an assessment of plant community - ohiolink

AN ASSESSMENT OF PLANT COMMUNITY COMPOSITION AND STRUCTURE

OF FORESTED MITIGATION WETLANDS AND RELATIVELY UNDISTURBED

REFERENCE FORESTED WETLANDS IN OHIO

A Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

By

John Edward Reinier, B.S.

Graduate Program in Environment and Natural Resources

*****

The Ohio State University

2011

Master's Examination Committee:

Dr. David M. Hix, Advisor

Dr. Dawn R. Ferris

Dr. P. Charles Goebel

Copyrighted by

John Edward Reinier Jr.

2011

ii

ABSTRACT

The ecological functions provided by mature forested wetlands are continually

being lost through permitted impacts associated with residential, commercial and

agricultural development. In response, regulatory agencies require that forested wetlands

be restored, established, enhanced or preserved (i.e., wetland mitigation) to replace those

ecological functions. Assessments of wetland mitigation projects often focus on plant

community characteristics as indicators of ecological integrity. Using the Vegetation

Index of Biotic Integrity (VIBI) sampling protocol, we collected herbaceous and woody

plant community data as well as physical habitat attribute data at four forested mitigation

areas and four relatively undisturbed reference forested wetlands within the Erie/Ontario

Drift and Lake Plains and Eastern Corn Belt Plains ecoregions of Ohio. The objectives of

the study were to compare the plant community composition and structure of these areas

and to identify factors that may be influencing their development. Our analyses revealed

both compositional and structural differences between mitigation and reference areas and

indicated that certain physical habitat features (e.g., hummocks, macrotopographic

depressions and coarse woody debris) may influence plant community development. Past

land use, disturbance history and landscape setting also likely played an important role in

shaping the plant communities of the sampled areas. Mitigation areas supported fewer

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ecologically-conservative species than reference areas and lacked a subcanopy of native,

shade-tolerant wetland tree and shrub species. VIBI results indicated that reference areas

were characterized by the presence of seedless vascular plants, a high Floristic Quality

Assessment Index score, the importance of native, shade-tolerant species as well as high

percent cover of sensitive species, hydrophytes and bryophytes. Mitigation areas were

characterized by ecologically tolerant species, the importance of canopy tree species, and

many trees in small diameter classes. These results suggest that forested mitigation

wetlands may support plant communities dominated by species with a wide range of

ecological tolerance and may lack the structure that characterizes mature forested

wetlands. Assuming mitigation areas are on a desirable trajectory, several decades may

pass before areas currently used for forested wetland mitigation begin to resemble

relatively undisturbed areas in terms of plant community composition and structure. As

more and more forested wetlands are negatively impacted by residential, commercial and

agricultural development, there will likely be a continual decline in the functions

provided by mature forested wetlands throughout Ohio.

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ACKNOWLEDGMENTS

I owe a great deal of thanks to my family, particularly my wife, Abby, for their

support and patience throughout my academic career. I also want to thank my advisor,

Dr. David Hix for his helpful advice and encouragement over the last two and a half

years. My other two committee members, Dr. Charles Goebel and Dr. Dawn Ferris also

provided helpful comments that improved this research. While working at Ohio EPA, I

learned a great deal about wetland ecology from Mick Micacchion and Brian Gara. They

have both been instrumental in my academic and professional development. I greatly

appreciate all the assistance provided by the faculty and staff of the School of

Environment and Natural Resources. Finally, this research would not have been possible

without the SEEDS grant awarded to me by the Ohio Agricultural Research and

Development Center.

v

VITA

2008................................................................B.S. Conservation, Kent State University

2008 to 2010 .................................................Graduate Teaching Associate, School of

Environment and Natural Resources, The

Ohio State University

2010 to present ..............................................Biologist, U.S. Army Corps of Engineers,

Buffalo District - Orwell, Ohio Field Office

Fields of Study

Major Field: Environment and Natural Resources

Area of Specialization: Forest Science

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TABLE OF CONTENTS

Abstract ............................................................................................................................... ii Acknowledgments.............................................................................................................. iv

Vita ...................................................................................................................................... v

List of Tables .................................................................................................................... iix

List of Figures .................................................................................................................... xi Chapter 1: Introduction ....................................................................................................... 1

Chapter 2: Herbaceous Plant Community Compostion of Forested Mitigation Wetlands and Relatively Undisturbed Reference Forested Wetlands ............................................... 7

Introduction ..................................................................................................................... 8

Study Areas ................................................................................................................... 11

Physiography and Soils ............................................................................................. 12

Methods ......................................................................................................................... 13

Vegetation Data ......................................................................................................... 13

Soil Data .................................................................................................................... 14

Physical Habitat Attributes ........................................................................................ 15

Plant Species Relative Cover Values ......................................................................... 16

Statistical Analyses .................................................................................................... 16

Results ........................................................................................................................... 18

Cluster Analysis ......................................................................................................... 19

Multi Response Permutation Procedure .................................................................... 19

Nonmetric Multidimensional Scaling ........................................................................ 20

Soil Characteristics .................................................................................................... 21


Discussion ..................................................................................................................... 22

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Past Land Use and Disturbance History .................................................................... 24 Physiographic Factors and Landscape Position ......................................................... 24


Conclusions ................................................................................................................... 30

Chapter 3: Woody Plant Community Composition and Structure of Forested Mitigation Wetlands and Relatively Undisturbed Reference Forested Wetlands .............................. 50

Introduction ................................................................................................................... 52

Study Areas ................................................................................................................... 54

Physiography and Soils ............................................................................................. 55

Methods ......................................................................................................................... 56

Vegetation Data ......................................................................................................... 56

Soil data ..................................................................................................................... 57


Vegetation Data Reduction and Metric Calculations ................................................ 58

Statistical Analyses .................................................................................................... 59

Results ........................................................................................................................... 62

Cluster Analysis ......................................................................................................... 63

Multi Response Permutation Procedure .................................................................... 63

Nonmetric Multidimensional Scaling ........................................................................ 63



Discussion ..................................................................................................................... 65

Canopy Composition ................................................................................................. 65

Canopy Structure ....................................................................................................... 68

Subcanopy Composition ............................................................................................ 69

Subcanopy Structure .................................................................................................. 71

Standing Dead Trees .................................................................................................. 75


Conclusions ................................................................................................................... 76

Chapter 4: Using the Vegetation Index of Biotic Integrity to Compare Forested Mitigation Wetlands and Relatively Undisturbed Reference Forested Wetlands ............ 97

Introduction ................................................................................................................... 98

viii

Study Area and Methods ............................................................................................. 100 Vegetation Data Reduction and Metric Calculations .............................................. 101

Statistical Analyses .................................................................................................. 101

Results ......................................................................................................................... 102

VIBI Metrics ............................................................................................................ 102

Nonmetric Multidimensional Scaling ...................................................................... 102

Discussion ................................................................................................................... 103

VIBI Scores ............................................................................................................. 103

Wetland Tiered Aquatic Life Use Designations ...................................................... 106

Conclusions ................................................................................................................. 107 Chapter 5: Applications and Limitations ........................................................................ 120

Bibliography ................................................................................................................... 123

Appendix A: Study Area Descriptions ........................................................................... 123

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LIST OF TABLES

Table Page

2.1 Summary information describing the four mitigation plots and the four relatively undisturbed reference plots………………………………………………………38

2.2 Mean (103) (± 1 standard error) relative cover values for herbaceous species in

mitigation and reference forested wetland plots. Nomenclature based on Andreas et al. (2004)……………………………………………………………………....39

2.3 Mean values of (± 1 standard error) soil characteristics from mitigation and

reference forested wetlands. An asterisk indicates a significant difference in the mean values between mitigation and reference sites (Mann-Whitney test; p < 0.05)………………………………………………………………...……………42

2.4 Mean values (± 1 standard error) of physical attributes from mitigation and

reference forested wetland plots. An asterisk indicates a significant difference in the mean values between mitigation and reference sites (Welch two-sample t-test; * p < 0.05)……………………………………………………………….……….42

3.1 Summary information describing the four mitigation plots and the four relatively

undisturbed reference plots………………………………………………………85 3.2 A description of the Vegetation Index of Biotic Integrity importance value metrics

used for forested wetland evaluation (Mack 2009)………………………………86 3.3 Mean (102) (± 1 standard error) importance values for woody plant species of

mitigation and reference forested wetland VIBI plots. Nomenclature follows Andreas et al. (2004)……………………………………………………….…….87

3.4 Total number of live trees in each diameter class in mitigation and in reference

plots. Plot abbreviations are provided in Table 3.1……………………………..88

x

3.5 Mean values (± 1 standard error) of soil characteristics from mitigation and

reference forested wetlands. An asterisk indicates a significant difference in the mean values between mitigation and reference plots (Mann-Whitney test; p < 0.05)………………………………………………………………….…………..89

3.6 Mean values (± 1 standard error) of physical attributes from mitigation and

reference forested wetland plots. An asterisk indicates a significant difference in the mean values between mitigation and reference plots (Welch two-sample t-test; * p < 0.05)…………………………………………………………………....…..89

4.1 Summary information describing the four mitigation plots and the four relatively

undisturbed reference plots…………………………………………………..…111 4.2 A summary of the VIBI-Forest metrics from Mack (2009)…………….....……112 4.3 A summary of the VIBI-Forest metric values for mitigation and reference plots.

An asterisk indicates a significant difference in the mean metric value between mitigation and reference plots (p < 0.05). A detailed description of the metrics is provided in Table 4.2 and study plot abbreviations are included in Table 4.1. …………………………………………………………………………………..113

4.4 Descriptions of the Wetland Tiered Aquatic Life Use categories (TALUs) from

Mack (2004)………………………………………………………………..….. 114 4.5 A summary of the VIBI-Forest scoring breakpoints for Wetland Tiered Aquatic

Life Use categories (TALUs). LQWLH = limited quality wetland habitat, RWLH = restorable wetland habitat, WLH = wetland habitat and SWLH = superior wetland habitat. A complete description of the TALUs is provided in Table 4.8…………………………………………………………………..……115

4.6 A summary of the ecoregion, VIBI-Forest score, study area type and Tiered

Aquatic Life Use categories that characterize the study plots. A complete description of the Tiered Aquatic Life Use categories is provided in Table 4.8 …………………………………………………………………………………..116

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LIST OF FIGURES

Figure Page

2.1 Locations of the four mitigation plots (black dots) and the four relatively undisturbed reference forested wetland plots (open circles)……….………….…32

2.2 A standard VIBI sampling plot (Mack 2007). The four intensively sampled

subplots (2, 3, 8, 9) are shaded. Small numbered squares in the subplot corners represent the location of nested quadrats used to sample herbaceous vegetation………………………………………………………………….……..33

2.3 Mean diversity values (± 1 standard error) for herbaceous plant species in

mitigation and reference forested wetland plots…………………………...…….34 2.4 Mean species richness (± 1 standard error) for herbaceous plant species in

mitigation and reference forested wetland plots………………….…….………..34 2.5 Dendrogram from a hierarchical cluster analysis using herbaceous plant species

relative cover values in mitigation (black dots) and reference (open circles) plots………………………………….………………………………..………….35

2.6 Nonmetric multidimensional scaling (NMDS) ordination of mitigation sites (black

dots) and reference sites (open circles). Environmental attributes represented by the vectors include: number of hummocks (# hummocks), number of tussocks (# tussocks), number of macrotopographic depressions (# macr. depr.), litter depth, water depth (H2O depth) and counts of coarse woody debris 12-40 cm. in diameter (cwd 12-40 cm) and greater than 40 cm (cwd > 40 cm)…………..…...36

2.7 Nonmetric multidimensional scaling (NMDS) ordination of herbaceous plant

species. Species abbreviations are provided in Table 2.2. Environmental attributes represented by the vectors include: number of hummocks (# hummocks), number of tussocks (# tussocks), number of macrotopographic depressions (# macr. depr.), litter depth, water depth (H2O depth) and counts of coarse woody debris 12-40 cm. in diameter (cwd 12-40 cm) and greater than 40 cm. (cwd > 40 cm)………………………………………………...……………..37

xii

3.1 Locations of the four forested wetland mitigation plots (black dots) and the four relatively undisturbed reference forested wetland plots (open circles)……….…79

3.2 Mean (± 1 standard error) diversity values for woody plant species of mitigation

and reference forested wetland plots……………………………………………..80 3.3 Mean (± 1 standard error) species richness for woody plant species of mitigation

and reference forested wetland plot……………..…………………………….…80 3.4 Mean (± 1 standard error) density of wetland shrubs (number per hectare) of

mitigation and reference plots. There was a significant difference between plot types (Welch two-sample t-test, p = 0.02)…………………………………..…...81

3.5 Mean (± 1 standard error) subcanopy IV metric value of mitigation and reference

plots. There was a significant difference between plot types (Welch two-sample t-test, p = 0.03)………………………………………………………...…………81

3.6 Mean (± 1 standard error) density of wetland trees (stems per hectare) of

mitigation and reference plots. There was no significant difference between plot types (Welch two-sample t-test, p = 0.20)……………………………………….82

3.7 Mean (± 1 standard error) canopy species IV metric value in mitigation and

reference plots. There was no significant difference between plot types (Welch two-sample t-test, p = 0.12)………………………………………………...……82

3.8 Dendrogram from a hierarchical cluster analysis using woody plant species

importance values from mitigation plots (black dots) and reference plots (open circles). The most important canopy tree species in each plot was added to the dendrogram to aid interpretation of the results………………………….……….83

3.9 Nonmetric multidimensional scaling (NMDS) ordination of mitigation plots

(black dots), reference plots (circles) and woody plant species (triangles). Species abbreviations are provided in Table 3.3. Environmental attributes represented by the vectors include: number of hummocks (# hummocks), number of tussocks (# tussocks), number of macrotopographic depressions (# macr. depr.), litter depth, water depth (H2O depth) and counts of coarse woody debris 12-40 cm. in diameter (cwd 12-40) and greater than 40 cm. (cwd > 40)……………….….…..84

4.1 Locations of the four forested wetland mitigation plots (black dots) and the four

relatively undisturbed reference forested wetland plots (open circles)………...108

xiii

4.2 Nonmetric multidimensional scaling ordinations of mitigation and reference

forested wetland plots based on (a) herbaceous species relative cover values and (b) woody plant species importance values. Vectors represent VIBI-Forest metric values. A complete description of the VIBI-Forest metrics is provided in Table 4.2…………………………………………………………………..………..….109

4.3 Mean (± 1 standard error) VIBI-Forest scores for mitigation and reference

forested wetland plots. Means of the two plot types were significantly different (p = 0.01)………………………………………………………………..……….110

1

CHAPTER 1

INTRODUCTION

To offset the loss of ecological functions resulting from impacts to aquatic

resources, the U.S. Army Corps of Engineers (USACE) and U.S. Environmental

Protection Agency (USEPA) require compensatory mitigation for discharges of dredged

or fill material into waters of the United States including wetlands (USACE and USEPA

2008). In response, wetlands must be restored, established, enhanced or preserved to

replace these ecosystem functions. Generally, approved mitigation projects must be

constructed within the same watershed as the impacted site and must be “in-kind,”

meaning that the hydrogeomorphic (Brinson 1993) and (or) ecological characteristics

(Cowardin et al. 1979) should be similar to the impacted site (USACE and USEPA

2008). Often, these requirements are not met (Brown and Veneman 2001, National

Research Council 2001, Cole and Shafer 2002, Kettlewell et al. 2008), resulting in

extended temporal or even permanent loss of wetland functions.

