Carex
intumescens
(Rudge)
FACW+
5
Carex
lupilina
(Muhl. ex
Willd.)
OBL
3
Carex lurida
(Wahlenb.)
0BL
3
Carex
tribuloides
FACW+
4
X
X
X
X
X
X
X
142
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
(Wahlenb.)
Carex sp.
na
X
X
Carex
utriculata
(Boott)
OBL
7
Carex
vulpinoidae
(Michx.)
OBL
1
Carpinus
caroliniana
(Walter)
FAC
5
Carya glabra
((Mill.)
Sweet)
FACU-
5
Carya ovalis
((Wangenh.)
Sarg.)
UPL
5
Carya ovata
((Miller)
K.Koch)
FACU-
6
Cassia
fasiculata
(Michx.)
FACU
3
Catalpa
bignoniodes
(Walter)
UPL
*
Cepthalanthu
s occidentalis
(L.)
OBL
6
Chrisanthem
um
leucanthemu
m (L.)
UPL
*
Cichorium
intybus (L.)
UPL
*
Cicuta
bulbifera (L.)
OBL
3
X
Cicuta
maculata (L.)
OBL
3
X
Cicuta sp.
OBL
na
Cirscium
altissimum
((L.) Hill)
UPL
4
Cirscium
arvense ((L.)
Scop.)
FACU
*
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
143
X
Convolvulus
sepium (L.)
FAC-
1
Cornus
amomum
(Mill.)
FACW
2
Cornus
florida (L.)
FACU-
5
Cornus sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Cuscuta
gronovii
(Willd. Ex
Schulte)
FACW+
3
Cuscuta sp.
NA
na
Cyperus
erythrorhizos
(Muhl.)
FACW+
4
Cyperus
esculentus
(L.)
FACW
0
Cyperus
squarrosus
(L.)
FACW+
3
Cyperus
strigosus (L.)
FACW
1
Daucus
carota (L.)
UPL
*
Decodon
verticillutus
((L.)Elliott)
OBL
6
Desmodium
cuspidatum
((Muhl. ex
Willd.) DC ex
Loudon)
UPL
4
Dianthus
armeria (L.)
OBL
*
Dipsacus
sylvestris (L.)
FACU-
*
Duchesnea
indica
((Andrews)
Focke)
FACU-
*
Echinacea
purpurea
((L.)
Moench)
UPL
6
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
144
X
X
X
X
X
X
X
X
X
X
X
X
X
Echinochloa
crusgali ((L.)
P. Beauv.)
FACU
*
X
X
X
Echinochloa
muricata ((P.
Beauv.)
Fernald)
FACW+
3
X
X
X
Eleocharis
acicularis
((L.) Roem.
& Schult.)
OBL
3
X
X
Eleocharis
obtusa
((Willd.)
Schult.)
OBL
1
X
X
Eleocharis
ovata ((Roth)
Roem. &
Schult.)
OBL
9
X
Eleocharis
quadrangulat
a ((Michx.)
Roem. &
Schult.)
OBL
4
X
X
Elodia
canadensis
(Michx.)
OBL
3
X
X
Epilobium
coloratum
(Biehler)
OBL
1
X
X
X
X
X
X
X
X
X
X
X
X
Epilobium sp.
na
Equisetum
sp.
na
Erechtites
hieracifolia
((L.) Raf. ex
DC.)
FACU
2
Erigeron
annuus ((L.)
Pers.)
FACU
0
Erigeron sp.
X
X
X
X
X
X
FACW
6
Eupatorium
hyssopifolium
(L.)
UPL
4
Eupatorium
FACW
6
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Eupatorium
fistulosum
(Barratt)
X
X
X
X
X
X
X
145
maculatum
(L.)
Eupatorium
perfoliatum
(L.)
FACW+
3
Eupatorium
serotinum
(Michx.)
FAC-
2
Festuca
pratensis
(Huds.)
FACU-
*
Fragaria
virginiana
(Duchesne)
FACU
1
Fraxinus
americanus
(L.)
FACU
6
Fraxinus
pennsylvanic
a (Marshall)
FACW
3
Galium sp.
X
X
X
na
Galium
asperllum
(Michx.)
OBL
4
Galium
tinctorium
((L.) Scop.)
OBL
4
Geum
aleppicum
(Jacq.)
FAC
3
Geum
laciniatum
(Murray)
FAC+
2
Geum
lanceolatum
(Murray)
FAC+
5
X
X
Glechoma
hederacea
(L.)
FACU
*
Gleditsia
triacanthos
(L.)
FAC-
4
OBL
2
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
N
a
Geum sp.
Glyceria
striata
((Lam.)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
146
X
X
Hitchc.)
Helenium
autumnale
(L.)
FACW+
4
Helianthus
divaricatus
(L.)
UPL
4
Helianthus
gigenteus (L.)
FACW
6
Hemerocallis
fulva ((L.)L.)
UPL
*
Hibiscus
laevis (All.)
OBL
7
Hordem
jubatum (L.)
FAC
5
Hypericum
canadense
(L.)
FACW
7
Hypericum
mutilum (L.)
FACW
3
Hypericum
perforatum
(L.)
UPL
*
Impatiens
capensis
(Meerb.)
FACW
2
X
X
Ipomoea
puppurea
((L.) Roth)
UPL
*
X
X
Iris sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Juglans nigra
(L.)
FACU
5
Juncus
canadensis (J.
Gay ex
Laharpe)
OBL
4
X
Juncus
dudleyi
(Wiegand)
FACW-
3
X
X
X
Juncus
effuses (L.)
FACW+
1
X
X
X
Juncus tenuis
(Willd.)
FAC-
1
Juncus
FACW
3
X
X
X
X
X
147
X
torreyi
(Coville)
Lathyrus
palustris (L.)
FACW+
5
Leersia
oryzoides
((L.) Sw.)
OBL
1
X
X
X
X
X
X
X
X
X
X
X
Lemna minor
(L.)
OBL
3
X
X
X
X
X
X
X
X
X
X
X
Lespedeza
violacea ((L.)
Pers.)
UPL
4
X
X
X
X
X
Lillium sp.
na
Lindernia
dubia ((L.)
Pennell)
OBL
2
Lobelia
cardinalis
(L.)
FACW+
5
Lobelia
siphilitica
(L.)
FACW+
3
X
X
X
Lonicera
maackii
((Rupr.)
Maxim.)
UPL
*
X
X
X
Lonicera sp.
X
X
X
X
X
X
na
Lotus
corniculatus
(L.)
FACU-
*
Ludwigia
alternifolia
(L.)
FACW+
3
Ludwigia
palustris ((L.)
Elliott)
OBL
3
Lycopus
americanus
(Muhl. ex
W.P.C.
Barton)
OBL
3
Lycopus sp.
X
X
OBL
3
Lysimachia
OBL
*
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Lycopus
uniflorus
(Michx.)
X
X
X
X
X
X
148
X
X
nummularia
(L.)
Lythrum
salicaria (L.)
FACW+
*
Mentha
aravensis (L.)
FACW
2
Mimulus
ringens (L.)
OBL
4
Morus rubra
(L.)
FACU
7
Nelumbo
lutea (Willd.)
OBL
7
Nuphar
advena
((Aiton)
W.T.Aiton)
OBL
4
X
X
X
Nymphaea
odorata
(Aiton)
OBL
6
X
X
X
Oenthera
biennis (L.)
FACU-
1
X
X
X
Onoclea
sensibilis (L.)
FACW
2
Oxalis sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
FAC-
9
Oxalis stricta
(L.)
UPL
0
Panicum
dichotomiflor
um (Michx.)
FACW-
0
Panicum
virgatum (L.)
FAC
4
Parthenociss
us
quinquefolia
((L.) Planch.)
FACU
2
Penthorum
sedoides (L.)
OBL
2
Phalaris
arundinacea
(L.)
FACW+
*
Phaseolus
UPL
3
X
X
X
X
X
na
Oxalis
acetosella
(L.)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
149
polystachios
((L.) B.S.P.)
Phleum
pretense (L.)
FACU
*
Phragmites
australis
((Cav.) Trin.)
FACW
*
X
X
X
Phyla
lanceolata
((Michx.)Gre
ene)
OBL
3
X
X
X
Phytolacca
americana
(L.)
FACU+
1
Plantago
major (L.)
FACU
*
Platanus
occidentalis
(L.)
FACW-
7
Poa palustris
(L.)
FACW
5
Podophyllum
peltatum (L.)
FACU
4
Polygonum
coccineum
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Polygonum
hydropiper
(L.)
OBL
1
Polygonum
hydropiperoi
des (Michx.)
OBL
6
Polygonum
lapathifolia
(L.)
FACW+
1
Polygonum
pennylvanica
(L.)
FACW
0
Polygonum
persicaria
(L.)
FACW
*
Polygonum
sagittatum
(L.)
OBL
2
Polygonum
virginianum
(L.)
FAC
3
X
X
X
X
X
150
X
X
X
X
X
X
X
Pontideria
cordata (L.)
OBL
6
Populus
deltoides (W.
Bartram ex
Marshall)
FAC
3
Populus
tremuloides
(Michx.)
FACU
Potamogeton
natans (L.)
Potamogeton
pectinatus
(L.)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2
X
X
X
X
X
OBL
8
X
X
X
OBL
2
X
X
Potamogeton
sp.
X
X
na
X
X
X
X
Prenanthes
altisima (L.)
FACU-
4
Prunella
vulgaris (L.)
FACU+
0
Quercus
bicolor
(Willd.)
FACW+
7
Quercus
palustris
(Muenchh.)
FACW
5
Quercus
rubra (L.)
FACU-
6
Ranunculus
abortivus (L.)
FACW-
1
Ranunculus
hispidus
(Michx.)
FAC
4
Rhus typhina
(L.)
UPL
2
Rorippa
palustris ((L.)
Besser)
OBL
2
X
X
X
Rorippa
sylvestris
((L.)Besser)
FACW
*
X
X
X
Rosa
multiflora
(Thunb. ex
Murray)
FACU
*
X
X
X
Rosa
OBL
5
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
151
X
X
palustris
(Marshall)
Rosa setigera
(Michx.)
FACU
Rosa sp.
4
na
X
X
X
X
X
X
X
Rubus
allegheniensi
s (Porter)
FACU-
1
Rubus
occidentalis
(L.)
UPL
1
Rudbeckia
hirta (L.)
FACU-
1
X
Rudbeckia
triloba (L.)
FACU
5
X
X
X
Rumex
crispus (L.)
FACU
*
Sagittaria
latifolia
(Willd.)
OBL
1
X
X
X
X
X
X
Sagittaria sp.
OBL
*
X
X
X
Salix alba
(L.)
FACW
*
X
X
X
X
X
Salix
amygdaloides
(Andersson)
FACW
3
X
X
X
X
X
Salix
babylonica
(L.)
FACW-
*
X
X
X
X
X
Salix interior
(Rowlee)
OBL
1
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Salix
matsudana
Salix nigra
(Marshall)
FACW+
Salix sp.
2
X
X
X
X
X
X
X
X
X
na
X
Sambucus
canadensis
OBL
3
X
X
X
Samolus
floribundus
(Kunth)
OBL
4
X
X
X
Saururus
cernuus (L.)
OBL
8
X
X
X
152
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Schoenoplect
us acutus
((Muhl. ex
Bigelow)
Love &
Love)
OBL
7
OBL
2
X
X
X
Scirpus
atrovirens
(Willd.)
OBL
1
X
X
X
Scirpus
cyperinus
((L.) Kunth.)
FACW+
1
X
X
X
X
Scirpus
fluviatilis
((Torr.) A.
Gray)
OBL
5
X
X
X
X
Scirpus
pungens
(Vahl)
FACW+
5
Seteria
glauca ((L.)
P. Beauv.)
FAC
*
Setaria
viridis ((L.) P.
Beauv.)
UPL
*
Sium suave
(Walter)
OBL
6
Solanum
carolinense
(L.)
UPL
*
Solanum
dulcamara
(L.)
FAC-
*
Solidago
altissima (L.)
FACU
1
Solidago
graminifolia
((L.) Salisb.)
FAC
1
Solidago
juncea
(Aiton)
UPL
2
Solidago
patula (Muhl.
ex Willd.)
OBL
6
Schoenoplect
us
tabernaemont
ani ((C.C.
Gmel.) Palla)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
153
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Solidago
rugosa (Mill.)
FAC
Solidago sp.
2
X
8
X
X
Solidago
speciosa
(Nutt.)
UPL
5
Solidago
uliginosa
(Nutt.)
OBL
9
Sonchus
oleraceus (L.)
UPL
*
Sorghasrum
nutans ((L.)
Nash)
UPL
5
Sparganium
americanum
(Nutt.)
OBL
6
Sparganium
eurycarpum
(Engelm. ex
A. Gray)
OBL
4
Spartina
pectinata
(Link)
OBL
5
Symplocarpu
s foetidus
((L.) Salisb.
ex Barton)
OBL
7
Taraxacum
officinale
(Weber ex
F.H. Wigg.)
FACU-
*
Toxicodendro
n radicans
((L.) Kuntze)
FAC
1
Trifolium
dubium
(Sibth.)
UPL
Trifolium
hybridum (L.)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
*
X
X
X
FACU-
*
X
X
X
X
X
Trifolium
pratense (L.)
FACU-
*
X
X
X
Typha
angustifolia
(L.)
OBL
*
X
X
X
Typha
latifolia (L.)
OBL
1
X
X
X
X
X
X
X
X
X
154
Typha x
glauca
(Godr.)
OBL
*
Typha sp.
OBL
na
X
X
X
Ulmus
americana
(L.)
FACW-
2
X
X
X
Ulmus rubra
(Muhl.)
FAC
3
X
X
Ulmus sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Utrica dioica
((Muhl. ex
Willd.)
Wedd.)
FAC-
1
Utricularia
vulgaris (L.)
OBL
6
Verbascum
blattaria (L.)