A particularly challenging type of wetland mitigation involves the restoration,

enhancement, or establishment of forested wetlands. Due to the time required to

establish woody vegetation, forested wetland mitigation is considered more challenging

than other types of compensatory wetland mitigation (Niswander and Mitsch 1995,

2

Brown and Veneman 1998, National Research Council 2001). The ecosystem functions

provided by forested wetlands (e.g., water quality enhancement and habitat for flora and

fauna) are directly influenced by the composition and structure of the plant communities.

Therefore, collecting and comparing plant community composition and structure data is

essential for understanding the factors influencing plant community development in both

natural and mitigation wetlands.

Most current research concerning wetland mitigation focuses primarily on the

establishment of wetland plant communities (Balcombe et al. 2005, Spieles 2005, Spieles

et al. 2006). Studies that assess vegetation establishment typically use percent cover of

hydrophytic vegetation, plant species richness and evenness, and percent cover of non-

native species for mitigation evaluation. In addition, floristic quality indices such as the

Floristic Quality Assessment Index for vascular plants and mosses for the State of Ohio

(Andreas et al. 2004), which incorporate the ecological tolerances of species into plant

community evaluation, have increasingly been used as a measure of overall success

(Mack and Micacchion 2006, Spieles et al. 2006). When forested wetlands are the focus,

the plant community attributes used to evaluate non-forested wetlands may not provide

accurate assessments. For example, using a diversity index as a measure of success may

not be appropriate for forested wetlands in Ohio because as Mack (2009) noted, forest

subcanopies tend to be low-energy systems, therefore significant differences in diversity

measures may not be expected. Furthermore, increases in diversity that may be desirable

for non-forested ecosystems are often indicative of significant disturbance in forests

(Mack 2009).

3

Results of mitigation assessments are mixed, but considerable data supports the

idea that wetland mitigation projects, particularly projects meant to restore, establish or

enhance forested wetlands, are not replacing the plant community composition and

structure lost following impacts (Brown and Veneman 2001, Cole and Shafer 2002,

Hoeltje and Cole 2007, 2009, Kettlewell et al. 2008). For instance, a study of mitigation

banks throughout the United States found significant variability in the development of

wetland vegetation but also identified a potential trend toward similarity with natural

wetlands (Spieles 2005). An ecological assessment of Ohio mitigation banks completed

by the Ohio Environmental Protection Agency (OEPA) found that of the 400 hectares

assessed, 25 percent was not wetland as defined in the U.S. Army Corps of Engineers

1987 Wetland Delineation Manual, and of the remaining wetland area, 25 percent was

considered “poor” quality (Mack and Micacchion 2006). In an Ohio study of the

effectiveness of compensatory mitigation in the Cuyahoga River watershed, forested

wetlands within the watershed were replaced at the lowest ratio among the wetland types

evaluated (Kettlewell et al. 2008). A similar trend was found by Cole and Shafer (2002)

in their study of wetland mitigation projects in Pennsylvania.

In Ohio, there is a pressing need to evaluate forested mitigation areas both as

components of mitigation banks and as individual projects. There is considerable interest

in learning both the current status of plant community development within these areas

and how mitigation designs can be improved in order to maintain important ecological

functions. The goal of this research was to gain a better understanding of the plant

community composition and structure of forested wetland mitigation areas in the

4

Erie/Ontario Drift and Lake Plains and Eastern Corn Belt Plains ecoregions of Ohio and

to describe how they differ from mature, natural, reference forested wetlands . For the

purposes of this study, reference forested wetlands are those that have developed in the

absence of direct human disturbance for many years and that are capable of supporting

and maintaining a high-quality plant community (Mack 2004). Additionally, we

investigated the role that certain physical habitat attributes may play in shaping the plant

communities of forested wetlands and discuss plant community development as it relates

to physiographic factors, past land use and disturbance history.

5

REFERENCES

Andreas, B. K., J. J. Mack and J. S. McCormac. 2004. Floristic quality assessment index (FQAI) for vascular plants and mosses for the state of Ohio. Ohio Environmental Protection Agency, Division of Surface Water, Wetland Ecology Group, Columbus, Ohio.

Balcombe, C.K., J.T. Anderson, R.H. Fortney, J.S. Rentch, W.N. Grafton and W.S. Kordek. 2005. A comparison of plant communities in mitigation and reference wetlands in the mid-Appalachians. Wetlands 25:130-142.

Brinson, M. M. 1993. A hydrogeomorphic classification for wetlands. U.S. Army Corps of Engineers Waterways Experiment Station. Wetlands Research Program Technical Report. WRP-DE-4.

Brown, S.C. and P.L.M. Veneman. 2001. Effectiveness of compensatory wetland mitigation in Massachusetts, USA. Wetlands 21:508-518.

Cole, C.A. and D. Shafer. 2002. Section 404 wetland mitigation and permit success criteria in Pennsylvania, USA, 1986-1999. Environmental Management. 30:508-515.

Cowardin, L. M., V. Carter, F. C. Golet and E. T. LaRoe. 1979. Classification of wetlands and deepwater habitats of the United States. Department of the Interior, Fish and Wildlife Service, Washington D.C.

Hoeltje, S.M. and C.A. Cole. 2007. Losing function through wetland mitigation in central Pennsylvania, USA. Environmental Management. 39:385-402.

Hoeltje, S.M. and C.A. Cole. 2009. Comparison of function of created wetlands of two age classes in central Pennsylvania. Environmental Management. 43:597-608.

Kettlewell, C.I., V. Bouchard, D. Porej, M. Micacchion, J.J. Mack, D. White and L. Fay. 2008. An assessment of wetland impacts and compensatory mitigation in the Cuyahoga River Watershed, Ohio, USA. Wetlands 28:57-67.

Mack, J.J. 2009. Development issues in extending plant-based IBIs to forested wetlands in the Midwestern United States. Wetlands Ecology and Management. 17:117-130.

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Mack, J. J. and M. Micacchion. 2006. An ecological assessment of Ohio mitigation banks: vegetation, amphibians, hydrology, and soils. Ohio EPA Technical Report WET/2006-1. Ohio Environmental Protection Agency, Division of Surface, Wetland Ecology Group, Columbus, Ohio.

National Research Council. 2001. Compensating for Wetland Losses Under the Clean Water Act. National Academy Press, Washington D.C.

Niswander, S.F. and W.J. Mitsch. 1995. Functional analysis of a 2-year-old created in-stream wetland: Hydrology, phosphorus retention, and vegetation survival and growth. Wetlands 15:212-225.

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., M. Coneybeer and J. Horn 2006. Community structure and quality after 10 years in two central Ohio mitigation bank wetlands. Environmental Management. 38:837-852.

U.S. Army Corps of Engineers (USACE) and U.S. Environmental Protection Agency (USEPA). 2008. Compensatory Mitigation for Losses of Aquatic Resources; Final Rule. Federal Register 73.

7

CHAPTER 2

HERBACEOUS PLANT COMMUNITY COMPOSITION OF FORESTED

MITIGATION WETLANDS AND RELATIVELY UNDISTURBED REFERENCE

FORESTED WETLANDS

Abstract

Herbaceous understory plant species are important components of forest

ecosystems and can be used as indicators of the biological integrity of forested wetlands.

Understanding how herbaceous plant communities are currently developing in forested

mitigation wetlands is important for assessing the functions and values of these areas and

for developing improved forested wetland restoration, enhancement and establishment

techniques. We examined the herbaceous plant community compositions and physical

habitat attributes of four forested mitigation wetlands and four relatively undisturbed

reference forested wetlands in the Erie/Ontario Drift and Lake Plains and Eastern Corn

Belt Plains ecoregions of Ohio. Our objectives were to compare the herbaceous plant

communities of mitigation and reference forested wetlands and identify factors that may

be influencing plant community development in these areas. Multi-response permutation

procedure results indicated that the herbaceous plant community compositions of

mitigation and reference plots were significantly different (p = 0.03). Cluster analysis

8

and nonmetric multidimensional scaling ordination (NMDS) revealed greater similarity

in herbaceous species composition among reference plots than among mitigation plots.

NMDS also indicated that reference plots were positively associated with several

physical habitat attributes (e.g., hummocks, tussocks, coarse woody debris and

macrotopographic depressions). These associations suggest that the environmental

heterogeneity provided by certain physical habitat attributes may influence overall

herbaceous community composition in forested wetlands, and that incorporating physical

habitat features into mitigation designs may encourage the development of a herbaceous

plant community resembling that of relatively undisturbed forested wetlands.

Disturbance history, physiographic factors and landscape position also influence plant

community development and should be important considerations during the design of

wetland mitigation projects.

Introduction

Prior to European settlement, extensive tracts of forested wetlands were common

within the deciduous forests of Ohio. Within the Beech-Maple Forest Region of Ohio,

elm-ash-maple forests typically occupied depressions and intermorainal flats (Braun

1961, Gordon 1969, Andreas 1989). Following European settlement of the state, much of

the forest including forested wetlands was cleared to harvest timber, make the land

suitable for agriculture (Gordon 1969), and later, for commercial and residential

developments. Not only were forests subjected to human-induced, major disturbances

that resulted in the removal of many of the canopy trees, but environmental attributes

9

such as soil structure and hydrologic regime were drastically altered by plowing and

draining sites that were too wet for agriculture and urban development.

Ohio is now among a list of 22 states that have lost a majority of their wetland

resources (Dahl 1990). Although all of the historical impacts on Ohio’s forested

wetlands will likely never be mitigated, since 1972, efforts of state and federal regulatory

agencies have been focused on protecting the remaining wetland resources under Section

404 of the Clean Water Act. As compensation for wetland losses resulting from

permitted activities, The U.S. Army Corps of Engineers requires mitigation (i.e., the

restoration, establishment, enhancement or in some cases preservation of wetlands) as a

means of restoring and maintaining the chemical, physical and biological integrity of the

Nation’s waters, including wetlands (USACE and USEPA 2008). Wetland mitigation

focuses on the replacement of ecological functions (e.g., water quality enhancement and

habitat for flora and fauna) provided by wetland ecosystems (USACE and USEPA 2008).

However, the unique habitats provided by natural forested wetland ecosystems are often

not replaced due to mitigation that is not “in-kind,” meaning that the hydrogeomorphic

(Brinson 1993) and (or) ecological characteristics (Cowardin et al. 1979) are different

from the impacted wetlands they are meant to replace. Yet, forested wetlands continue to

be impacted at rates equal to or higher than other wetland types (Porej 2003). The result

is a net decrease in forested wetlands in Ohio and elsewhere.

Replacing forested wetlands with other wetland types does not adequately

compensate for the loss of habitat. In addition to concerns over sensitive faunal taxa that

inhabit forested wetlands in Ohio (e.g., amphibians (Pfingston 1998)), the declining

10

populations of many native herbaceous understory plants should be a serious concern for

anyone familiar with Ohio’s native plant communities. Herbaceous understory plant

species are responsible for much of the overall plant species diversity in temperate forest

landscapes (Gilliam and Roberts 2003), and the wetland components of these landscapes

offer a unique set of environmental conditions. As a result, many herbaceous plant

species unique to forested wetlands are considered threatened or endangered (Ohio

Department of Natural Resources 2011).

There continues to be discussion among regulators, scientists and others about

how to successfully mitigate impacts to Ohio’s forested wetlands and how to encourage

the development of plant communities similar to those found under natural, less disturbed

conditions. As residential and commercial development continues in areas that have

already lost much of their forest, there is an urgent need to understand the mechanisms

influencing the plant community composition of Ohio’s forested mitigation wetlands and

to develop sound techniques that can be used in designing future mitigation areas. To

address this need, we examined the herbaceous plant community composition and

physical habitat attributes (e.g. microtopographic features) of both relatively undisturbed

forested wetlands and mitigation areas in the Erie/Ontario Drift and Lake Plains and

Eastern Corn Belt Plains ecoregions (Woods et al. 1998) of Ohio. The relatively

undisturbed areas represent reference standards that are important for mitigation wetland

assessment (Brinson and Rheinhardt 1996). In this chapter, our objectives are to: 1)

compare the herbaceous plant community composition of forested mitigation wetlands

and relatively undisturbed forested wetlands and 2) identify factors that may be

11

influencing the current development of the herbaceous plant communities in both types of

forested wetlands.

Study Areas

A total of eight study areas (Figure 2.1, Table 2.1) were located within the

Erie/Ontario Drift and Lake Plains (EOLP) and Eastern Corn Belt Plains (ECBP)

ecoregions of Ohio (Woods et al. 1998). The study areas were confined to these

ecoregions to reduce the effect of major climatic and physiographic differences compared

with adjacent ecoregions. Both ecoregions fall within the Beech-Maple Forest Region

described by Braun (1950). The Wisconsinan glacial deposits of this region support a

variety of lakes, streams and wetlands, and forested wetland plant communities in the

region are typically dominated by the following canopy tree species and genera:

American elm (Ulmus americana), ash (Fraxinus spp.), silver maple (Acer saccharinum),

and red maple (Acer rubrum) (Braun 1950,1961, Gordon 1969, Andreas 1989).

Four study areas identified as representing relatively undisturbed reference

forested wetlands were randomly selected from a database provided by the Ohio

Environmental Protection Agency’s Wetland Ecology Group (OEPA). The four

reference plots were located in low-lying natural depressions. The four randomly

selected mitigation areas were in relatively flat areas or depressions where the restoration,

establishment or enhancement (USACE and USEPA 2008) of forested wetlands had

occurred. All eight study plots were classified as palustrine forested wetlands according

12

to the Classification of Wetlands and Deepwater Habitats of the United States (Cowardin

et al. 1979).

Three of the mitigation plots were located within approved mitigation banks and

the fourth is within a “consolidated” mitigation area that includes several individual

mitigation projects constructed according to Clean Water Act, Section 404 permits.

Banks are different from consolidated areas because they are large areas where wetland

restoration, establishment or enhancement activities are planned and approved by the

Interagency Review Team before being used for any specific compensatory mitigation

(USACE and USEPA 2008). Once approved, bank credits become available to anyone

needing to purchase mitigation acreage to compensate for permitted wetland impacts.

Currently, mitigation banks are the preferred mitigation option of federal regulatory

agencies (USACE and USEPA 2008).

Physiography and Soils

Six of the sample plots were located in the EOLP ecoregion that is characterized

as an area with a variety of lakes, streams and wetlands in locations where the land is flat

and soils are clayey (Woods et al. 1998). More specifically, two sample plots were

located in the Mosquito Creek/Pymatuning Lowlands sub-ecoregion that is characterized

by poorly drained soils and wet-mesic forests (Woods et al. 1998). The other four EOLP

plots were located in the Low Lime Drift Plain sub-ecoregion that is characterized by low

rolling hills and end moraines with scattered kettle lakes (Woods et al. 1998). The soil

13

map units associated with the sampling locations (Table 2.1) in the EOLP are in a range

of drainage classes from very poorly drained to well-drained (Soil Survey Staff 2011).

Two sample plots were located in the Eastern Corn Belt Plains (ECBP) ecoregion

that is characterized as a rolling till plain with local end moraines (Woods et al. 1998).

One ECBP plot (Alum Creek) was within the Loamy, High Lime Till Plains sub-

ecoregion which contains soils that developed from loamy, limy Wisconsinan glacial

deposits. The other ECBP plot (Big Island) was within the Clayey, High Lime Till Plains

sub-ecoregion with soils that contain more clay content than the adjacent Loamy, High

Lime Till Plains. The soil map units associated with these two sample plots (Table 2.1)

are described as somewhat poorly drained or very poorly drained (Soil Survey Staff

2011).

Methods

Vegetation Data

With the goal of comparing the herbaceous plant community compositions of

forested wetlands in Ohio, it was important to use a method that has been previously

applied for mitigation project evaluation (Mack and Micacchion 2006, Micacchion and

Gara 2008, Micacchion et al. 2010). To allow for direct comparisons, the Vegetation

Index of Biotic Integrity for Wetlands v1.4 (VIBI), a vegetation-based wetland

assessment tool developed by the OEPA (Mack 2007b) was used to characterize plant

communities. All vegetation data were collected between June 15 and September 15,

14

2009. The VIBI sampling method is based on the releve method for vegetation sampling

described by Peet et al. (1998).

At each study area, a standard VIBI plot (Figure 2.2) was established. Each plot

was 20 meters wide by 50 meters long and divided using measuring tapes into 10-meter

by 10-meter subplots marked with stake flags. The compass bearing of the center line

was recorded and each plot corner was georeferenced using a Garmin 60 CSx handheld

GPS unit.

The percent cover of all plant species less than one meter tall was determined in

each of the four intensive subplots (2, 3, 8, and 9) (Figure 2.2). Any species encountered

in the six remaining subplots that did not occur in subplots 2, 3, 8, and 9 (i.e., new

species) was assigned a cover value based on the total area of the six subplots that were

not sampled intensively. Coverage was visually estimated using the following ten cover

classes: 1 (solitary/few), 2 (0-1% cover), 3 (1-2% cover), 4 (2-5% cover), 5 (5-10%

cover), 6 (10-25% cover), 7 (25-50% cover), 8 (50-75% cover), 9 (75-90% cover) and 10

(90-99% cover). Species level identification according to Voss (1971, 1985, 1996) was

recorded for most vascular plant species encountered. Mosses were identified only as

“bryophytes.” Any vascular plant that was not able to be confidently identified in the

field was collected, pressed, mounted on herbarium-grade paper and identified at a later

time. The complete methodology used to sample the vegetation in this study is described

in the Field Manual for the Vegetation Index of Biotic Integrity for Wetlands v1.4 (Mack

2007b).

15

Soil Data

A soil sample was taken to a depth of 12 cm, using a bucket auger, at the center of

each plot as stated in the VIBI field manual (Mack 2007b). The soil samples were placed

in bags, labeled and stored in a freezer until analyses could be conducted. The samples

were submitted to the Ohio State University’s Service Testing and Research (STAR)

Laboratory located at the Ohio Agricultural Research and Development Center (OARDC)

campus in Wooster, Ohio on October 30, 2009. Results of the analyses were received on

December 3, 2009. The samples were analyzed for soil reaction (pH), cation exchange

capacity (CEC), total phosphorus (P), total potassium (K), total calcium (Ca), total

magnesium (Mg), percent calcium (%Ca), percent magnesium (%Mg), percent potassium

(%K), percent nitrogen (%N), percent carbon (%C), and percent organic matter (%OM)

(STAR Lab 2011).

Physical Habitat Attributes

Physical habitat attribute data were collected in subplots 2, 3, 8 and 9. The

physical attributes recorded were: depth of standing surface water, litter depth, number of

tussocks, number of hummocks, number of macrotopographic depressions, a count of

coarse woody debris 12-40 cm in diameter, and a count of coarse woody debris greater

than 40 cm in diameter. Water and litter depth were determined using a measuring tape.

16

Plant Species Relative Cover Values

The relative cover of each plant species encountered in the sample plot was

calculated using an Excel spreadsheet designed specifically for VIBI data analysis (Mack

2007a). For each plot, the midpoints of the cover classes assigned to each species were

summed. Next, the total cover per species was summed to yield the total cover of all

species in the plot. Finally, the total cover of each species was divided by the total cover

of all species to yield the relative percent cover for each species. The formula used for

this calculation is:

Relative cover = ∑Ai / ∑Aij

where Ai = the cover class midpoints for a species, and Aij = the total cover of species Ai ,

Aj , etc. (Mack 2007b). A detailed description of the method used to calculate relative

cover is also provided in the VIBI field manual (Mack 2007b).

Statistical Analyses

Species richness, evenness (Pielou’s J) (Pielou 1969), Shannon-Weiner

index and Simpson’s index were calculated using the PC-ORD statistical software

program (McCune and Mefford 1999). Differences in the means of these diversity

measures between mitigation and reference plots were determined using Welch’s two-

sample t-test. A significance level of 0.05 was used to determine if differences between

groups were statistically significant.

In order to describe the similarity with respect to herbaceous plant communities,

an agglomerative hierarchical clustering technique based on a Euclidean dissimilarity

17

matrix was used to compare species cover values between mitigation and reference

forested wetland plots. Ward’s linkage method was used because of its compatibility

with Euclidean distances (McCune and Grace 2002). Cluster analysis is a useful way of

identifying groups when working with ecological data and has been used for many years

in community ecology (McCune and Grace 2002). The analysis was completed using the

PC-ORD statistical software program (McCune and Mefford 1999). The dendrogram

used to interpret the cluster analysis was scaled using Wishart’s (1969) objective function

which measures the amount of information lost at each step of the clustering procedure.

Multi-Response Permutation Procedure (MRPP) (Meilke and Berry 2001) was

used to test the null hypothesis of no significant differences between mitigation and

reference plots. A significance level of 0.05 was used to determine if the compositional

differences between groups were statistically significant. MRPP is a nonparametric

procedure that is useful for testing for significant differences between two or more

groups. It is an appropriate method to use with ecological community data that often do

not meet distributional assumptions required of parametric tests that address similar

questions (McCune and Grace 2002). Euclidean distance was used and groups were

defined based on classification as a mitigation plot or as a reference plot.

Nonmetric multidimensional scaling analysis (NMDS) was used to examine plant

community composition and species assemblages as they relate to physical environmental

attributes. NMDS is generally the most effective ordination method to use on ecological

data sets and its advantages include: (1) avoiding assumptions of linear relationships

among variables, (2) relieving the “zero truncation problem” associated with all

18

ordinations of community data sets, and (3) allowing the use of any distance measure or

relativation (McCune and Grace 2002). NMDS uses an iterative search to converge on a

solution that minimizes “stress” in the configuration. Stress is a function of a linear

monotone transformation of observed dissimilarities and ordination distances. The

NMDS procedure was performed using the VEGAN metaMDS function (Oksanen et al.

2010) in the R statistical computing program (R Development Core Team 2009). NMDS

allows for an indirect examination of how environmental factors may be influencing

community composition by fitting vectors representing environmental variables onto the

ordination using the envfit function (Oksanen et al. 2010).

Differences in soil chemical characteristics were examined using Mann-Whitney

nonparametric tests of group differences and a Welch two-sample t-tests were used to test

for significant group differences in physical habitat attributes. Methods of analysis were

chosen following Shapiro-Wilk tests for normality. A significance level of 0.05 was used

to evaluate if differences between groups were statistically significant.

Results

A total of 113 herbaceous species and genera were recorded in the sample plots

(Table 2.2). In mitigation plots, the most common herbaceous species based on mean

relative cover values included Lysimachia nummularia, Toxicodendron radicans, Bidens

frondosa, Lemna minor, Utricularia vulgaris and Carex lurida. The highest relative

covers in reference plots were recorded for Osmunda cinnamomea, moss spp., Osmunda

regalis and Glyceria striata. Welch’s two-sample t-tests indicated there were no

19

significant differences in species richness, evenness, Shannon-Wiener Index value or

Simpson diversity value between mitigation and reference plots (p > 0.05) (Figures 2.3

and 2.4).

Cluster Analysis

The cluster analysis dendrogram (Figure 2.5) shows that all four reference plots

were grouped together at distance 0.14 and before 15% percent of the available grouping

information was used. Trumbull Creek (TC) was grouped with the reference plots with

approximately 30% of the grouping information used. At distance 0.45 and with just

under 50% of the grouping information used, the Hale Road (HR) plot was added to the

group. The two remaining mitigation plots, Big Island (BI) and Columbia Station (CS),

were not grouped with the others until distance 0.94 and with almost 100% of the

grouping information used.

Multi Response Permutation Procedure

MRPP analysis indicated that there was a significant difference in herbaceous

species composition between mitigation and reference plots (p = 0.03). The chance-

corrected within group agreement (A), which is a description of the effect size that is

independent of the sample size, was 0.06. According to McCune and Grace (2002), A

values in community ecology are commonly below 0.1.

20

Nonmetric Multidimensional Scaling

Two convergent NMDS solutions were confirmed after three tries with a final

stress value of 5.98. Four of the environmental vectors that were fit to the ordination

(water depth, number of hummocks, number of tussocks, and number of

macrotopographic depressions) were statistically significant based on 999 permutations

of the data (p < 0.05) (Oksanen 2011). The vectors representing environmental gradients

(Figure 2.6) point in the direction of the most rapid change in each variable (Oksanen

2011).

The ordination plots depict site and species groupings (Figures 2.6 and 2.7).

Some species are grouped very closely near plot positions (Figures 2.6 and 2.7),

indicating that those species occurred either exclusively in particular plots or had very

high cover values relative to the corresponding values that were recorded for other plots.

For example, Trientalis borealis, Coptis trifolia, Rubus hispidus and Carex seorsa are all

very closely grouped near the position of Pallister State Nature Preserve (PA).

Additionally, Lysimachia nummularia, Apocynum cannabinum, Carex muskingumensis

and Asclepias incarnata are among species grouped near the position of the Big Island

(BI) mitigation plot. Species that occur in multiple plots and (or) have relatively

comparable cover values across different plots are located between plot locations in

ordination space. An example is Osmunda regalis which occurred commonly in both the

PA and Brown’s Lake Bog State Nature Preserve (BB) plots and is situated between the

two plot locations in ordination space (Figures 2.6 and 2.7).

21

All four reference plots and one mitigation plot (TC) were positively associated

with vectors representing the number of hummocks, number of tussocks, number of

macrotopographic depressions, litter depth, and counts of coarse woody debris greater

than 12 cm. (Figure 2.6). Three of the four mitigation plots were negatively associated

with these same vectors. One mitigation plot (HR) had a strong positive association with

water depth (Figure 2.6).

Soil Characteristics

The distributions of all soil characteristics were determined to be non-normal

using Shapiro-Wilk tests. Consequently, the mean value for each soil characteristic was

subjected to a Mann-Whitney nonparametric test of group differences. Mean values for

cation exchange capacity (p = 0.03) and total phosphorus (p = 0.03) were significantly

higher in the reference plots than in the mitigation plots (Table 2.3). There were no

significant differences found for any of the other soil characteristics.


The mean number of hummocks (p = 0.08), number of tussocks (p = 0.17),

number of macrotopographic depressions (p = 0.07), count of coarse woody debris > 40

cm in diameter (p = 0.19), litter depth (p = 0.23) and water depth (p = 0.39) were all

compared using Welch two-sample t-tests (Table 2.4). Only one attribute, the mean

count of coarse woody debris 12-40 cm in diameter, was significantly higher in reference

plots (p = 0.04).

22

Discussion

The results of this study highlight differences in the herbaceous plant community

composition of mitigation and reference forested wetlands in two ecoregions of Ohio and

revealed trends important for understanding wetland plant community development in

these ecoregions. MRPP analysis indicated that, overall, mitigation and reference plots

had significantly different herbaceous plant species compositions. Cluster analysis and

NMDS ordinations indicated a higher degree of similarity among reference plots than

among mitigation plots (Figures 2.5 and 2.6). Furthermore, ordination revealed several

tight species groupings which indicate that many species were unique to a single plot or

had a relatively high cover value in a particular plot (Figure 2.7). No significant

differences in species richness, evenness or diversity values were found, unlike the results

from other research comparing plant communities of non-forested natural and mitigation

wetlands (Balcombe et al. 2005). However, as noted by Mack (2009), forest subcanopies

tend to be low-energy systems compared with non-forested areas, therefore, significant

differences in diversity values may not be expected.


Many different factors may influence herbaceous plant community composition in

forest ecosystems including disturbance, physical factors (soil, microtopography),

biological processes (competition, colonization), and history (land use, successional age)

(Beatty 2003). In this study, we used the physical habitat attributes measured as part of

the VIBI protocol (Mack 2007b) as indicators of habitat complexity and

23

microtopographic heterogeneity. The importance of microtopography for plant

community development has been well-documented (Beatty 1984, Huenneke and Sharitz

1986, Titus 1990, Vivian-Smith 1997, Lee and Sturgess 2001, Anderson and Leopold

2002, Alsfeld et al. 2009, Duberstein and Conner 2009, Blood and Titus 2010).

In forested wetlands, there is often a wide variety of microsite types on the forest

floor including woody debris, tree trunks, litter- and moss-covered sediments and various

types of soil substrates (Ehrenfeld 1995). The presence and abundance of these

microsites has been shown to influence plant community development in forested

wetlands (Huenneke and Sharitz 1986, Blood and Titus 2010, Duberstein and Conner

2009). Consequently, microtopography has been a focus of several wetland restoration

projects (Rossell and Wells 1999, Bruland and Richardson 2005, Alsfeld et al. 2009,

Rossell et al. 2009).

The NMDS ordination revealed that all four undisturbed reference plots were

positively associated with hummocks, tussocks, macrotopographic depressions, coarse

woody debris and litter depth (Figure 2.6). These features were lacking in mitigation

plots (Figure 2.6, Table 2.4). This suggests that more mature, relatively undisturbed

forested wetlands may provide more microsites and therefore greater habitat complexity

which may favor the establishment and persistence of particular herbaceous plant species.

The physical attributes we measured may affect rates of litter accumulation, soil redox

potential, and soil moisture levels (Ehrenfeld 1995), and ultimately affect processes such

as seed accumulation, germination requirements and plant growth and mortality which

are critical to plant community development (Huenneke and Sharitz 1986). Some

24

physical habitat attributes may be critical for conservative plant species that have a

narrow range of ecological tolerances. Indeed, several ecologically conservative species

(e.g., Osmunda cinnamomea, Osmunda regalis, Glyceria septentrionalis) were associated

with physical habitat attributes that characterized reference plots (Figure 2.6) (Andreas et

al. 2004).

Past Land Use and Disturbance History

For both mitigation and reference plots, plant community composition and

physical habitat characteristics can be better understood by considering past land use and

disturbance history. Sampling for this study occurred in areas representing a range of

disturbance histories from relatively undisturbed areas to those recovering from recent

major disturbance (i.e., land clearing for agriculture). The BI and HR mitigation plots

were located in areas that were used for agricultural production just prior to wetland

restoration treatments (Envirotech Consultants, personal communication 2011; Vince

Messerly, personal communication 2011). Consequently, plant community development

in these areas has likely been affected by the lack of environmental heterogeneity that is

typical of post-agricultural landscapes (Beatty 2003, Flinn 2007).

Human-induced major disturbances often result in the removal of all native

vegetation and the elimination of the existing soil structure and microtopographic

features that are important for habitat heterogeneity. This reduction in environmental

heterogeneity and its effect on plant communities has been illustrated by the bryophyte

community compositions (Ross-Davis and Frego 2002) and fern recruitment (Flinn 2007)

25

found following human-induced disturbance. The development of microtopographic

features in heavily disturbed areas may only occur after a mature forest develops and then

experiences minor disturbances resulting in tree mortality and their eventual decay on the

forest floor. The resulting structure is a legacy of disturbance that lasts for hundreds of

years following the disturbance event itself (Lindenmayer and Franklin 2002, Beatty

2003). Furthermore, the formation of hummocks may require disturbance that creates

organically-derived structures (e.g., coarse woody debris and soil aggregates)

(Lindenmayer and Franklin 2002), followed by an accumulation of sediment and organic

material over the course of many years (Ehrenfeld 1995).