UPL
*
Verbascum
thapsus (L.)
UPL
*
X
Verbena
hastata (L.)
FACW+
4
X
Verbena
urticifolia
(L.)
FACU
3
Vernonia
gigentea
((Walter)
Trel.)
FAC
2
X
Vernonia
noveboracens
is ((L.)
Michx.)
FACW+
3
X
Vernonia sp.
X
X
X
OBL
6
Viburnum
lentago (L.)
FAC
5
Vicia sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
na
Vinca minor
(L.)
UPL
*
Viola sororia
(Willd.)
FAC-
1
X
X
X
X
X
X
X
X
X
X
X
na
Veronic
scutellata (L.)
X
X
X
155
Viola sp.
na
X
Vitus riparia
(Michx.)
FACW
3
Vitis vulpina
(L.)
FAC
3
Xanthium
strumarium
(L.)
FAC
*
X
Species
Wetland
Indicator
Status
C
of
C
Trumbull Creek
Phase I
X
X
2008
2009
X
2010
X
X
X
X
X
X
Trumbull Creek
Phase II North
2008
2009
2010
X
X
X
X
X
X
X
X
X
X
Trumbull Creek
Phase II South
2008
2009
2010
Calamus
Swamp
2010
Abies
balsamea
Abutilon
theophrasti
(Medik.)
UPL
*
Acalypha
rhomboidea
(Raf.)
FAC-
0
Acalypha
virginica (L.)
FACU-
0
Acer negundo
(L.)
FAC+
3
Acer rubrum
(L.)
FAC
2
Acer
saccharinum
(L.)
FAC
3
Acer
saccharum
(Marshall)
FACU-
5
X
n
a
X
Acer sp.
Acorus
calamus (L.)
OBL
*
Actaea alba
(L.)
UPL
7
Agrostis
FACU-
*
X
X
X
X
X
X
X
X
X
X
156
X
X
X
X
X
X
X
X
tenuis (Sibth.)
Alisma
plantagoaquatica (L.)
OBL
2
Alliaria
petiolata
FACU
*
Allium
canadense
(L.)
FACU
2
Ambrosia
artemisiifolia
(L.)
FACU
0
Ambrosia
trifida (L.)
FAC
0
Andropogon
gerardii
(Vitman)
FAC
5
Apocynum
cannabinum
(L.)
FACU
1
Aristolochia
serpentaria
(L.)
UPL
7
Artemisia
vulgaris (L.)
FACU-
*
Asclepias
incarnate (L.)
OBL
4
X
X
X
Asclepias
syriaca (L.)
FACU-
1
X
X
X
Asclepias
tuberosa (L.)
UPL
4
Asimina
triloba ((L.)
Dunal)
FACU+
6
Aster
ericoides (L.)
FACU
2
Aster
lanceolatus
(Willd.)
FACW
3
Aster novaeangliae (L.)
FACW-
2
X
X
Aster pilosus
(Willd.)
UPL
1
X
X
Aster
FACW
6
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
157
X
X
X
X
X
praealtus
(Poir.)
Aster
racemosus
(Elliott)
FACW
X
X
X
n
a
Aster sp.
Aster
umbellatus
(Mill.)
2
FACW
Betula sp.
3
X
n
a
X
Bidens
aristota
((Michx.)Brit
ton)
FACW-
4
Bidens
cernua (L.)
OBL
2
Bidens
connata
(Muhl. ex
Willd.)
FACW+
3
Bidens
coronata
((L.) Britton)
OBL
3
Bidens
frondosa (L.)
FACW
2
X
X
X
X
X
X
X
X
X
X
X
n
a
Bidens sp.
X
X
Bidens
vulgata
(Greene)
FACW
2
X
Boehmeria
cylindrica
((L.) Sw.)
FACW+
4
X
Brasenia
schreberi
(J.F. Gmel.)
OBL
7
Campsis
radicans (L.)
FAC
1
Carex
bromoides
(Schkuhr ex
Willd.)
FACW
7
X
Carex frankii
(Kunth)
OBL
2
X
X
X
X
X
X
158
X
X
X
X
X
Carex
intumescens
(Rudge)
FACW+
5
X
X
X
Carex
lupilina
(Muhl. ex
Willd.)
OBL
3
X
X
X
Carex lurida
(Wahlenb.)
0BL
3
X
X
X
Carex
tribuloides
(Wahlenb.)
FACW+
4
X
X
X
Carex sp.
na
Carex
utriculata
(Boott)
OBL
7
Carex
vulpinoidae
(Michx.)
OBL
1
X
X
Carpinus
caroliniana
(Walter)
FAC
5
X
X
Carya glabra
((Mill.)
Sweet)
FACU-
5
X
X
Carya ovalis
((Wangenh.)
Sarg.)
UPL
5
X
X
Carya ovata
((Miller)
K.Koch)
FACU-
6
X
Cassia
fasiculata
(Michx.)
FACU
3
Catalpa
bignoniodes
(Walter)
UPL
*
Cepthalanthu
s occidentalis
(L.)
OBL
6
Chrisanthem
um
leucanthemu
m (L.)
UPL
*
Cichorium
intybus (L.)
UPL
*
Cicuta
OBL
3
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
159
bulbifera (L.)
Cicuta
maculata (L.)
OBL
3
Cicuta sp.
OBL
n
a
Cirscium
altissimum
((L.) Hill)
UPL
4
Cirscium
arvense ((L.)
Scop.)
FACU
*
Convolvulus
sepium (L.)
FAC-
1
Cornus
amomum
(Mill.)
FACW
2
Cornus
florida (L.)
FACU-
5
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Cuscuta
gronovii
(Willd. Ex
Schulte)
FACW+
3
Cuscuta sp.
NA
n
a
Cyperus
erythrorhizos
(Muhl.)
FACW+
4
Cyperus
esculentus
(L.)
FACW
0
Cyperus
squarrosus
(L.)
FACW+
3
Cyperus
strigosus (L.)
FACW
1
X
X
X
X
X
X
X
X
X
Daucus
carota (L.)
UPL
*
X
X
X
X
X
X
X
X
X
Decodon
verticillutus
((L.)Elliott)
OBL
6
UPL
4
Desmodium
cuspidatum
((Muhl. ex
Willd.) DC ex
X
X
n
a
Cornus sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
160
Loudon)
Dianthus
armeria (L.)
UPL
*
Dipsacus
sylvestris (L.)
FACU-
*
Duchesnea
indica
((Andrews)
Focke)
FACU-
*
Echinacea
purpurea
((L.)
Moench)
UPL
6
Echinochloa
crusgali ((L.)
P. Beauv.)
FACU
*
X
X
Echinochloa
muricata ((P.
Beauv.)
Fernald)
FACW+
3
X
X
Eleocharis
acicularis
((L.) Roem.
& Schult.)
OBL
3
Eleocharis
obtusa
((Willd.)
Schult.)
OBL
1
Eleocharis
ovata ((Roth)
Roem. &
Schult.)
OBL
9
Eleocharis
quadrangulat
a ((Michx.)
Roem. &
Schult.)
OBL
4
Elodia
canadensis
(Michx.)
OBL
3
Epilobium
coloratum
(Biehler)
OBL
1
Epilobium sp.
n
a
Equisetum
sp.
n
a
Erechtites
FACU
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2
161
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
hieracifolia
((L.) Raf. ex
DC.)
Erigeron
annuus ((L.)
Pers.)
FACU
0
X
X
X
X
X
X
X
X
X
n
a
Erigeron sp.
Eupatorium
fistulosum
(Barratt)
FACW
6
Eupatorium
hyssopifolium
(L.)
UPL
4
Eupatorium
maculatum
(L.)
FACW
6
Eupatorium
perfoliatum
(L.)
FACW+
3
Eupatorium
serotinum
(Michx.)
FAC-
2
Festuca
pratensis
(Huds.)
FACU-
*
Fragaria
virginiana
(Duchesne)
FACU
1
Fraxinus
americanus
(L.)
FACU
6
X
Fraxinus
pennsylvanic
a (Marshall)
FACW
3
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
n
a
Galium sp.
Galium
asperllum
(Michx.)
OBL
4
Galium
tinctorium
((L.) Scop.)
OBL
4
Geum
aleppicum
(Jacq.)
FAC
3
Geum
FAC+
2
X
162
laciniatum
(Murray)
Geum
lanceolatum
(Murray)
FAC+
5
X
N
a
Geum sp.
Glechoma
hederacea
(L.)
FACU
*
Gleditsia
triacanthos
(L.)
FAC-
4
Glyceria
striata
((Lam.)
Hitchc.)
OBL
2
Helenium
autumnale
(L.)
FACW+
4
Helianthus
divaricatus
(L.)
UPL
4
Helianthus
gigenteus (L.)
FACW
6
Hemerocallis
fulva ((L.)L.)
UPL
*
Hibiscus
laevis (All.)
OBL
7
Hordem
jubatum (L.)
FAC
5
Hypericum
canadense
(L.)
FACW
7
X
X
X
Hypericum
mutilum (L.)
FACW
3
X
X
Hypericum
perforatum
(L.)
UPL
*
Impatiens
capensis
(Meerb.)
FACW
2
Ipomoea
puppurea
((L.) Roth)
UPL
*
Iris sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
n
X
X
X
163
a
Juglans nigra
(L.)
FACU
5
Juncus
canadensis (J.
Gay ex
Laharpe)
OBL
4
Juncus
dudleyi
(Wiegand)
FACW-
3
Juncus
effuses (L.)
FACW+
1
Juncus tenuis
(Willd.)
FAC-
1
Juncus
torreyi
(Coville)
FACW
3
Lathyrus
palustris (L.)
FACU-
5
Leersia
oryzoides
((L.) Sw.)
OBL
1
X
X
X
Lemna minor
(L.)
OBL
3
X
X
X
Lespedeza
violacea ((L.)
Pers.)
UPL
4
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
n
a
Lillium sp.
Lindernia
dubia ((L.)
Pennell)
OBL
2
Lobelia
cardinalis
(L.)
FACW+
5
Lobelia
siphilitica
(L.)
FACW+
3
Lonicera
maackii
((Rupr.)
Maxim.)
UPL
*
X
X
X
X
X
n
a
Lonicera sp.
Lotus
corniculatus
X
FACU-
*
X
X
X
164
(L.)
Ludwigia
alternifolia
(L.)
FACW+
3
X
X
X
Ludwigia
palustris ((L.)
Elliott)
OBL
3
X
X
X
Lycopus
americanus
(Muhl. ex
W.P.C.
Barton)
OBL
3
X
X
X
X
X
X
n
a
Lycopus sp.
Lycopus
uniflorus
(Michx.)
OBL
3
Lysimachia
nummularia
(L.)
OBL
*
Lythrum
salicaria (L.)
FACW+
*
Mentha
aravensis (L.)
FACW
2
Mimulus
ringens (L.)
OBL
4
X
X
X
Morus rubra
(L.)
FACU
7
X
X
X
Nelumbo
lutea (Willd.)
OBL
7
Nuphar
advena
((Aiton)
W.T.Aiton)
OBL
4
X
X
X
Nymphaea
odorata
(Aiton)
OBL
6
X
X
X
Oenthera
biennis (L.)
FACU-
1
Onoclea
sensibilis (L.)
FACW
2
X
n
a
X
Oxalis sp.
Oxalis
acetosella
(L.)
X
FAC-
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
9
165
X
X
Oxalis stricta
(L.)
UPL
0
Panicum
dichotomiflor
um (Michx.)
FACW-
0
Panicum
virgatum (L.)
FAC
4
Parthenociss
us
quinquefolia
((L.) Planch.)
FACU
2
Penthorum
sedoides (L.)
OBL
2
Phalaris
arundinacea
(L.)
FACW+
*
Phaseolus
polystachios
((L.) B.S.P.)
UPL
3
Phleum
pretense (L.)
FACU
*
Phragmites
australis
((Cav.) Trin.)
FACW
*
Phyla
lanceolata
((Michx.)Gre
ene)
OBL
3
Phytolacca
americana
(L.)
FACU+
1
X
Plantago
major (L.)
FACU
*
X
Platanus
occidentalis
(L.)
FACW-
7
Poa palustris
(L.)
FACW
5
Podophyllum
peltatum (L.)
FACU
4
Polygonum
hydropiper
(L.)
OBL
1
Polygonum
OBL
6
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Polygonum
coccineum
X
X
X
X
X
X
X
166
X
X
X
X
X
X
X
hydropiperoi
des (Michx.)
Polygonum
lapathifolia
(L.)
FACW+
1
Polygonum
pennylvanica
(L.)
FACW
0
Polygonum
persicaria
(L.)
FACW
*
X
X
Polygonum
sagittatum
(L.)
OBL
2
X
X
Polygonum
virginianum
(L.)
FAC
3
Pontideria
cordata (L.)
OBL
6
Populus
deltoides (W.
Bartram ex
Marshall)
FAC
3
Populus
tremuloides
(Michx.)
FACU
2
Potamogeton
natans (L.)
OBL
8
Potamogeton
pectinatus
(L.)
OBL
2
Potamogeton
sp.
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
n
a
Prenanthes
altisima (L.)
FACU-
4
X
X
X
Prunella
vulgaris (L.)
FACU+
0
X
X
X
Quercus
bicolor
(Willd.)
FACW+
7
X
X
X
X
X
Quercus
palustris
(Muenchh.)
FACW
5
X
X
X
X
X
Quercus
rubra (L.)
FACU-
6
X
Ranunculus
FACW-
1
X
X
X
167
X
X
X
X
X
abortivus (L.)
Ranunculus
hispidus
(Michx.)
FAC
4
Rhus typhina
(L.)
UPL
2
Rorippa
palustris ((L.)
Besser)
OBL
2
Rorippa
sylvestris
((L.)Besser)
FACW
*
Rosa
multiflora
(Thunb. ex
Murray)
FACU
*
X
X
X
X
X
X
Rosa
palustris
(Marshall)
OBL
5
X
X
X
X
X
X
Rosa setigera
(Michx.)