Several species adapted to full sun conditions (e.g., Scirpus cyperinus, Scirpus

atrovirens, Juncus effusus, Apocynum cannabinum, and Asclepias incarnata) (Mack

2007a) characterize the BI and HR mitigation plots (Figure 2.6 and 2.7). These species

may be biological legacies of the open, emergent or scrub-shrub wetlands that likely

occupied these plots in the relatively recent past. The presence of these early

successional species would be expected to decline over time as overall species diversity

changes in response to competition intensity, microclimate development, increases in

horizontal and vertical heterogeneity and sensitivity to disturbance (Gilliam and Roberts

2003). As these young forests transition from the stem exclusion stage to the understory

reinitiation stage of forest development (Oliver and Larson 1996), which takes several

decades, a herbaceous layer similar to those in the reference plots may begin to develop.

Two mitigation plots, TC and CS, were in mitigation areas with an existing,

relatively mature forest canopy at the time of treatment and may represent a less-

26

disturbed condition than the other two mitigation plots, BI and HR. The presence of a

diverse hummock-hollow forest prior to treatment at the TC mitigation area was noted

during an Ohio Environmental Protection Agency study of mitigation banks (Mack and

Micacchion 2006). Cluster analysis and NMDS ordination indicated some similarity

between the Trumbull Creek (TC) mitigation plot and reference plots (Figures 2.5 and

2.6). Ordination also indicated that this plot was positively associated with some

physical habitat attributes that characterized reference plots. In the TC mitigation area,

the existing hummock-hollow forest with pockets of wetland was hydrologically

manipulated through dam and berm construction in an attempt to create more wetland

habitat (Mack and Micacchion 2006, Vince Messerly, personal communication 2011).

As a result of increased water retention at the site, many of the existing canopy trees were

stressed and (or) killed, but some legacies (e.g., shade-tolerant herbaceous species and

hummocks) of the forest that existed prior to treatment survived (personal observation).

This may explain the association of TC with several herbaceous species (e.g.,

Maianthemum canadense, Lycopus rubellus, Scutellaria lateriflora) that were also

associated with reference plot locations in the ordination (Figures 2.6 and 2.7). However,

the increased canopy openness and hydrologic inundation in the TC forest may have

encouraged the establishment of herbaceous plant species, e.g., Carex lurida, Carex

scoparia, Penthorum sedoides, Eupatorium perfoliatum, and Sparganium americanum

(Figure 2.6 and 2.7), that are commonly found in emergent wetlands that receive full sun

(Mack 2007a). The trajectory of this plot is difficult to assess, but the herbaceous plant

27

community appears to be transitioning to one that is dominated by shade-intolerant

species.

Mack (2009) highlighted the difficulty in assessing forested wetland condition in

areas such as TC where disturbance affects the herbaceous plant community while the

canopy remains somewhat intact. Our result highlights the caution that must be used

when manipulating hydrology in mitigation wetlands and supports the conclusions of

other studies that found inundation to be greater in areas where hydrology was

manipulated for the purpose of creating wetlands (Cole et al. 2006, Gamble and Mitsch

2009). Additionally, our NMDS ordination indicated that the Hale Road (HR) plot was

highly associated with deeper water (Figure 2.6), and this result is corroborated by the

strong association of species that tolerate considerable inundation (e.g., Scirpus

atrovirens, Carex vulpinoidea, Lemna minor, Utricularia vulgaris) (personal

observation) with this plot (Figures 2.6 and 2.7). Our results further support the

suggestion that human manipulation of hydrology can have strong influences on the plant

community development of mitigation areas.

Neither cluster analysis nor NMDS ordination suggested that mitigation areas that

had a relatively mature canopy at the time of treatment were more similar to each other

than they were to young forested areas developing on post-agricultural land. Our results

illustrate considerable dissimilarity among mitigation areas and indicate that the

herbaceous understory communities of the young forests (BI and HR) were more similar

to mitigation plots with relatively mature trees (CS and TC) than they were to each other

(Figure 2.5).

28

The variability among mitigation plots may be suggestive of plant communities

that are adjusting to changing environmental conditions. The relatively similar

composition of reference plots (Figures 2.5 and 2.6) indicates that these forests are more

stable than those of mitigation plots. Also, ordination indicates that reference plots may

have well-established biological legacies (i.e., organically-derived structures

(Lindenmayer and Franklin 2002)) that may provide important habitat for some plant

species. These microsites may have provided the environmental conditions preferred by

ecologically conservative herbaceous understory plants (e.g., Carex bromoides, Carex

canescens, Carex tuckermanii, Dryopteris cristata, Coptis trifolia, Trientalis borealis)

(Andreas et al. 2004) that occurred exclusively in reference plots (Table 2.2).

Physiographic Factors and Landscape Position

It is likely that physiographic factors and landscape position affected the

herbaceous plant community composition of the sample plots. The TC mitigation plot

and PA reference plot were located in the northeasternmost county of Ohio (Ashtabula)

within the Mosquito Creek/Pymatuning Lowlands area of the EOLP ecoregion (Woods et

al. 1998). As a result of glacial influence, the Mosquito Creek/Pymatuning Lowlands

contain many areas with poorly drained soils that support relatively high numbers of

lakes, streams and wetlands (Woods et al. 1998). The poorly drained soils of this area

and its location in far northeastern Ohio, which is relatively cool and wet compared with

adjacent areas in the state, have encouraged the establishment of wetland plant species

that are more common in the Hemlock-White Pine-Northern Hardwoods Forest Region

29

than in the Beech-Maple Forest Region (Braun 1950, Andreas 1989). Notable

herbaceous species that occurred in these two plots and that are common in more

northern forests include: Maianthemum canadense, Dryopteris intermedia, Trientalis

borealis, Coptis trifolia, and Rubus hispidus (Figure 2.7). Also important is the observed

higher relative cover of moss, particularly Sphagnum spp. in these plots (personal

observation) which, as we suggested for vascular plants, may be related to the presence of

microtopographic features (Anderson et al. 2007) and physiographic factors.

The influence of physiographic conditions and landscape position was also

evident in the BB reference plot. This plot was located within the Low Lime Drift Plain

of the EOLP ecoregion characterized by scattered kettle lakes (Woods et al. 1998). The

sample plot itself was located in a depression near a kettle-hole bog (Brown’s Lake Bog

(The Nature Conservancy 2011)) and consequently supported many species associated

with “boggy forests” (e.g., Dryopteris cristata, Osmunda cinnamomea, Osmunda regalis,

Hydrocotyle americana, Circaea lutetiana) (Andreas 1989).

The CS mitigation plot was located in area of abandoned river channels (i.e.,

oxbows) associated with the West Branch of the Rocky River. It had relatively high

cover values for herbaceous species typically found in floodplains (e.g., Verbesina

alternifolia, Rudbeckia laciniata, Impatiens capensis) (Gordon 1969, Andreas 1989).

However, activities meant to restore hydrology in this area (i.e., ditch plugging and drain

tile destruction (Envirotech Consultants, personal communication 2011)) will presumably

influence the trajectory of the plant community. This hydrologic alteration will likely

encourage the establishment of herbaceous plant species adapted to more poorly drained

30

conditions rather than the well-drained conditions that characterize the soil map unit

associated with this plot (Table 2.1). The plant community may already be transitioning

given the association of shade-tolerant obligate wetland species such as Carex crinita var.

crinita and Glyceria striata with this plot (Figures 2.6 and 2.7).


Soil samples from reference plots had significantly higher cation exchange

capacity and total phosphorus than those taken from mitigation plots; however, no

significant differences for any of the other soil characteristics were found. Other studies

have shown that soil properties vary among natural and mitigation wetlands (Moser et al.

2009). Furthermore, it has been suggested that microtopographic relief may also

influence soil properties in forested ecosystems (Beatty 1984, Ahn et al. 2009) and in

constructed depressional wetlands (Alsfeld et al. 2009). Since we collected a single soil

sample from the center of each plot, we were not able to investigate the variation in soil

characteristics in relation to plant community composition in these ecoregions. Given the

range of past land use intensity that characterized the mitigation plots in this study, future

investigations of soil characteristics at these sites may prove to be important for the

development of wetland mitigation techniques.

Conclusions

The techniques used to establish, restore and enhance forested wetlands result in

mitigation areas that represent a range of forest development stages that are inherently

31

different in their herbaceous plant community composition. This study suggests that

physical habitat attributes may influence herbaceous plant community development in

Ohio forested wetlands by providing a range of microsites that are available for plant

establishment. Mitigation areas may lack the microtopographic heterogeneity found in

relatively undisturbed forested wetlands and this feature can be attributed to past land

use, disturbance history, physiographic factors and landscape position. Furthermore,

wetland mitigation areas, particularly mitigation banks, are large, heterogeneous areas

that often support hundreds of acres of wetland in different stages of plant community

development. As forested wetlands currently used for mitigation continue to develop and

new mitigation projects are approved by regulatory agencies, more opportunities to study

herbaceous plant communities in these areas will arise. Nevertheless, our study provides

important insight into the current plant community development of some of Ohio’s

forested mitigation wetlands and represents a point from which additional research can be

conducted.

32

Figure 2.1: Locations of the four mitigation plots (black dots) and the four relatively undisturbed reference forested wetland plots (open circles).

33

Figure 2.2: A standard VIBI sampling plot (Mack 2007b). The four intensively sampled subplots (2, 3, 8, 9) are shaded. Small numbered squares in the subplot corners represent the location of nested quadrats used to sample herbaceous vegetation.

34

Figure 2.3: Mean diversity values (± 1 standard error) for herbaceous plant species in mitigation and reference forested wetland plots.

Figure 2.4: Mean species richness (± 1 standard error) for herbaceous plant species in mitigation and reference forested wetland plots.

mitigation reference

Spec

ies

Ric

hnes

s

0

10

20

30

40

50

mitigation reference0.0

0.5

1.0

1.5

2.0

2.5

3.0

Evenness (Pielou's J)Shannon-Weiner IndexSimpson's Index

Figure 2.5: Dendrogram from a hierarchical cluster analysis using herbaceous plant species relative cover values in mitigation (black dots) and reference (open circles) plots.

Reference Plots

35

Figure 2.6: Nonmetric multidimensional scaling (NMDS) ordination of mitigation plots (black dots) and reference plots (open circles). Environmental attributes represented by the vectors include: number of hummocks (# hummocks), number of tussocks (# tussocks), number of macrotopographic depressions (# macr. depr.), litter depth, water depth (H2O depth) and counts of coarse woody debris 12-40 cm in diameter (cwd 12-40 cm) and greater than 40 cm (cwd > 40 cm).

NMDS 1

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

NM

DS

2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6


HR

TC

BI

CS

PA

BB

ACFW

H2O depth

# tussocks

litter depth

# macr. depr.

# hummocks

cwd >40 cm

cwd 12-40 cm

36

Figure 2.7: Nonmetric multidimensional scaling (NMDS) ordination of herbaceous plant species. Species abbreviations are provided in Table 2.2. Environmental attributes represented by the vectors include: number of hummocks (# hummocks), number of tussocks (# tussocks), number of macrotopographic depressions (# macr. depr.), litter depth, water depth (H2O depth) and counts of coarse woody debris 12-40 cm. in diameter (cwd 12-40 cm) and greater than 40 cm. (cwd > 40 cm).

NMDS 1

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

NM

DS

2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

H2O depth

# tussocks

litter depth

# macr. depr.cwd >40

cwd 12-40

# hummocksEQUARVRUDLAC

VERALTCXAMPH

CICMACARIDRA

PLGPUN

CXGRAY ALLPETLYSCIL

THAPUB

ELYRIP

CINARUTOXRAD

APOCANLUDALTCXHYAL CXMUSK

ASCINC

RICNAT

LYSNUM

PARQUI

GEUCAN

PLGVIR

RANHISCIRLUT

LEEVIRAGRPAR

RICFLUCXSQUA

LEMMINCXVULP

SCICYPUTRVUL

VITRIPSCIATR

JUNEFF

ELEOBTALISUB

BIDCER

LYSTERPILPUM

IMPCAPCARSTI

CXCRINC

CXTRIB

BIDFRO

CXLUPL

GLYSTROSMCINDRYCRI

PANLAN PANCLA

SPIALB

HYDAMECXSWANCLEVIR

PILFON

SOLPATCXATLAA PLGCES

PHAARU

LEEORY PLGHYDGALTIN

LYCRUBSCULAT

MAICAN

PLGSAG

CXINTUCXGRAC

CXLURI

CXSCOP

POLPEN

LUDPAL ARITRI

PLGPERCUSGRO

HYPMUT

EUPPER

SPAAME

PENSEDTHENOV

CXSEOR

COPTRITRIBORSMIROT

RUBHISMITREP

LYCOBS

OSMREG

ONOSEN

DRYCAR

BIDCON

MOSS

BOECYL

GLYSEPJUNTEN

SANMAR

GALCIR

CXLUPFCRYCAN

OSMCLAEREHIE

GALAPACXBLAN

CXTUCK

CXBROM

SIUSUAAMBART

37

Table 2.1: Summary information describing the four mitigation plots and the four relatively undisturbed reference plots.

Plot name and

Symbol

Latitude Longitude Ecoregion Soil Map Unit Cowardin Classification

Plot Type Treatment Type

Alum Creek State Park

(AC)

40.20569

-82.94516

Eastern Corn Belt

Plains

Condit silt loam,

0-1 percent slopes (CnA)

palustrine, forested

reference

None

Brown’s Lake Bog SNP (BB)

40.68223 -82.06538 Erie/Ontario Drift and Lake

Plains

Carlisle muck (Ch)

palustrine, forested reference None

Fowler Woods SNP

(FW)


Plains

Bennington silt loam, 0-2 percent

slopes (BnA)


Pallister SNP (PA)


Plains

Caneadea-Canadice silt

loam, 0-2 percent slopes (CeA)


Big Island

(BI)

40.57825

-83.29400

Eastern Corn Belt

Plains

Latty silty clay

(La) and Paulding clay (Pa)


mitigation (bank)

hydrologic restoration

Columbia

Station (CS)

41.31612

-81.92192

Erie/Ontario

Drift and Lake Plains

Chagrin silt loam

(Ch)


mitigation

(consolidated)


Hale Road

(HR)

41.28845

-82.16198

Erie/Ontario


Mahoning silt

loam, 0-2 percent slopes (MgA)


mitigation (bank)


Trumbull Creek (TC)

41.65917

-81.00477

Erie/Ontario Drift and Lake

Plains

Platea-Darien silt loams complex, 0-2 percent slopes

(PtA)


mitigation (bank)

hydrologic enhancement

38

39

Table 2.2: Mean (103) (± 1 standard error) relative cover values for herbaceous species in mitigation and reference forested wetland plots. Nomenclature based on Andreas et al. (2004). Species Abbreviation Authority Mitigation Reference Agrimonia parviflora AGRPAR Aiton 0.28 (0.28) 0.44 (0.44) Alisma subcordatum ALISUB Raf. 1.01(0.60) 0.94 (0.54) Alliaria petiolata ALLPET (M. Bieb.) Cavara &

Grande 0.02 (0.02) 0.00 (0.00)

Ambrosia artemisiifolia AMBART L. 0.00 (0.00) 0.01 (0.01) Apocynum cannabinum APOCAN L. 0.20 (0.20) 0.00 (0.00) Arisaema dracontium ARIDRA (L.) Schott 0.01 (0.01) 0.00 (0.00) Arisaema triphyllum subsp. triphyllum

ARITRIT (L.) Schott 0.68 (0.68) 0.01 (0.01)