FACU
4
X
X
n
a
Rosa sp.
Rubus
allegheniensi
s (Porter)
FACU-
1
Rubus
occidentalis
(L.)
UPL
1
Rudbeckia
hirta (L.)
FACU-
1
Rudbeckia
triloba (L.)
FACU
5
Rumex
crispus (L.)
FACU
*
Sagittaria
latifolia
(Willd.)
OBL
1
Sagittaria sp.
OBL
*
Salix alba
(L.)
FACW
*
Salix
amygdaloides
(Andersson)
FACW
3
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
168
Salix
babylonica
(L.)
FACW-
*
Salix interior
(Rowlee)
OBL
1
FACW+
2
Salix
matsudana
Salix nigra
(Marshall)
X
X
X
n
a
Salix sp.
Sambucus
canadensis
OBL
3
Samolus
floribundus
(Kunth)
OBL
4
Saururus
cernuus (L.)
OBL
8
Schoenoplect
us acutus
((Muhl. ex
Bigelow)
Love &
Love)
OBL
7
Schoenoplect
us
tabernaemont
ani ((C.C.
Gmel.) Palla)
OBL
2
Scirpus
atrovirens
(Willd.)
OBL
1
Scirpus
cyperinus
((L.) Kunth.)
FACW+
1
Scirpus
fluviatilis
((Torr.) A.
Gray)
OBL
5
Scirpus
pungens
(Vahl)
FACW+
5
Seteria
glauca ((L.)
P. Beauv.)
FAC
*
Setaria
viridis ((L.) P.
Beauv.)
UPL
*
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
169
X
Sium suave
(Walter)
OBL
6
Solanum
carolinense
(L.)
UPL
*
Solanum
dulcamara
(L.)
FAC-
*
Solidago
altissima (L.)
FACU
1
Solidago
graminifolia
((L.) Salisb.)
FAC
1
X
X
X
Solidago
juncea
(Aiton)
UPL
2
X
X
X
Solidago
patula (Muhl.
ex Willd.)
OBL
6
X
X
Solidago
rugosa (Mill.)
FAC
2
Solidago sp.
X
X
X
X
8
Solidago
speciosa
(Nutt.)
UPL
5
Solidago
uliginosa
(Nutt.)
OBL
9
Sonchus
oleraceus (L.)
UPL
*
Sorghasrum
nutans ((L.)
Nash)
UPL
5
Sparganium
americanum
(Nutt.)
OBL
6
Sparganium
eurycarpum
(Engelm. ex
A. Gray)
OBL
4
Spartina
pectinata
(Link)
OBL
5
Symplocarpu
s foetidus
((L.) Salisb.
ex Barton)
OBL
7
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
170
Taraxacum
officinale
(Weber ex
F.H. Wigg.)
FACU-
*
Toxicodendro
n radicans
((L.) Kuntze)
FAC
1
Trifolium
dubium
(Sibth.)
UPL
*
Trifolium
hybridum (L.)
FACU-
*
Trifolium
pratense (L.)
FACU-
*
Typha
angustifolia
(L.)
OBL
*
Typha
latifolia (L.)
OBL
1
Typha x
glauca
(Godr.)
OBL
*
Typha sp.
OBL
n
a
Ulmus
americana
(L.)
FACW-
2
X
Ulmus rubra
(Muhl.)
FAC
3
X
n
a
Ulmus sp.
Utrica dioica
((Muhl. ex
Willd.)
Wedd.)
FAC-
1
Utricularia
vulgaris (L.)
OBL
6
Verbascum
blattaria (L.)
UPL
*
Verbascum
thapsus (L.)
UPL
*
Verbena
hastata (L.)
FACW+
4
Verbena
urticifolia
(L.)
FACU
3
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
171
X
X
X
X
Vernonia
gigentea
((Walter)
Trel.)
FAC
2
Vernonia
noveboracens
is ((L.)
Michx.)
FACW+
3
X
X
X
X
X
X
X
X
n
a
Vernonia sp.
Veronic
scutellata (L.)
OBL
6
Viburnum
lentago (L.)
FAC
5
n
a
Vicia sp.
Vinca minor
(L.)
UPL
*
Viola sororia
(Willd.)
FAC-
1
X
X
X
X
X
X
X
X
n
a
Viola sp.
Vitus riparia
(Michx.)
FACW
3
Vitis vulpina
(L.)
FAC
3
Xanthium
strumarium
(L.)
FAC
*
172
Appendix D: Olentangy River Wetland Research Park Experimental Wetlands
Vegetation Community Maps from 2008-2011
173
Figure D.1 2008 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park
174
Figure D.2 2009 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park
175
Figure D.3 2010 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park
176
Figure D.4 2011 vegetation map of dominant communities in the two experimental
wetlands at the Olentangy River Wetland Research Park
177
Appendix E: Calculations for Metabolism Chambers
Gross Primary Productivity and Respiration
gC =
m2 hr
(
Flow Rate *
)
∆ mg C-CO2
m3
* 60 *60
Area of Chamber Base * 1000
∆ mg C-CO2
m3
=
(
12.01 * ppmin CO2 * atm
0.08214 * Kin
-
12.01 * ppmout CO2 * atm
0.08214 * Kout
)
ppmin = ppm CO2 from Inflow
ppmout = ppm CO2 from Outflow
atm = Barometric Pressure (atm)
Kin = Ambient Temperature (K)
Kout = Chamber Temperature (K)
Flow Rate (m3/s)
=
(√
2*lb/ft2
ρ
)
* 1.12 * CA * 0.02832
lb/ft2 = Air Pressure Reading from Inflow Pitot Tube
CA = Cross Sectional Area of Pipe
ρ (slug/ft ) =
3
lb/ft2
1716.0 * R
lb/ft2 = Barometric Pressure
R = Ambient Temperature (R)
178
Appendix F: Statistical Tables
179
F.1 Chapter 2 Statistical Tables
F.1.1 Mitigation vs. Reference Wetlands
aov1<-aov(Species.Richness~status)
summary(aov1)
Df
status
Residuals
Mean
F value Pr(>F)
Sq
7363
7363 20.33 7e-05
12675
362
Sum Sq
1
35
***
aov1<-aov(NumberCommunities~status)
summary(aov1)
Df
status
Residuals
Mean
F value Pr(>F)
Sq
1
9.95
9.946 1.548
0.254
35 258.32
7.381
Sum Sq
aov5<-aov(FQAIscore~status)
summary(aov5)
Df
status
Residuals
Mean
F value Pr(>F)
Sq
63.7
63.73 4.451
0.0421 *
501.2
14.32
Sum Sq
1
35
ANOV3<-lm(H~status)
summary.aov(ANOV3)
Mean
F value Pr(>F)
Sq
1 0.2917 0.29168 1.6309
0.2117
29 5.1865 0.17884
6 observations deleted due
to
missingness
Df
status
Residuals
Sum Sq
ANOV4<-lm(CDI~status)
summary.aov(ANOV4)
Df
status
Residuals
Mean
F value Pr(>F)
Sq
1 0.2859 0.28587 2.014
0.1647
35 4.9681 0.14195
Sum Sq
aov8<aov(Biomass.Weighted.by.Community.area~status)
summary(aov8)
Df
status
Residuals
180
Mean
F value Pr(>F)
Sq
1 427293 427293 18.63
0.000124 ***
35 802670 22933
Sum Sq
aov1<-aov(loganpp~status)
summary(aov1)
Df
status
Residuals
1
35
181
Mean
F value Pr(>F)
Sq
0.764
0.764 6.864
0.0129 *
3.896 0.1113
Sum Sq
F.1.2 Age of Mitigation Bank Wetlands
aov4<-aov(Species.Richness~ATS)
summary(aov4)
Df
ATS
Residuals
1
28
Sum Sq Mean Sq F value Pr(>F)
1887
1887.1
5.031
0.033 *
10502
375.1
1
28
Sum Sq Mean Sq F value Pr(>F)
1.22
1.217
0.131
0.72
260.73
9.312
1
28
Sum Sq Mean Sq F value Pr(>F)
116.7
116.69
9.218
0.00513 **
354.6
12.66
1
28
Sum Sq Mean Sq F value Pr(>F)
0.247 0.24706
1.653
0.209
4.185 0.14946
aov3<-aov(NumberCommunities~ATS)
summary(aov3)
Df
ATS
Residuals
aov5<-aov(FQAIscore~ATS)
summary(aov5)
Df
ATS
Residuals
aov2<-aov(H.~ATS)
summary(aov2)
Df
ATS
Residuals
aov1<-aov(CDI~ATS)
summary(aov1)
Df
ATS
Residuals
1
28
Sum Sq Mean Sq F value Pr(>F)
0.268
0.2682
1.827
0.187
4.111
0.1468
Df
Sum
aov6<aov(Biomass.Weighted.by.Community.area~ATS)
summary(aov6)
Df
ATS
Residuals
1
28
Sum Sq Mean Sq F value Pr(>F)
20612
20612
8.705
0.006 **
66299
2368
1
28
Sum Sq Mean Sq F value Pr(>F)
0.1321 0.13209 14.374 0.000787 ***
0.2573 0.00919
aov7<-aov(loganppw~ATS)
summary(aov7)
Df
ATS
Residuals
182
F.1.3 Hydrology
aov2<-aov(Species.Richness~Hydrology)
summary(aov2)
Df
Hydrology
Residuals
Sum
F
Mean Sq
Pr(>F)
Sq
value
2 9751
4875 16.11 1.2e-05
34 10288
303
***
aov2<-aov(NumberCommunities~Hydrology)
summary(aov2)
Df
Hydrology
Residuals
Sum
F
Mean Sq
Pr(>F)
Sq
value
2 14.93
7.466 1.025
0.367
34 247.66
7.284
aov6<-aov(FQAIscore~Hydrology)
summary(aov6)
Df
Hydrology
Residuals
Sum
F
Mean Sq
Pr(>F)
Sq
value
2 116.1
58.06 4.399
0.02 *
34 448.8
13.2
ANOV9<-lm(H~Hydrology)
summary.aov(ANOV9)
Df
Hydrology
Residuals
Sum
Sq
Mean Sq
F
Pr(>F)
value
0.01302
0.0691
0.7945
1
0.18845
29 5.4651
3
6 observations deleted due
to
missingness
1 0.013
ANOV10<-lm(CDI~Hydrology)
summary.aov(ANOV10)
Df
Hydrology
Residuals
Sum
F
Mean Sq
Pr(>F)
Sq
value
2 0.4617 0.23086 1.6379
0.2093
34 4.7923 0.14095
aov9<-aov(Biomass.Weighted.by.Community.area~Hy­
drology)
summary(aov9)
Df
Hydrology
Residuals
183
Sum
F
Mean Sq
Pr(>F)
Sq
value
2 236863 118431 4.055
0.0263 *
34 993100
29209
F.1.4 Location in Ohio
aov3<-aov(Species.Richness~Location)
summary(aov3)
Df
Location
Residuals
Mean F
Pr(>F)
Sq
value
3400
3400 7.151
0.0113 *
16639
475
Sum Sq
1
35
aov4<-aov(NumberCommunities~Location)
summary(aov4)
Df
Location
Residuals
Mean F
Pr(>F)
Sq
value
121.6 121.64 29.04 4.94e-06
146.6
4.19
Sum Sq
1
35
***
aov7<-aov(FQAIscore~Location)
summary(aov7)
Df
Location
Residuals
ANOV5<-lm(H~Location)
summary.aov(ANOV5)
Sum Sq
Location
Residuals
Mean F
Pr(>F)
Sq
value
163.7 163.7 14.28
0.000589 ***
401.2 11.46
Sum Sq
1
35
Mean Sq
F value Pr(>F)
1 0.1294 0.12938 0.7015
0.4091
29 5.3488 0.18444
6 observations deleted due
to
missingness
>
>
ANOV6<-lm(CDI~Location)
summary.aov(ANOV6)
Df
Location
Residuals
Mean F
Pr(>F)
Sq
value
0.7625 0.76252 5.942
0.02 *
4.4915 0.12833
Sum Sq
1
35
aov3<aov(Biomass.Weighted.by.Community.area~Location)
summary(aov3)
Df
Location
Residuals
184
Mean F
Pr(>F)
Sq
value
1
3023
3023 0.087
0.77
35 1223272 34951
Sum Sq
F.2 Chapter 3 Statistical Tables
F.2.1 Wetland vegetation
aov6<-aov(Species.Richness~Wet­
land*YS)
summary(aov6)
Df
Wetland
YS
Wetland:YS
Residuals
Sum Sq
1
1
1
2
37.5
6.25
0.25
1.5
Mean Sq
F value
Pr(>F)
37.5
50
0.0194 *
6.25
8.333
0.102
0.25
0.333
0.622
0.75
aov4<-aov(FQAIscore~Wetland*YS)
summary(aov4)
Df
Wetland
YS
Wetland:YS
Residuals
Sum Sq
Mean Sq
F value
Pr(>F)
1
18.027
18.027
540.8
0.00184 **
1
0.09
0.09
2.7
0.24206
1
0.09
0.09
2.7
0.24206
2
0.067
0.033
aov3<-aov(CDI~Wetland*YS)
summary(aov3)
Df
Wetland
YS
Wetland:YS
Residuals
1
1
1
2
Sum Sq
Mean Sq
F value
Pr(>F)
0.05607
0.05607
0.624
0.512
0.06003
0.06003
0.668
0.5
0.00023
0.00023
0.003
0.965
0.17962
0.08981
1
1
1
2
Sum Sq
Mean Sq
F value
Pr(>F)
20.17
20.167
2.847
0.234
12.25
12.25
1.729
0.319
0.25
0.25
0.035
0.868
14.17
7.083
1
1
1
2
Sum Sq
Mean Sq
F value
Pr(>F)
24890
24890
158.26
0.00626 **
242
242
1.54
0.34047
2992
2992
19.02
0.04875 *
315
157
aov11<aov(macrophyte~Wetland*YS)
summary(aov11)
Df
Wetland
YS
Wetland:YS
Residuals
aov7<aov(WANPPannual~Wetland*YS)
summary(aov7)
Df
Wetland
YS
Wetland:YS
Residuals
185
aov8<aov(WBNPPannual~Wetland*YS)
summary(aov8)
Df
Wetland
YS
Wetland:YS
Residuals
1
1
1
2
Sum Sq
Mean Sq
F value
Pr(>F)
42447
42447
2.832
0.234
68826
68826
4.592
0.165
5618
5618
0.375
0.603
29977
14988
1
1
1
2
Sum Sq
Mean Sq
F value
Pr(>F)
132346
132346
7.355
0.113
60903
60903
3.385