Asclepias incarnata ASCINC L. 0.20 (0.20) 0.00 (0.00) Bidens cernua BIDCER L. 1.69 (0.61) 0.00 (0.00) Bidens connata BIDCON Muhl. ex Willd. 0.00 (0.00) 8.90 (6.58) Bidens frondosa BIDFRO L. 85.30 (85.30) 6.22 (2.43) Boehmeria cylindrica BOECYL (L.) Sw. 2.03 (2.03) 19.10 (10.20) Carex amphibola CXAMPH Steud. 1.91 (1.91) 0.00 (0.00) Carex atlantica subsp. atlantica

CXATLAA L.H. Bailey 0.00 (0.00) 0.01 (0.01)

Carex blanda CXBLAN Dewey 0.00 (0.00) 0.01 (0.01) Carex bromoides CXBROM Schkuhr ex Willd. 0.00 (0.00) 3.26 (1.90) Carex canescens CXCANE L. 0.00 (0.00) 5.98 (5.97) Carex crinita var. crinita CXCRINC Lam. 0.82 (0.82) 7.63 (6.37) Carex gracillima CXGRCLI Schwein. 0.68 (0.68) 0.00 (0.00) Carex grayi CXGRAY J. Carey 1.36 (1.36) 0.00 (0.00) Carex hyalinolepis CXHYAL Steud. 0.40 (0.40) 0.00 (0.00) Carex intumescens CXINTU Rudge 2.03 (2.03) 0.00 (0.00) Carex lupuliformis CXLUPF Sartwell ex Dewey 0.00 (0.00) 8.84 (8.84) Carex lupulina CXLUPL Muhl. ex Willd. 0.83 (0.64) 1.99 (1.99) Carex lurida CXLURI Wahlenb. 51.40 (51.40) 0.01 (0.01) Carex muskingumensis CXMUSK Schwein. 0.40 (0.40) 0.00 (0.00) Carex scoparia CXSCOP Schkuhr ex Willd. 2.71 (2.71) 0.00 (0.00) Carex seorsa CXSEOR Howe 0.00 (0.00) 20.40 (20.40) Carex squarrosa CXSQUA L. 0.40 (0.40) 0.44 (0.44) Carex stipata CXSTIP Muhl. ex Willd. 0.22 (0.20) 1.74 (0.66) Carex swanii CXSWAN (Fernald) Mack. 0.00 (0.00) 0.54 (0.54) Carex tribuloides CXTRIB Wahlenb. 4.96 (1.98) 5.20 (3.07) Carex tuckermanii CXTUCK Dewey 0.00 (0.00) 0.44 (0.44) Carex vulpinoidea CXVULP Michx. 0.60 (0.60) 0.00 (0.00) Cicuta maculata CICMAC L. 0.82 (0.82) 0.00 (0.00) Cinna arundinacea CINARU L. 27.20 (27.20) 0.98 (0.56) Circaea lutetiana CIRLUT L. 0.55 (0.55) 0.46 (0.45) Clematis virginiana CLEVIR L. 0.00 (0.00) 0.27 (0.27) Coptis trifolia COPTRI (L.) Salisb. 0.00 (0.00) 0.64 (0.64) Cryptotaenia canadensis CRYCAN (L.) DC. 0.00 (0.00) 0.45 (0.45) Cuscuta gronovii CUSGRO Willd. ex Schulte 0.68 (0.68) 0.00 (0.00) Dryopteris carthusiana DRYCAR (Vill.) H.P. Fuchs 0.01 (0.01) 4.16 (1.38) Dryopteris cristata DRYCRI (L.) A. Gray 0.00 (0.00) 0.29 (0.27) Eleocharis obtusa ELEOBT (Willd.) Schult. 1.09 (0.67) 0.00 (0.00) Elymus riparius ELYRIP Wiegand 0.27 (0.27) 0.00 (0.00) Equisetum arvense EQUARV L. 0.27 (0.27) 0.00 (0.00) Erechtites hieracifolia EREHIE (L.) Raf. ex DC. 0.00 (0.00) 0.45 (0.44) Eupatorium perfoliatum EUPPER L. 2.03 (2.03) 0.01 (0.01) Fragaria virginiana FRAVIR Duchesne 0.00 (0.00) 1.01 (1.01) (Continued)

40

Table 2.2: Continued Galium aparine GALAPA L. 0.00 (0.00) 0.44 (0.44) Galium circaezans GALCIR Michx. 0.00 (0.00) 0.01 (0.01) Galium tinctorium GALTIN (L.) Scop. 4.37 (3.97) 3.43 (2.87) Geum canadense GEUCAN Jacq. 1.63 (1.63) 0.45 (0.44) Glyceria septentrionalis GLYSEP Hitchc. 0.00 (0.00) 14.20 (14.10) Glyceria striata GLYSTR (Lam.) Hitchc. 3.01 (1.99) 33.00 (27.70) Hydrocotyle americana HYDAME L. 0.00 (0.00) 1.61 (1.61) Hypericum mutilum HYPMUT L. 0.01 (0.01) 0.00 (0.00) Impatiens capensis IMPCAP Meerb. 5.44 (5.44) 25.70 (18.40) Juncus effusus JUNEFF L. 4.00 (3.16) 0.00 (0.00) Juncus tenuis JUNTEN Willd. 0.00 (0.00) 0.44 (0.44) Leersia oryzoides LEEORY (L.) Sw. 9.76 (7.64) 1.44 (0.95) Leersia virginica LEEVIR Willd. 2.90 (2.34) 2.39 (0.33) Lemna minor LEMMIN L. 75.40 (75.40) 1.57 (1.47) Ludwigia alternifolia LUDALT L. 0.61 (0.61) 0.00 (0.00) Ludwigia palustris LUDPAL (L.) Elliott 4.06 (4.06) 0.00 (0.00) Lycopodium obscurum LYCOBS L. 0.00 (0.00) 0.64 (0.64) Lycopus rubellus LYCRUB Moench 23.30 (22.90) 22.80 (8.06) Lysimachia ciliata LYSCIL L. 0.27 (0.27) 0.00 (0.00) Lysimachia nummularia LYSNUM L. 175.00 (129.00) 0.00 (0.00) Lysimachia terrestris LYSTER (L.) B.S.P. 0.54 (0.54) 2.49 (2.49) Maianthemum canadense MAICAN Desf. 2.71 (2.71) 2.56 (2.56) Mitchella repens MITREP L. 0.00 (0.00) 0.64 (0.64) Moss spp. MOSS N/A 20.10 (15.10) 91.30 (37.40) Onoclea sensibilis ONOSEN L. 4.06 (4.06) 3.71 (3.08) Osmorhiza claytonii OSMOCL (Michx.) C.B.

Clarke 0.00 (0.00) 0.44 (0.44)

Osmunda cinnamomea OSMCIN L. 0.00 (0.00) 101.00 (83.70) Osmunda regalis OSMREG L. 0.00 (0.00) 46.40 (30.40) Oxalis stricta OXASTR L. 0.00 (0.00) 0.50 (0.50) Panicum clandestinum PANCLA L. 0.00 (0.00) 0.27 (0.27) Panicum lanuginosum PANLAN Elliott 0.00 (0.00) 0.27 (0.27) Parthenocissus quinquefolia PARQUI (L.) Planch. 6.53 (6.53) 1.93 (0.74) Penthorum sedoides PENSED L. 2.88 (2.67) 0.00 (0.00) Phalaris arundinacea PHAARU L. 1.03 (0.59) 0.27 (0.27) Pilea fontana PILFON (Lunnell) Rydb. 0.00 (0.00) 0.01 (0.01) Pilea pumila PILPUM (L.) A. Gray 0.82 (0.82) 8.85 (7.18) Polygonum cespitosum PLGCES Blume 0.00 (0.00) 1.61 (1.61) Polygonum hydropiperoides PLGHPO Michx. 3.79 (3.27) 0.89 (0.89) Polygonum pensylvanicum PLGPEN L. 1.35 (1.35) 0.00 (0.00) Polygonum persicaria PLGPER L. 0.68 (0.68) 0.01 (0.01) Polygonum punctatum PLGPUN Elliott 0.27 (0.27) 0.00 (0.00) Polygonum sagittatum PLGSAG L. 0.68 (0.68) 0.28 (0.28) Polygonum virginianum PLGVIR L. 10.30 (10.30) 2.27 (1.67) Ranunculus hispidus RANHIS Michx. 0.54 (0.54) 0.44 (0.44) Riccia fluitans RICFLU L. 0.81 (0.81) 0.50 (0.50) Ricciocarpos natans RICNAT (L.) Corda 0.81 (0.81) 0.01 (0.01) Rubus hispidus RUBHIS L. 0.00 (0.00) 0.66 (0.66) Rudbeckia laciniata RUDLAC L. 0.01 (0.01) 0.00 (0.00) Sanicula marilandica SANIMA L. 0.00 (0.00) 0.88 (0.88) Scirpus atrovirens SCIATR Willd. 1.21 (1.21) 0.00 (0.00) Scirpus cyperinus SCICYP (L.) Kunth. 2.49 (1.71) 0.00 (0.00) Scutellaria lateriflora SCULAT L. 2.71 (2.71) 2.45 (1.88) Sium suave SIUSUA Walter 0.00 (0.00) 1.82 (1.25) Smilax rotundifolia SMXROT L. 0.00 (0.00) 0.64 (0.64) Solidago patula SOLPAT Muhl. ex Willd. 0.00 (0.00) 0.01 (0.01) Sparganium americanum SPAAME Nutt. 0.68 (0.68) 0.00 (0.00) (Continued)

41

Table 2.2: Continued Spiraea alba SPIALA Du Roi 0.00 (0.00) 0.27 (0.27) Thalictrum pubescens THAPUB Pursh 0.28 (0.28) 0.02 (0.02) Thelypteris noveboracensis THENOV (L.) Nieuwl. 0.68 (0.68) 0.00 (0.00) Toxicodendron radicans TOXRAD (L.) Kuntze 117.00 (113.00) 2.99 (0.98) Trientalis borealis TRIBOR Raf. 0.00 (0.00) 0.66 (0.66) Utricularia vulgaris UTRVUL L. 75.40 (75.40) 9.58 (9.58) Verbesina alternifolia VERALT (L.) Britton ex

Kearney 0.27 (0.27) 0.00 (0.00)

Vitis riparia VITRIP Michx. 0.16 (0.15) 0.00 (0.00)

42

Table 2.3: Mean values of (± 1 standard error) soil characteristics from mitigation and reference forested wetlands. An asterisk indicates a significant difference in the mean values between mitigation and reference sites (Mann-Whitney test; p < 0.05).

Soil Characteristic Mitigation Reference soil reaction (pH) 5.89 (0.41) 5.23 (0.13) total phosphorus (μg/g) 8.60 (0.93) 44.98 (29.96)* total potassium (μg/g) 165.76 (25.87) 179.62 (15.92) total calcium (μg/g) 2031.66 (462.50) 3793.69 (811.67) total magnesium (μg/g) 396.73 (68.47) 619.18 (82.09) cation exchange capacity 21.24 (2.36) 41.78 (6.58)* % calcium 51.08 (13.27) 44.00 (4.08) % magnesium 16.30 (0.39) 12.67 (1.41) % potassium 2.04 (0.27) 1.20 (0.25) % nitrogen 0.25 (0.04) 0.84 (0.45) % carbon 2.40 (0.61) 10.47 (6.31) % organic matter 4.85 (0.10) 23.85 (13.46)

Table 2.4: Mean values (± 1 standard error) of physical attributes from mitigation and reference forested wetland plots. An asterisk indicates a significant difference in the mean values between mitigation and reference sites (Welch two-sample t-test; * p < 0.05).

Attribute Mitigation Reference Depth of standing water 0.31 (0.31) 0.00 (0.00) Litter depth 0.57 (0.26) 1.25 (0.43) Number of tussocks 0.44 (0.36) 1.13 (0.24) Number of hummocks 0.81 (0.81) 3.69 (1.08) Number of macrotopographic depressions 2.25 (0.53) 3.75 (0.40) Coarse woody debris 12-40 cm diameter 0.19 (0.19) 4.81 (1.36)* Coarse woody debris >40 cm diameter 0.00 (0.00) 0.75 (0.45)

43

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Woods, A. J., J. M. Omernik, C. S. Brockman, T. D. Gerber, W. D. Hosteter and S. H. Azevedo. 1998. Ecoregions of Indiana and Ohio, 1:1500000. Available at http://www.epa.gov/wed/pages/ecoregions/ohin_eco.htm. Accessed May 28, 2011.

50

CHAPTER 3

WOODY PLANT COMMUNITY COMPOSITION AND STRUCTURE OF

FORESTED MITIGATION WETLANDS AND RELATIVELY UNDISTURBED

REFERENCE FORESTED WETLANDS

Abstract

Compensatory mitigation for impacts to forested wetlands is a topic that has

generated considerable debate among scientists and regulators in recent years. There is

considerable uncertainty associated with the development of desirable woody plant

communities in forested mitigation areas, which is critical to the overall biological

integrity of forested wetlands. We examined woody plant community characteristics and

physical habitat attributes of four forested mitigation wetlands and four relatively

undisturbed reference forested wetlands within the Erie/Ontario Drift and Lake Plains

and Eastern Corn Belt Plains ecoregions of Ohio. Within the study areas we sampled

woody vegetation and physical habitat attributes using protocols established by the Ohio

Environmental Protection Agency for wetland assessment. Our objective was to compare

the woody plant community composition and structure of mitigation and reference

forested wetlands and identify factors that may be influencing woody plant community

development in these areas. Multi-response permutation procedure (MRPP) indicated

that woody plant composition was not significantly different between

51

mitigation and reference plots (p = 0.11). The compositions of both reference and

mitigation plots concurred with the general description of elm-ash-maple forests in Ohio.

Dominant canopy tree species in both plot types were Ulmus americana, Fraxinus

pennsylvanica, Acer saccharinum and Acer rubrum. However, our data suggests that

relatively undisturbed forests supported higher numbers of ecologically-conservative

species and species adapted to certain environmental conditions (e.g., shade and saturated

soil). Nonmetric multidimensional scaling (NMDS) ordination revealed that reference

plots were positively associated with several physical habitat attributes, e.g., hummocks,

macrotopographic depressions and coarse woody debris which were also associated with

several ecologically conservative species. Structurally, the density of wetland shrub

species (p = 0.02) and importance of native woody subcanopy species (p = 0.03) were

both significantly higher in reference plots than in mitigation plots. Activities meant to

restore, establish or enhance forested wetlands for use as compensatory mitigation result

in forests representing a range of disturbance histories and development stages. The

woody plant community characteristics of these mitigation areas are likely influenced by

several variables including disturbance history, physiographic factors and landscape

position. At a more local scale, physical habitat attributes such as hummocks,

macrotopographic depressions and coarse woody debris provide important microsites that

encourage the establishment and persistence of some woody species in forested

mitigation wetlands.

52

Introduction

Woody plants (i.e., trees, shrubs and vines) are important components of forested

wetland ecosystems. This structure is characterized by the physical and temporal

distribution of woody plants (Oliver and Larson 1996), and is closely associated with the

species composition at any given time during forest stand development. Understanding

the factors that influence woody plant community composition and structure can provide

insight into the capacity of a forested wetland to provide important ecological functions

(e.g., water quality enhancement and habitat for flora and fauna). The unique

hydroperiods and plant community structure of forested wetlands are important for

certain bird species (Kaminski et al. 1993, Riffell et al. 2006, Hoover 2009, Benson and

Bednarz 2010), amphibian species (Baldwin et al. 2006, Karraker and Gibbs 2009),

invertebrate species (Braccia and Batzer 2001, Batzer et al. 2005) and rare plant species

(Laidig et al. 2009, Scanga and Leopold 2010). Unfortunately, forested wetlands are

very difficult to restore, establish and (or) enhance (National Research Council 2003,

Mitsch and Gosselink 2007). Yet in Ohio, forested wetlands continue to be negatively

impacted at rates equal to or higher than those for other wetland types (Porej 2003).

Prior to European settlement, forested wetlands were common within the

deciduous forest that covered most of Ohio (Braun 1961, Gordon 1969, Andreas 1989).

Within the Beech-Maple Forest Region of Ohio, Braun (1961) describes extensive tracts

of elm-ash-maple forest that occupied depressions and intermorainal flats. Following

European settlement of the state, much of the forest including forested wetlands was

cleared to harvest timber, to make the land suitable for agriculture (Gordon 1969), and

53

later, for commercial and residential developments. In addition, environmental attributes

such as soil structure, hydrologic regime and microtopographic heterogeneity were

drastically altered by draining and plowing areas that were too wet for agriculture and

urban development.

The ecological value of undisturbed forested wetlands has been increasingly

realized and understanding the factors influencing forested wetland restoration is now a

priority for scientists (Schoenholtz et al. 2001, Allen et al. 2004). During the design of

wetland restoration, establishment and enhancement projects (i.e., wetland mitigation)

(USACE and USEPA 2008) meant to replace wetland functions following disturbance,

much attention is given to the establishment of woody plant communities similar to those

that characterize natural, undisturbed forested wetlands. To identify factors that may be

influencing forested wetland mitigation development, we investigated woody plant

community composition and structure in mitigation forested wetlands and relatively

undisturbed reference forested wetlands in Ohio. The relatively undisturbed areas

represent reference standards that are important for mitigation wetland assessment

(Brinson and Rheinhardt 1996). Specifically, the objectives of this chapter are to: 1)

compare the woody plant community composition and structure of forested mitigation

wetlands and relatively undisturbed reference forested wetlands, and 2) identify factors

that may be influencing the woody plant community composition and structure in both

types of forested wetlands.

54

Study Areas

A total of eight study areas (Figure 3.1, Table 3.1) were located within the

Erie/Ontario Drift and Lake Plains (EOLP) and Eastern Corn Belt Plains (ECBP)

ecoregions of Ohio (Woods et al. 1998). The study areas were confined to these

ecoregions to reduce the effect of major climatic and physiographic differences compared

with adjacent ecoregions. Both ecoregions fall within the Beech-Maple Forest Region

described by Braun (1950). The Wisconsinan glacial deposits of this region support a

variety of lakes, streams and wetlands, and forested wetland plant communities in the

region are typically dominated by the following canopy tree species and genera:

American elm (Ulmus americana), ash (Fraxinus spp.), silver maple (Acer saccharinum),

and red maple (Acer rubrum) (Braun 1950,1961, Gordon 1969, Andreas 1989).

Four study areas identified as representing relatively undisturbed reference

forested wetlands were randomly selected from a database provided by the Ohio

Environmental Protection Agency’s Wetland Ecology Group. The four reference plots

were located in low-lying natural depressions. The four randomly selected mitigation

areas were in relatively flat areas or depressions where the restoration, establishment and

(or) enhancement (USACE and USEPA 2008) of forested wetlands had occurred. All

eight study plots were located in areas classified as palustrine forested wetlands

according to the Classification of Wetlands and Deepwater Habitats of the United States

(Cowardin et al. 1979).

Three of the mitigation plots were located within approved mitigation banks and

the fourth was within a “consolidated” mitigation area that includes several individual

55

mitigation projects constructed according to Clean Water Act, Section 404 permits.

Banks are different from consolidated areas because they are large areas where wetland

restoration, establishment, enhancement and in some cases preservation activities are

planned and approved by the Interagency Review Team before being used for any

specific compensatory mitigation (USACE and USEPA 2008). Once approved, bank

credits become available to anyone needing to purchase mitigation acreage to compensate

for permitted wetland impacts. Currently, mitigation banks are the preferred mitigation

option of federal regulatory agencies (USACE and USEPA 2008).

Physiography and Soils

Six of the sample plots were located in the EOLP ecoregion that is characterized

as an area with a variety of lakes, streams and wetlands in locations where the land is flat

and soils are clayey (Woods et al. 1998). More specifically, two sample plots (Pallister

SNP and Trumbull Creek) were located in the Mosquito Creek/Pymatuning Lowlands

sub-ecoregion that is characterized by poorly drained soils and wet-mesic forests (Woods

et al. 1998). The other four EOLP plots (Brown’s Lake Bog SNP, Columbia Station,

Fowler Woods SNP and Hale Road) were located in the Low Lime Drift Plain sub-

ecoregion that is characterized by low rolling hills and end moraines with scattered kettle

lakes (Woods et al. 1998). The soil map units associated with the sampling locations

(Table 3.1) in the EOLP are in a range of drainage classes from very poorly drained to

well-drained (Soil Survey Staff 2011).

56

Two sample plots and were located in the Eastern Corn Belt Plains (ECBP)

ecoregion that is characterized as a rolling till plain with local end moraines (Woods et al.

1998). One ECBP plot (Alum Creek State Park) was within the Loamy, High Lime Till

Plains sub-ecoregion which contains soils that developed from loamy, limy Wisconsinan

glacial deposits. The other ECBP plot (Big Island) was within the Clayey, High Lime

Till Plains sub-ecoregion with soils that contain more clay content than the adjacent

Loamy, High Lime Till Plains. The soil map units associated with these two sample plots

(Table 3.1) are described as somewhat poorly drained or very poorly drained (Soil Survey

Staff 2011).

Methods

Vegetation Data

With the objective of comparing woody plant community composition and

structure at forested wetland mitigation areas in Ohio, it was important to use methods

that have been previously applied for mitigation project evaluation (Mack and

Micacchion 2006, Micacchion and Gara 2008, Micacchion et al. 2010). To allow for

direct comparison, the Vegetation Index of Biotic Integrity for Wetlands v1.4 (VIBI), a

vegetation-based wetland assessment tool developed by Mack (2007b) was used to

characterize woody plant communities. All vegetation data were collected between June

15 and September 15, 2009. The VIBI sampling method is based on the releve method

for vegetation sampling described by Peet et al. (1998).

57

At each study area, a standard VIBI plot (Figure 3.2) was established. Each plot

was 20 meters wide by 50 meters long and divided using measuring tapes into 10-meter

by 10-meter subplots marked with stake flags. The compass bearing of the center line

was recorded and each plot corner was georeferenced using a Garmin 60 CSx handheld

GPS unit.

In each of the ten subplots, species-level identification according to Braun (1961)

was recorded for every woody plant greater than one meter tall. Any woody plant that

was not able to be confidently identified in the field was collected, pressed, mounted on

herbarium-grade paper and identified at a later time. The diameter at breast height (dbh)

was taken at a height of 1.37 meters for each individual woody plant with the exception

of multi-stemmed shrubs (e.g., Cephalanthus occidentalis) which were classified as

“shrub clumps.” The dbh measurement of each woody plant that was not a multi-

stemmed shrub was placed in one of the following classes: 1 (0 - < 1cm), 2 (1 - <2.5 cm),

3 (2.5 - <5 cm), 4 (5 - <10 cm), 5 (10 - <15 cm), 6 (15 - <20 cm), 7 (20 - <25 cm), 8 (25 -

<30 cm), 9 (30 - <35 cm) and 10 (35 - <40 cm). Any dbh measurement greater than 40

cm was recorded individually to the nearest tenth of a centimeter. The complete

methodology used to sample the woody vegetation in this study is described in the Field

Manual for the Vegetation Index of Biotic Integrity for Wetlands v1.4 (Mack 2007b).

Soil data

A soil sample was taken to a depth of 12 cm, using a bucket auger, at the center of

each plot as stated in the VIBI field manual (Mack 2007b). The soil samples were placed

58

in bags, labeled and stored in a freezer until analyses could be conducted. The samples

were submitted to the Ohio State University’s Service Testing and Research (STAR)

Laboratory located at the Ohio Agricultural Research and Development Center (OARDC)

campus in Wooster, Ohio on October 30, 2009. Results of the analyses were received on

December 3, 2009. The samples were analyzed for soil reaction (pH), cation exchange

capacity (CEC), total phosphorus (P), total potassium (K), total calcium (Ca), total

magnesium (Mg), percent calcium (%Ca), percent magnesium (%Mg), percent potassium

(%K), percent nitrogen (%N), percent carbon (%C), and percent organic matter (% OM)

(STAR Lab 2011).


Physical habitat attribute data were collected in subplots 2, 3, 8 and 9. The

physical attributes recorded were: depth of standing water, litter depth, number of

tussocks, number of hummocks, number of macrotopographic depressions, a count of

coarse woody debris 12-40 cm in diameter, and a count of coarse woody debris greater

than 40 cm in diameter. Water depth and litter depth were determined using a measuring

tape.

Vegetation Data Reduction and Metric Calculations

To calculate species importance values (the average of the relative frequency,

relative density and relative dominance of a species) and VIBI metric values, a series of

data reductions were necessary as outlined in the VIBI field manual (Mack 2007b). For

59

calculations that consider only wetland species, the wetland indicator statuses according

to the National List of Plant Species That Occur in Wetlands (Reed 1988) were used to

separate wetland species from non-wetland species. Those statuses are: obligate wetland

(OBL), facultative wetland (FACW), facultative (FAC), facultative upland (FACU) and

obligate upland (UPL) (Reed 1988). To calculate the density of wetland shrubs and

wetland trees, only species with a FACW or OBL indicator status were used in the

calculation (Mack 2007a). Data reduction calculations were completed using a Microsoft

Excel spreadsheet created by OEPA specifically for VIBI data analysis (Mack 2007a).


Species richness, evenness (Pielou’s J) (Pielou 1969), Shannon-Weiner

index values and Simpson diversity values were calculated using the PC-ORD statistical

software program (McCune and Mefford 2006). Differences in the means of these

diversity measures between mitigation and reference plots were determined using

Welch’s two-sample t-test. A significance level of 0.05 was used to evaluate if

differences between groups were statistically significant.

To characterize woody plant community structure, differences in mean density

(number of stems per hectare) of wetland tree species (i.e., species coded as “native

wetland tree” by Mack (2007a)) and mean importance value of canopy tree species (i.e.,

those that would occupy the upper canopy at maturity (Mack 2007b, 2009)) (Table 3.2)

between mitigation and reference plots were evaluated using Mann-Whitney

nonparametric tests for group differences. Additionally, Welch two-sample t-tests were

60

used to evaluate differences in mean density (number of shrub clumps per hectare) of

wetland shrub species and subcanopy IV metric values (Table 3.2). Shapiro-Wilk tests

for normality were used to determine the appropriate tests for significant group

differences in these structural measures between mitigation and reference plots. A

significance level of 0.05 was used to evaluate if differences between groups were

statistically significant.

In order to describe the similarity with respect to woody plant community

composition, an agglomerative hierarchical clustering technique based on a Euclidean

dissimilarity matrix was used to compare the woody plant species importance values

between mitigation and reference forested wetland plots. Ward’s linkage method was

used because of its compatibility with Euclidean distances (McCune and Grace 2002).

Cluster analysis is a useful way of identifying groups when working with ecological data

and has been used for many years in community ecology (McCune and Grace 2002). The

analysis was completed using the PC-ORD statistical software program (McCune and

Mefford 2006). The dendrogram used to interpret the cluster analysis was scaled using

Wishart’s (1969) objective function which measures the amount of information lost at

each step of the clustering procedure.

Multi-Response Permutation Procedure (MRPP) (Meilke and Berry 2001) was

used to test for a significant difference in woody plant species composition between

mitigation and reference plots. MRPP is a nonparametric procedure that is useful for

testing for a significant difference between two or more groups. It is an appropriate

method to use with ecological community data that often do not meet distributional

61

assumptions required of parametric tests that address similar questions (McCune and

Grace 2002). Euclidean distance was used and groups were defined based on

classification as a mitigation plot or as a reference plot. Statistical significance of the

results was evaluated using a significance level of 0.05.

Nonmetric multidimensional scaling analysis (NMDS) was used to examine

woody plant community compositions as they relate to physical habitat attributes.

NMDS is an effective ordination method to use on ecological data sets and its advantages

include: (1) avoiding assumptions of linear relationships among variables, (2) relieving

the “zero truncation problem” associated with all ordinations of community data sets, and

(3) allowing the use of any distance measure or relativation (McCune and Grace 2002).

NMDS uses an iterative search to converge on a solution that minimizes “stress” in the

configuration. Stress is a function of a linear monotone transformation of observed

dissimilarities and ordination distances. The NMDS procedure was performed using the

VEGAN metaMDS function (Oksanen et al. 2010) in the R statistical computing program

(R Development Core Team 2009). NMDS allows for an indirect examination of how

environmental factors may be influencing community composition by fitting vectors

representing environmental variables onto the ordination using the envfit function

(Oksanen et al. 2010).

Differences in soil chemical characteristics were examined using Mann-Whitney

nonparametric tests of group differences, and Welch two-sample t-tests were used to test

for significant group differences in physical habitat attributes. Methods of analysis were

62

chosen following Shapiro-Wilk tests for normality. A significance level of 0.05 was used

to evaluate if differences between groups were statistically significant.

Results

A total of forty-eight woody plant species greater than one meter tall were found

in the sample plots (Table 3.3). The results from Welch’s two-sample t-tests indicated

that there were no significant differences in species richness, evenness (Pielou’s J),

Shannon-Wiener index values or Simpson diversity values between mitigation and

reference plots (p > 0.05) (Figures 3.2 and 3.3). In mitigation plots, the most important

species were Fraxinus pennsylvanica, Acer saccharinum, Acer rubrum and Ulmus

americana. The highest mean importance values in reference plots were calculated for

Acer saccharinum, Acer rubrum, Ulmus americana, Nyssa sylvatica, Cephalanthus

occidentalis and Quercus bicolor. Standing dead trees had high mean importance values

in both mitigation and reference plots. The tests for group differences in structural

measures found a significantly higher density of wetland shrub species (p = 0.02) (Figure

3.4) and significantly higher mean subcanopy IV metric value at mitigation sites (p =

0.03) (Figure 3.5), but no significant difference for mean density of wetland trees (p =

0.20) or mean canopy IV metric values (p = 0.12) (Figures 3.6 and 3.7). Two mitigation

plots had considerably higher stem densities in smaller diameter classes than the other six

plots (Table 3.4).

63

Cluster Analysis

Two reference plots, Fowler Woods (FW) and Alum Creek (AC) grouped

together very early in the clustering procedure at distance 0.063. At distance 0.13, two

mitigation plots, Big Island (BI) and Hale Road (HR) grouped together. Trumbull Creek

(TC) grouped with Pallister State Nature Preserve (PA) at distance 0.24 followed by the

grouping of Columbia Station (CS) with FW and AC at distance 0.38, and Brown’s Lake

Bog (BB) was added to that group at 0.56. Finally, BI and HR were grouped with CS,

FW, and AC at distance 1.20 and TC and PA were added at 2.03 to complete the

dendrogram (Figure 3.8).

Multi Response Permutation Procedure

MRPP indicated that there was no significant difference in woody plant species

composition between mitigation and reference plots (p = 0.11). The chance-corrected

within group agreement (A), which is a description of the effect size that is independent

of the sample size, was 0.07. A values in community ecology are commonly below 0.1

(McCune and Grace 2002).