0.207
16810
16810
0.934
0.436
35987
17993
aov10<aov(tnpp.annual~Wetland*YS)
summary(aov10)
Df
Wetland
YS
Wetland:YS
Residuals
186
F.2.2 Nutrient Analysis
aov1<-aov(logn~Wet­
land)
summary(aov1)
Df
Wetland
Residuals
1
77
Sum Sq
Mean Sq
F value
Pr(>F)
0.0093
0.009324
0.59
0.445
1.2162
0.015795
aov2<-aov(logp~Wet­
land)
summary(aov2)
Df
Wetland
Residuals
Sum Sq
1
77
0
3.638
Mean Sq
F value
Pr(>F)
0.00017
0.004
0.953
0.04725
0.442
3.527
Mean Sq
F value
Pr(>F)
0.4421
9.652
0.00265 **
0.0458
aov3<-aov(logk~Wet­
land)
summary(aov3)
Df
Wetland
Residuals
Sum Sq
1
77
aov4<-aov(Ca~Wet­
land)
summary(aov4)
Df
Wetland
Residuals
aov6<-aov(srtal~Wet­
land)
>
Wetland
Residuals
Sum Sq
1 8.279e+07
77 1.606e+09
Mean Sq
F value
Pr(>F)
82791300
3.969
0.0499 *
20862048
summary(aov6)
Df
Sum Sq
Mean Sq
F value
Pr(>F)
1
1.407
1.4066
7.642
0.00713 **
77
14.172
0.1841
aov9<-aov(logfe~Wet­
land)
summary(aov9)
Df
Wetland
Residuals
Sum Sq
1
77
0.58
9.711
Mean Sq
F value
Pr(>F)
0.5804
4.603
0.0351 *
0.1261
187
aov10<-aov(Mn~Wet­
land)
summary(aov10)
Df
Sum Sq
Wetland
Residuals
1
77
Mean Sq
167
413500
167
5370
F value
Pr(>F)
0.031
0.861
aov<aov(N~year)
summary(aov)
Df
Sum Sq
year
Residuals
1
2
Mean Sq
3.585
0.995
F value
3.585
0.497
Pr(>F)
7.209
0.115
aov3<-aov(logk~Spe­
cies)
summary(aov3)
Df
Species
Residuals
Sum Sq
8
70
2.529
1.44
Mean Sq
F value
Pr(>F)
0.31613
15.37 8.89e-13
0.02057
***
Mean Sq
F value
Pr(>F)
166229979
32.38 <2e-16
5133274
***
aov17<-aov(Ca~Spe­
cies)
summary(aov17)
Df
Species
Residuals
Sum Sq
8 1.330e+09
70 3.593e+08
aov6<-aov(srtal~Spe­
cies)
summary(aov6)
Df
Species
Residuals
Sum Sq
8
70
4.646
10.933
Mean Sq
F value
Pr(>F)
0.5808
3.719
0.00113 **
0.1562
aov9<-aov(logfe~Spe­
cies)
summary(aov9)
Df
Species
Residuals
Sum Sq
8
70
2.958
7.333
Mean Sq
F value
Pr(>F)
0.3697
3.529
0.00175 **
0.1048
188
F.2.3 Edge Vegetation
aov1<aov(hanpp~Wetland+YS)
summary(aov1)
Df
Sum Sq
Wetland
YS
Residuals
1
1
1
Mean Sq
529
9
225
F value
529
9
225
Pr(>F)
2.351
0.04
0.368
0.874
aov2<aov(hbnpp~Wetland+YS)
summary(aov2)
Df
Wetland
YS
Residuals
1
1
1
Sum Sq
Mean Sq
F value
3861225
3861225
3218436
3218436
357604
357604
Pr(>F)
10.8
9
0.188
0.205
total litterfall
aov1<-aov(talf~wetland)
summary(aov1)
Df
Sum Sq
wetland
Residuals
1
34
55
58540
Mean Sq
54.6
1721.8
F value
Pr(>F)
0.032
0.86
tree aboveground net primary pro­
ductivity
aov<-aov(tnpp~wetland)
summary(aov)
Df
wetland
Residuals
1
4
Sum Sq
Mean Sq
F value
Pr(>F)
0.0236
0.02359
0.177
0.696
0.5341
0.13353
aov<aov(sd~w)
summary(aov)
Df
w
Residuals
Sum Sq
1
22
1.17
1.61
Mean Sq
F value
Pr(>F)
1.1702
15.99
0.000605 ***
0.0732
189
F.3 Chapter 4 Statistical Tables
F.3.1 Gross Primary Productivity
aov1<-aov(gpp~year)
summary(aov1)
Df
year
Residuals
Sum Sq
Mean Sq
F-value
1
0
0
38
970
25.53
Pr(>F)
0
0.997
aov2<-aov(gpp~month)
summary(aov2)
Df
month
Residuals
Sum Sq
Mean Sq
5
468.9
93.78
34
501.1
14.74
F-value
6.364
Pr(>F)
0.000286 ***
aov3<-aov(gpp~species)
summary(aov3)
Df
species
Residuals
Sum Sq
Mean Sq
4
351.2
87.79
35
618.8
17.68
F-value
4.965
Pr(>F)
0.00281 **
aov1<-aov(gpp~solar)
summary(aov1)
Df
solar
Residuals
Sum Sq
Mean Sq
1
283.7
283.66
38
686.4
18.06
F value
15.71
Pr(>F)
0.000315 ***
aov1<-aov(gpp~ambient)
summary(aov1)
Df
ambient
Sum Sq
1
227
Mean Sq
227.05
190
F value
11.69
Pr(>F)
0.00173 **
Residuals
32
621.7
19.43
aov4<-aov(gpp~soil)
summary(aov4)
Df
soil
Residuals
Sum Sq
Mean Sq
F value
1
245.5
245.51
32
603.2
18.85
Pr(>F)
13.02
0.00104 **
aov2<-aov(gpp~wd)
summary(aov2)
Df
wd
Residuals
Sum Sq
Mean Sq
F value
1
18.2
18.21
32
830.5
25.95
Pr(>F)
0.702
0.408
Tukey multiple comparisons of means
95% family-wise confidence level
Fit:
aov(formula = gpp ~ species)
$species
diff
lwr
upr
p adj
pharg-open
7.39705627
1.26151561
13.5325969
0.0115694
scirpus-open
1.54089259
-4.83071609
7.9125013
0.9561739
sparg-open
1.5239843
-6.27961075
9.3275794
0.9797372
typha-open
6.22748877
-0.01538727
12.4703648
0.0508264
scirpus-pharg
-5.85616368
-11.28988982
-0.4224375
0.0293667
sparg-pharg
-5.87307196
-12.93168927
1.1855453
0.1413911
typha-pharg
-1.1695675
-6.45175302
4.112618
0.9679899
sparg-scirpus
-0.01690829
-7.28165992
7.2478433
1
typha-scirpus
4.68659618
-0.86804349
10.2412358
0.1322577
typha-sparg
4.70350447
-2.44860854
11.8556175
0.3409683
191
Tukey multiple comparisons of means
95% family-wise confidence level
Fit:
aov(formula = gpp ~ month)
$month
diff
August-April
lwr
upr
p adj
7.4694196
0.3738395
14.5649998
0.0342913
July-April
10.1216729
3.0260928
17.2172531
0.0017244
June-April
9.3468142
1.8674161
16.8262124
0.007505
May-April
4.2136815
-2.8818987
11.3092616
0.483828
September-April
2.3586492
-5.120749