NMDS analysis of woody plant importance values converged on a stable solution

after five tries with a final stress value of 3.53. The ordination diagram depicts the

relationships among the sample plots and species, and provides an indirect examination

of physical habitat attribute influences using vectors (Figure 3.9). The number of

64

tussocks and depth of litter were both strongly associated with NMDS axis 1. Coarse

woody debris 12-40 cm in diameter and greater than 40 cm in diameter were associated

with NMDS axis 2. The number of hummocks, number of macrotopographic depressions

and water depth were associated with both axes.

The ordination plot depicts plot and species groupings (Figure 3.9). Some species

are grouped very closely near plot positions, indicating that those species occurred either

exclusively in particular plots or had high importance values relative to the corresponding

values that were recorded for other plots. For example, Betula alleghaniensis, Carpinus

caroliniana, Fraxinus nigra and Pinus strobus are species grouped very closely near the

PA plot position. Also, Acer negundo, Platanus occidentalis, Quercus macrocarpa, and

Viburnum lentago are species that are strongly associated with the CS plot in ordination

space. Species that occur in multiple plots and (or) have comparable importance values

across several plots are located between plot locations in ordination space. An example

is Ulmus americana, an important species in several plots that is not strongly associated

with any single plot position in ordination space.


The distributions of all the soil characteristics were determined to be non-normal

using Shapiro-Wilk tests. Consequently, the mean values for soil characteristics were

subjected to Mann-Whitney nonparametric tests for group differences. Mean values for

cation exchange capacity (CEC) (p = 0.03) and total phosphorus (P) (p = 0.03) were

significantly higher for reference plots than for mitigation plots (Table3.5).

65


The mean number of hummocks (p = 0.08), number of tussocks (p = 0.17),

number of macrotopographic depressions (p = 0.07), count of coarse woody debris > 40

cm in diameter (p = 0.19), litter depth (p = 0.23) and water depth (p = 0.39) were all

compared using Welch two-sample t-tests (Table 2.4). Only one attribute, the mean

count of coarse woody debris 12-40 cm in diameter, was significantly higher in reference

plots (p = 0.04).

Discussion

Canopy Composition

The composition of canopy tree species (i.e., those that would occupy the upper

canopy at maturity (Mack 2007b, 2009)) in all plots concurred with the general

description of the elm-ash-maple forests known to occur in Ohio (Braun 1950, 1961;

Gordon 1969, Andreas 1989). Dominant canopy tree species in both plot types were

Ulmus americana, Fraxinus pennsylvanica, Acer saccharinum and Acer rubrum. Given

the low number of tree species that occurred in the sample plots, MRPP results did not

indicate a significant compositional difference between mitigation and reference plots.

When the most important canopy species in each plot were used for interpretation,

cluster analysis revealed three distinct groups (Figure 3.8). Two young mitigation plots,

BI and HR, in the stem exclusion stage of forest development (Oliver and Larson 1996)

were very similar and were characterized by the dominance of one species, Fraxinus

pennsylvanica (Figure 3.8). This is not surprising given the lightweight seeds of this

66

species and its documented dominance of restored, 5 to 7 year old forested wetlands

(Kruse and Groninger 2003, Ruzicka et al. 2010) and disturbed forested wetlands in Ohio

(Mack 2004). Acer rubrum and Acer saccharinum characterized the canopies of the

reference plots as well as the TC and CS mitigation plots which both had existing,

relatively mature canopies prior to restoration or enhancement treatment (Figure 3.8).

Acer spp. may be important components of more mature forested wetlands and may

become more important in the BI and HR plots during later stages of forest development.

Diversity index values were similar for the two plot types (Figures 3.2 and 3.3);

however, several canopy species that are considered to be ecologically-conservative

(Andreas et al. 2004) were strongly associated with reference plots (Figure 3.9). Those

species include: Quercus bicolor, Quercus rubra, Quercus alba, Nyssa sylvatica,

Fraxinus nigra, Fraxinus profunda, Betula alleghaniensis and Pinus strobus (Table 3.3).

Only one conservative canopy species, Quercus macrocarpa, occurred in mitigation plots

but not reference plots (Table 3.3).

NMDS ordination indicates that conservative canopy species in reference plots

may be associated with physical habitat attributes that increase microtopographic

heterogeneity (Figure 3.9). The establishment of canopy species that prefer upland

habitats (e.g., Quercus rubra, Quercus alba, Fagus grandifolia, Liriodendron tulipifera)

(Reed 1988) may have been encouraged by the greater abundance of elevated microsites

such as hummocks, tussocks and coarse woody debris that characterized the reference

plots (Table 3.6). Conservative wetland species (e.g., Quercus bicolor, Nyssa sylvatica)

67

which can tolerate saturated soil conditions, may prefer the range of hydrologic

conditions offered by the more microtopographically diverse reference plots.

Physiographic factors and landscape position were likely very important in

shaping the canopy composition of some plots. The PA reference plot and TC mitigation

plot were more similar to each other than to other plots as indicated by the cluster

dendrogram (Figure 3.8), and both were located in the Mosquito Creek/Pymatuning

Lowlands described by Woods et al. (1998) as a poorly drained area containing many

wetlands. This sub-ecoregion extends into the far northeastern part of Ohio, where Braun

(1950, 1961) describes a transition zone within the Beech-Maple Forest Region where

species typically found in the Hemlock-White Pine-Northern Hardwoods Forest Region

become more common. NMDS ordination shows that species associated with the

Hemlock-White Pine-Northern Hardwoods Forest Region (e.g., Betula alleghaniensis,

Pinus strobus) were strongly associated with the PA plot (Figure 3.9). Also, the

dominance of Acer rubrum in the PA and TC plots (Figure 3.8) is not surprising given the

importance of this species in forests of northeastern Ohio where soils tend to be poorly

drained (Widmann et al. 2009).

Landscape position was likely very important for the canopy development of the

CS mitigation plot. This plot was located in an area of abandoned river channels (i.e.,

oxbows) associated with the West Branch of the Rocky River. Consequently, species

typically associated with floodplains (e.g., Acer negundo, Platanus occidentalis and

Populus deltoides (Braun 1961, Gordon 1969, Andreas 1989)) were associated with this

plot (Figure 3.9). Drain tile destruction and ditch plugging characterized the restoration

68

treatment in this area (Envirotech consultants, personal communication 2011). As a

result of increased soil saturation and (or) inundation, the floodplain species associated

with this plot may be replaced with species that can tolerate poorly drained soil

conditions.

Canopy Structure

Although not statistically significant, mitigation plots had a higher mean stem

density of wetland trees than reference plots (Figure 3.6). This was primarily driven by

the high stem densities of the BI and HR plots (Table 3.4) that are in the stem exclusion

stage of forest development. It is very important to consider that the canopy structure of

these two plots may change drastically within the next several years. Mortality of

Fraxinus pennsylvanica, which dominates these two plots, is expected to increase

throughout Ohio due to emerald ash borer (Agrilus planipennis) infestation (Widmann et

al. 2009). As a result, forested wetlands dominated by Fraxinus pennsylvanica may

develop large canopy openings (i.e., gaps) which will have implications for both

overstory and understory plant community development in these areas.

The TC and CS mitigation plots supported canopy tree species in a broader range

of diameter classes than the BI and HR plots (Table 3.4), which is suggestive of the

understory reinitiation development stage and greater structural complexity (Oliver and

Larson 1996). This trend highlights one advantage of restoring, establishing or

enhancing wetlands in areas that are already forested: desired vertical structure may

already exist. However, manipulating areas that already support mature trees can have

69

negative effects on the long-term development of forested wetlands. This has been well-

documented in studies of “greentree reservoirs” in the Lower Mississippi Alluvial Valley

(LMAV). The impoundment of bottomland hardwood forests in the LMAV has been

shown to affect forest composition, tree mortality and recruitment (King et al. 1998,

Ervin et al. 2006).

In this study, we were able to identify some structural characteristics of the

sampled areas based on tree stem densities and the distributions of stems among diameter

classes. However, a more in-depth analysis of canopy structure that considers crown

dimensions and the relative positions of tree crowns in the canopy is needed to fully

understand how the canopies of mitigation and reference forested wetlands compare to

one another.

Subcanopy Composition

The subcanopy communities of the reference plots were generally dominated by

shrub and small tree species considered to be ecologically conservative, e.g., Vaccinium

corymbosum, Ilex verticillata, Amelanchier arborea, Hamamelis virginiana and

Cephalanthus occidentalis (Andreas et al. 2004). Subcanopy species composition in the

mitigation plots was characterized by the presence and relative importance of non-native

and invasive species (e.g., Rhamnus frangula and Rosa multiflora) and species that are

not shade-tolerant (e.g., Cornus amomum, Rosa palustris) (Mack 2007a) (Figure 3.9,

Table 3.3). Differences in disturbance history and forest development stage may partly

explain these compositional differences.

70

During the stem exclusion phase of forest development very few plants are able to

successfully establish in the understory (Peet and Christensen 1987, Oliver and Larson

1996). This exclusion of new understory plants may be responsible for the absence of

shade-tolerant shrub and small tree species in the BI and HR plots, and indicates that

shrub species persisting in these plots represent biological legacies of the early

successional emergent or scrub-shrub wetland communities that once occupied the

sampled areas. Furthermore, certain physical habitat attributes (e.g., coarse woody debris

and hummocks) that may favor the establishment of some woody species by creating

elevated microsites (Huenneke and Sharitz 1986, Titus 1990, Blood and Titus 2010) are

lacking in the BI and HR plots (Figure 3.9). Physical habitat attributes that are legacies

of disturbance may not develop in these forests until the canopy trees mature, die and fall

to the forest floor (Lindenmayer and Franklin 2002).

Several shade-tolerant and shade-facultative wetland shrub species (e.g., Ilex

verticillata, Cephalanthus occidentalis, Lindera benzoin, Vaccinium corymbosum) were

strongly associated with reference plots (Figure 3.9). In addition, ecologically

conservative species (e.g., Ilex verticillata, Cephalanthus occidentalis, Vaccinium

corymbosum, Amelanchier arborea, Hamamelis virginiana) (Andreas et al. 2004) were

also associated with reference plots (Figure 3.9). Our ordination suggests that the

presence of certain physical habitat attributes may be partly responsible for the

establishment and persistence of these shrub species (Figure 3.9). The processes through

which physical habitat attributes may promote woody plant establishment are discussed

later in this chapter.

71

As we suggested for the canopy tree composition, landscape position may have

influenced subcanopy species composition. The BB reference plot was located near a

kettle-hole bog (Brown’s Lake Bog (The Nature Conservancy 2011)) and Vaccinium

corymbosum, a dominant shrub of bogs in Ohio (Andreas 1989), was an important

species in this plot. It is likely that this species was able to persist in the sampled area

following canopy closure over what was once an open bog community. Vaccinium

corymbosum is known to occur in the understory of swamp forests of northeastern Ohio

(Braun 1961).

Although mitigation plots generally lacked hummocks, tussocks, coarse woody

debris and had shallower litter layers (Table 3.6), the TC mitigation plot was positively

associated with some of these features (Figure 3.9). The presence of these features in the

TC plot may represent a legacy of the mature hummock-hollow forest that characterized

this area prior to hydrologic enhancement (Mack and Micacchion 2006), and helps

explain the presence of Hamamelis virginiana, a conservative shrub species that occurred

exclusively on remnant hummocks in this plot (personal observation).

Subcanopy Structure

Our results indicate that reference forested wetlands had a significantly higher

density of wetland shrub species and significantly higher subcanopy species importance

values (IV) than mitigation areas (Figures 3.4 and 3.5). The density of wetland shrubs

reflects the abundance of native shrub species with a facultative wetland or obligate

wetland (Reed 1988) indicator status. The subcanopy IV metric is a measure of the

72

importance of native shrub or small tree species that tolerate at least partial shade

regardless of their wetland indicator status (Table 3.2) (Mack 2007b, 2009). Both of

these measures were significantly higher in reference plots indicating that they have more

complex vertical and horizontal subcanopy structure than mitigation plots. Shrub species

that characterize mitigation plot subcanopies (e.g., Cornus amomum, Rosa palustris,

Rosa multiflora) (Figure 3.9) may not survive in these forests due to either intolerance of

shade or a preference for drier habitats (Reed 1988, Mack 2007a).

The lower density of shade-tolerant wetland shrubs in mitigation plots can be

better understood by considering disturbance history and forest development stage. Two

mitigation plots, BI and HR, are young forests developing on post-agricultural land and

are still in the stem exclusion stage of development which is characterized by self-

thinning and a lack of new woody plant establishment in the understory (Peet and

Christensen 1987, Oliver and Larson 1996). The young dense canopies of these plots

likely have not experienced gap-forming disturbances that temporarily increase available

light and encourage the establishment and growth of woody understory plants (Peet and

Christensen 1987). In young even-aged forests, canopy gaps resulting from tree mortality

are quickly occupied by expansion of the crowns of neighboring trees (Oliver and Larson

1996). Even in the absence of canopy gaps, the lack of light penetration through the

young dense canopy may discourage woody subcanopy species establishment which has

been strongly correlated with increased light levels (Van Pelt and Franklin 2000).

Minor disturbances also often leave structural legacies (i.e., pits, mounds and

coarse woody debris) (Lindenmayer and Franklin 2002) on the forest floor. These

73

legacies influence environmental conditions such as the spatial variability of hydrologic

conditions, and soil nutrient and soil oxygen heterogeneity which affect plant growth and

establishment (Vivian-Smith 1997, Neatrour et al. 2005, 2007). Furthermore, elevated

microsites associated with physical legacies may provide “safe sites” or “windows of

opportunity” that allow shrub and small tree species to become established (Huenneke

and Sharitz 1986, Jordan and Hartman 1995, Eriksson and Froborg 1996). Although

first-year woody plant seedlings have been found to be most abundant in wet depressions

in forested wetlands (Blood and Titus 2010), flooding may ultimately inhibit their

survivorship and growth (Kozlowski 1984, Anderson et al. 2009), and seedlings that

survive longer than one year likely established on elevated microsites (Titus 1990, Blood

and Titus 2010).

Following establishment, the presence of some shrub species in deeply shaded

forest understories may be maintained through vegetative reproduction rather than sexual

reproduction (Kanno and Seiwa 2004, Matlaga and Horvitz 2009). Vegetative

reproduction presumably favors shrub species in forests because new stems are able to

rely on stored carbohydrates from their parents until gap formation allows rapid growth

in the presence of increased light (Kanno and Seiwa 2004). Additionally, the physical

connection to parent plants prevents stems from being easily uprooted by flooding

disturbance (Huenneke and Sharitz 1986).

In this study, many native shade-tolerant and shade-facultative shrub and small

tree species were associated with physical habitat attributes that characterized the

reference plots (Figure 3.9). This suggests that certain habitat characteristics of relatively

74

undisturbed forested wetlands may promote the establishment of native subcanopy

species that will be long-term structural components due to their tolerance of shaded

conditions. This is consistent with other research that investigated understory vegetation

as it relates to forest maturity, structural complexity and microhabitat heterogeneity

(D’Amato et al. 2009). Many of the shrub species associated with mitigation plots (e.g.,

Cornus amomum, Rosa palustris, Rubus allegheniensis) are shade-intolerant species that

represent biological legacies of the open emergent or scrub-shrub wetland communities

that likely occupied these sites prior to canopy closure. These species would not be

expected to persist as forest development progresses.