9.8380474
0.9296991
July-August
2.6522533
-3.1412636
8.4457702
0.7373203
June-August
1.8773946
-4.3803189
8.1351081
0.9424242
May-August
-3.2557382
-9.0492551
2.5377787
0.5435877
September-August
-5.1107704
-11.3684839
1.146943
0.1632857
June-July
-0.7748587
-7.0325722
5.4828548
0.9989606
May-July
-5.9079915
-11.7015084
-0.1144746
0.0434788
September-July
-7.7630238
-14.0207372
-1.5053103
0.0080777
May-June
-5.1331328
-11.3908463
1.1245807
0.1599124
September-June
-6.988165
-13.6779421
-0.298388
0.0363672
September-May
-1.8550323
-8.1127457
4.4026812
0.9451575
192
F.3.2 Respiration
aov7<-aov(resp~year)
summary(aov7)
Df
year
Residuals
Sum Sq
1
38
Mean Sq
5.7
459.9
F-value
5.717
12.102
Pr(>F)
0.472
0.496
aov8<-aov(resp~month)
summary(aov8)
Df
month
Residuals
Sum Sq
5
34
Mean Sq
140.6
325
F-value
28.12
9.559
Pr(>F)
2.942
0.026
aov10<-aov(resp~spe­
cies)
summary(aov10)
Df
species
Residuals
Sum Sq
4
35
Mean Sq
59.1
406.6
F-value
14.76
11.62
Pr(>F)
1.271
0.3
aov4<-aov(resp~solar)
summary(aov4)
Df
solar
Residuals
Sum Sq
1
32
Mean Sq
F value
14.36
14.359
307.63
9.613
Pr(>F)
1.494
0.231
aov6<-aov(resp~ambient)
summary(aov6)
Df
ambient
Residuals
Sum Sq
1
32
Mean Sq
41.31
280.68
F value
41.31
8.77
Pr(>F)
4.71
0.0375
aov9<-aov(resp~soil)
summary(aov9)
Df
soil
Residuals
Sum Sq
1
32
26.09
295.9
Mean Sq
F value
26.088
9.247
Pr(>F)
2.821
0.103
aov7<-aov(resp~wd)
summary(aov7)
Df
wd
Residuals
Sum Sq
1
32
Mean Sq
5.2
316.8
193
F value
5.159
9.901
Pr(>F)
0.521
0.476
Fit:
Tukey multiple comparisons of means
95% family-wise confidence level
aov(formula = resp ~ month)
$month
diff
August-April
July-April
June-April
May-April
September-April
July-August
June-August
May-August
September-August
June-July
May-July
September-July
May-June
September-June
September-May
lwr
-0.5584066
-1.2301503
-0.2714699
3.3138235
3.1461692
-0.6717437
0.2869367
3.8722301
3.7045758
0.9586805
4.5439738
4.3763196
3.5852933
3.4176391
-0.1676542
upr
-6.2728773
-6.944621
-6.2950509
-2.4006472
-2.8774118
-5.3375895
-4.7527527
-0.7936157
-1.3351136
-4.081009
-0.121872
-0.6633699
-1.4543961
-1.9700155
-5.2073437
194
5.156064
4.48432
5.752111
9.028294
9.16975
3.994102
5.326626
8.538076
8.744265
5.99837
9.20982
9.416009
8.624983
8.805294
4.872035
p adj
0.9996711
0.9861333
0.9999928
0.509777
0.618958
0.9978583
0.9999771
0.1511007
0.2558845
0.9920945
0.0599484
0.1196508
0.2884381
0.4110625
0.9999984
F.3.3 Methane
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by year
chi-squared = 2.3775,
df = 1,
p-value = 0.1231
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by month
Chi-squared = 21.349,
df = 5,
p-value = 0.0006958
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by species
chi-squared = 3.5147,
df = 3,
p-value = 0.3189
df = 37,
p-value = 0.181
df = 70,
p-value = 0.4451
df = 34,
P-value = 0.049
df = 28,
p-value = 0.02272
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by solar
chi-squared = 44.657,
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by ambient
chi-squared = 70.9715,
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by soil
Chi-squared = 48.732,
Kruskal-Wallis rank sum test
data:
Kruskal-Wallis
ch4 by depth
chi-squared = 44.8744,
195
p.value:
Comparisons
Multiple comparison test after Kruskal-Wallis
0.05
obs.dif
April-August
April-July
April-June
April-May
April-September
August-July
August-June
August-May
August-September
July-June
July-May
July-September
June-May
June-September
May-September
critical.dif difference
21.5625 32.47013FALSE
37.0625 32.47013TRUE
41.75 32.47013TRUE
22 32.47013FALSE
14.4375 32.47013FALSE
15.5 26.51175FALSE
20.1875 26.51175FALSE
0.4375 26.51175FALSE
7.125 26.51175FALSE
4.6875 26.51175FALSE
15.0625 26.51175FALSE
22.625 26.51175FALSE
19.75 26.51175FALSE
27.3125 26.51175TRUE
7.5625 26.51175FALSE
Wilcoxon rank sum test with continuity correction
data:
ch4[month April] and ch4[month June]
W = 5,
p-value = 0.0003405
alternative hypothesis:
true location shift is not equal to 0
95 percent confidence interval:
-28.87594
sample estimates:
-10.53378
difference in location
Wilcoxon rank sum test with continuity correction
data:
ch4[month April] and ch4[month July]
W = 16,
p-value = 0.003629
alternative hypothesis:
true location shift is not equal to 0
95 percent confidence interval:
-30.159909
sample estimates:
difference in location
196
-6.600953
Wilcoxon rank sum test with continuity correction
data:
ch4[month June] and ch4[month September]
W = 213,
p-value = 0.001449
alternative hypothesis:
true location shift is not equal to 0
95 percent confidence interval:
6.108392
sample estimates:
difference in location
197
21.978579