Barring any major disturbance, the subcanopy strata of the CS and TC mitigation

plots may begin to resemble those of the reference plots sooner than BI and HR given

their more advanced development stage. The stem exclusion stage that characterized the

young BI and HR plots may last for several decades before a stratum of shade-tolerant

shrubs becomes established (Oliver and Larson 1996). The forests of CS and TC will

likely be influenced by minor disturbances in the near future that create gaps and leave

physical legacies that encourage the establishment of native, shade-tolerant shrub and

small tree species. NMDS ordination indicated that the TC plot may already have some

physical habitat attributes similar to those of the reference plots (Figure 3.9). However,

the manipulation of hydrology (i.e., dam and berm construction) in the TC mitigation

area which has already caused canopy tree mortality and encouraged the dominance of

species adapted to full sun conditions (Chapter 2), may inhibit the establishment of

shade-tolerant subcanopy species for many years.

75

Standing Dead Trees

Standing dead trees were important structural components of both the mitigation

plots and the reference plots. However, the processes responsible for tree mortality may

differ between the plot types. The standing dead trees in the BI and HR mitigation plots

were likely casualties of the intense competition for canopy space that characterizes self-

thinning and the stem exclusion development stage (Peet and Christensen 1987, Oliver

and Larson 1996). Tree mortality in the reference plots may be more influenced by

disturbance (e.g., windthrow, impacts from falling trees, and disease) which have been

documented as important causes of tree mortality in mature forests (Larson and Franklin

2010). Natural processes may also have contributed to the presence of standing dead

trees in the TC and CS mitigation plots that had existing, relatively mature canopies at the

time of treatment. However, the aforementioned hydrologic disturbance in the TC plot is

likely responsible for the high importance of standing dead trees in that plot which was

highlighted in an Ohio EPA study of wetland mitigation banks (Mack and Micacchion

2006). Additionally, increased water retention resulting from hydrologic manipulation in

the CS mitigation area (Envirotech Consultants, personal communication 2011) may have

contributed to the deaths of some trees adapted to the well-drained conditions that

characterize the soil map unit in that area (Soil Survey Staff 2011) (Table 3.1). It is

difficult to predict the long-term response of canopy trees in the CS mitigation plot to the

hydrologic manipulation. Certainly, any area with a mature canopy that is subjected to

hydrologic manipulation should be monitored closely.

76


Soil characteristics likely play a role in shaping woody plant communities in the

sample plots. Soils in the reference plots have a significantly higher mean cation

exchange capacity and total phosphorus than those of mitigation plots, but no significant

differences were found in any of the other soil characteristics. Other researchers have

shown that soil properties vary among natural and mitigation wetlands (Moser et al.

2009), and that microtopographic relief may influence soil properties in forested

ecosystems (Beatty 1984, Ahn et al. 2009) and plant community development in

constructed depressional wetlands (Alsfeld et al. 2009). Since we collected a single soil

sample from each plot, we were not able to investigate the role soil characteristics may

have in shaping woody plant community composition especially as it relates to

microtopographic variation. Future studies of the effects of microtopographic

heterogeneity on soil characteristics in forested wetlands are needed to fully understand

the role soils play in shaping woody plant communities in Ohio’s forested mitigation

wetlands.

Conclusions

The results of this study indicate that both relatively undisturbed forested

wetlands and mitigation forested wetlands in northern Ohio are dominated by species that

characterize the elm-ash-maple forests known to occur in the state. However, a closer

examination reveals that young mitigation plots were characterized by the dominance of

Fraxinus pennsylvanica while more mature mitigation areas and reference plots had

77

canopies dominated by Acer spp. As the young forests mature, Acer spp. may become

more important in the canopies of these areas.

The undisturbed conditions of reference forested wetlands may provide more

suitable habitat for some ecologically conservative tree and shrub species that are adapted

to wet, shaded conditions. Our analyses suggest that the diversity of physical habitat

attributes that characterized reference plots in this study may be important for the

establishment of these conservative woody plants. We suggest that the development of

physical habitat features that increase microtopographic heterogeneity should be

incorporated into future forested mitigation wetland designs.

The techniques currently used to restore, establish and enhance forested wetlands

for the purpose of mitigation have resulted in forests representing a range of disturbance

histories and developmental stages with inherently different structural characteristics.

Although mitigation plots sampled for this study did support some shrub species, many of

these species are more typical of open, emergent or scrub-shrub wetlands than forested

wetland subcanopies. Structural complexity associated with native, shade-tolerant

wetland subcanopy species may not develop in mitigation wetlands for several decades.

The result may be a temporal shift in forested wetland structure across Ohio’s landscape.

As forested wetlands currently used for mitigation develop and new mitigation projects

are approved by regulatory agencies, more opportunities to study woody plant

community development in these areas will arise. Nevertheless, our study provides

important insight into the current status of plant community development of Ohio’s

78

forested mitigation wetlands and represents a point from which research can be

continued.

79

Figure 3.1: Locations of the four forested wetland mitigation plots (black dots) and the four relatively undisturbed reference forested wetland plots (open circles).

80

Figure 3.2: Mean (± 1 standard error) diversity values for woody plant species of mitigation and reference forested wetland plots.

Figure 3.3: Mean (± 1 standard error) species richness for woody plant species of mitigation and reference forested wetland plot

mitigation reference0.0

0.5

1.0

1.5

2.0

2.5

3.0

Evenness (Pielou's J)Shannon-Wiener IndexSimpson's Index


Spec

ies

Ric

hnes

s

0

5

10

15

20

25

81


Num

ber o

f wet

land

shr

ubs

per h

ecta

re

0

200

400

600

800

1000

Figure 3.4: Mean (± 1 standard error) density of wetland shrubs (number per hectare) of mitigation and reference plots. There was a significant difference between plot types (Welch two-sample t-test, p = 0.02).

Figure 3.5: Mean (± 1 standard error) subcanopy IV metric value of mitigation and reference plots. There was a significant difference between plot types (Welch two-sample t-test, p = 0.03).


Subc

anop

y IV

met

ric v

alue

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

82

Figure 3.6: Mean (± 1 standard error) density of wetland trees (stems per hectare) of mitigation and reference plots. There was no significant difference between plot types (Welch two-sample t-test, p = 0.20).

Figure 3.7: Mean (± 1 standard error) canopy species IV metric value in mitigation and reference plots. There was no significant difference between plot types (Welch two-sample t-test, p = 0.12).


Num

ber o

f wet

land

tree

s pe

r hec

tare

0

2000

4000

6000

8000

10000


Cano

py IV

met

ric v

alue

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Figure 3.8: Dendrogram from a hierarchical cluster analysis using woody plant species importance values from mitigation plots (black dots) and reference plots (open circles). The most important canopy tree species in each plot was added to the dendrogram to aid interpretation of the results.

Acer saccharinum

Fraxinus pennsylvanica

Acer rubrum

83

Figure 3.9: Nonmetric multidimensional scaling (NMDS) ordination of mitigation plots (black dots), reference plots (circles) and woody plant species (triangles). Species abbreviations are provided in Table 3.3. Environmental attributes represented by the vectors include: number of hummocks (# hummocks), number of tussocks (# tussocks), number of macrotopographic depressions (# macr. depr.), litter depth, water depth (H2O depth) and counts of coarse woody debris 12-40 cm in diameter (cwd 12-40) and greater than 40 cm (cwd > 40).

NMDS 1

-1.0 -0.5 0.0 0.5 1.0 1.5

NM

DS

2

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

speciesmitigation reference

PRUSER

QUEIMB

ILEVER

VACCOR

QUERUB

ACERUBNYSSYL

FAGGRA

FRANIGPINSTR

QUEALBSMIROTHAMVIR CRPCAR

BETALLAMEARB

STADEA

CAROVT

FRAPROLIRTUL

LINBENTOXRAD

VIBREC

CEPOCC

QUEBICULMAME

ACESACCARLACPARQUI

ROSMUL

ACENEGQUEMACVIBLEN

RIBAME

VIBDENCATBIGPOPDEL

PLAOCCRUBALL

QUEPAL

FRAPEN

GLETRI

ROSPAL

CORAMO

ROSSETVITRIP

RHAFRA

BB

PA

FW

AC

TCHR

BI

CS

cwd >40

cwd 12-40

# macr. depr.

# hummocks

# tussocks

litter depth

H2O depth84


Plot name and Symbol




(AC)

40.20569

-82.94516

Eastern

Corn Belt Plains

Condit silt loam,

0-1 percent slopes (CnA)


reference

None

Brown’s Lake Bog SNP

(BB)

40.68223 -82.06538 Erie/Ontario Drift and

Lake Plains

Carlisle muck (Ch)


Fowler Woods SNP

(FW)


Lake Plains


slopes (BnA)


Pallister SNP (PA)


Lake Plains

Caneadea-Canadice silt

loam, 0-2 percent slopes (CeA)


Big Island

(BI)

40.57825

-83.29400

Eastern

Corn Belt Plains

Latty silty clay

(La) and Paulding clay (Pa)


mitigation (bank)


Columbia

Station (CS)

41.31612

-81.92192

Erie/Ontario


Chagrin silt loam

(Ch)


mitigation

(consolidated)


Hale Road

(HR)

41.28845

-82.16198

Erie/Ontario


Mahoning silt

loam, 0-2 percent slopes (MgA)


mitigation (bank)


Trumbull Creek (TC)

41.65917

-81.00477

Erie/Ontario Drift and

Lake Plains

Platea-Darien silt loams complex, 0-2 percent slopes

(PtA)


mitigation (bank)


85

Table 3.2: A description of the Vegetation Index of Biotic Integrity importance value metrics used for forested wetland evaluation (Mack 2009).

Metric Abbreviation Type Description Subcanopy IV

Importance value

The sum of the mean importance values for native shade-tolerant subcanopy species plus the mean importance value of facultative shade subcanopy species

Canopy IV Importance value The mean importance value of canopy species (species which at maturity will occupy the upper canopy of the forest even if at the time of sampling they are growing in the subcanopy

86

87

Table 3.3: Mean (102) (± 1 standard error) importance values for woody plant species of mitigation and reference forested wetland VIBI plots. Nomenclature follows Andreas et al. (2004). Species Abbreviation Authority Mitigation Reference Acer negundo ACENEG L. 4.30 (4.30) 0.00 (0.00) Acer rubrum ACERUB L. 15.97 (13.08) 17.80 (10.47) Acer saccharinum ACESAC L. 16.04 (10.60) 38.17 (13.15) Amelanchier arborea AMEARB (F. Michx.) Fernald 0.00 (0.00) 2.92 (2.92) Betula alleghaniensis BETALL Britton 0.00 (0.00) 3.28 (3.28) Carpinus caroliniana CARCAR Walter 0.00 (0.00) 1.47 (1.47) Carya laciniosa CARLAC (F. Michx.) Loudon 0.00 (0.00) 1.51 (1.51) Carya ovata CAROVA (Miller) K. Koch 2.26 (1.45) 0.00 (0.00) Catalpa bignoniodes CATBIG Walter 0.78 (0.78) 0.00 (0.00) Cephalanthus occidentalis CEPOCC L. 0.00 (0.00) 7.70 (3.30) Cornus amomum CORAMO Mill. 1.99 (1.16) 0.00 (0.00) Crataegus sp. CRATAE N/A 0.77 (0.77) 0.00 (0.00) Fagus grandifolia FAGGRA Ehrh. 0.79 (0.79) 5.08 (4.13) Fraxinus nigra FRANIG Marshall 0.00 (0.00) 1.49 (1.49) Fraxinus pennsylvanica FRAPEN Marshall 45.89 (15.09) 6.55 (6.55) Fraxinus profunda FRAPRO (Bush) Bush 0.00 (0.00) 2.54 (2.54) Gleditsia triacanthos GLETRI L. 0.71 (0.71) 0.00 (0.00) Hamamelis virginiana HAMVIR L. 1.52 (1.52) 2.38 (2.38) Ilex verticillata ILEVER (L.) A. Gray 0.00 (0.00) 6.32 (4.78) Lindera benzoin LINBEN (L.) Blume 0.00 (0.00) 4.53 (1.77) Liriodendron tulipifera LIRTUL L. 0.00 (0.00) 0.75 (0.75) Nyssa sylvatica NYSSYL Marshall 0.77 (0.77) 8.22 (7.25) Parthenocissus quinquefolia PARQUI (L.) Planch. 0.00 (0.00) 0.75 (0.75) Pinus strobus PINSTR L. 0.00 (0.00) 0.75 (0.75) Platanus occidentalis PLAOCC L. 0.88 (0.88) 0.00 (0.00) Populus deltoides POPDEL W. Bartram ex

Marshall 3.25 (3.25) 0.00 (0.00)

Prunus serotina PRUSER Ehrh. 0.00 (0.00) 5.03 (3.10) Quercus alba QUEALB L. 0.00 (0.00) 0.80 (0.80) Quercus bicolor QUEBIC Willd. 1.42 (1.42) 6.88 (2.40) Quercus imbricaria QUEIMB Michx. 0.00 (0.00) 1.12 (1.12) Quercus macrocarpa QUEMAC Michx. 1.67 (1.67) 0.00 (0.00) Quercus palustris QUEPAL Muenchh. 10.67 (6.25) 2.49 (2.49) Quercus rubra QUERUB L. 0.00 (0.00) 3.25 (2.22) Rhamnus frangula RHAFRA L. 0.72 (0.72) 0.00 (0.00) Ribes americanum RIBAME Mill. 0.93 (0.93) 0.00 (0.00) Rosa multiflora ROSMUL Thunb. ex Murray 1.41 (1.41) 0.93 (0.93) Rosa palustris ROSPAL Marshall 2.32 (0.77) 0.00 (0.00) Rosa setigera ROSSET Michx. 0.71 (0.71) 0.00 (0.00) Rubus allegheniensis RUBALL Porter 0.73 (0.73) 0.00 (0.00) Smilax rotundifolia SMIROT L. 0.00 (0.00) 0.74 (0.74) Standing Dead STNDEAD N/A 25.88 (8.58) 18.85 (6.74) Toxicodendron radicans TOXRAD (L.) Kuntze 0.00 (0.00) 1.67 (0.97) Ulmus americana ULMAME L. 15.42 (4.71) 12.19 (4.19) Vaccinium corymbosum VACCOR L. 0.00 (0.00) 2.21 (1.36) Viburnum dentatum VIBDEN L. 0.73 (0.73) 0.00 (0.00) Viburnum lentago VIBLEN L. 0.77 (0.77) 0.00 (0.00) Viburnum recognitum VIBREC Aiton 1.09 (1.09) 1.75 (1.75) Vitis riparia VITRIP Michx. 0.70 (0.70) 0.00 (0.00)

88

Table 3.4: Total number of live trees in each diameter class in mitigation and in reference plots. Plot abbreviations are provided in Table 3.1. Diameter Class (cm) Mitigation Plots Reference Plots BI CS HR TC AC BB FW PA 0 - < 1 41 4 49 1 0 0 0 8 1 - < 2.5 60 16 256 1 1 0 5 20 2.5 - < 5 87 38 300 1 9 4 14 30 5 - < 10 114 29 181 3 12 3 13 16 10 - < 15 83 17 34 4 13 6 11 2 15 - < 20 30 5 1 11 9 5 4 2 20 - < 25 0 14 0 6 4 5 0 1 25 - < 30 1 6 0 12 8 3 3 5 30 - < 35 0 5 0 10 3 8 5 1 35 - < 40 0 4 0 2 3 4 4 4 > 40 0 5 0 0 6 3 15 7

89

Table 3.5: Mean values (± 1 standard error) of soil characteristics from mitigation and reference forested wetlands. An asterisk indicates a significant difference in the mean values between mitigation and reference plots (Mann-Whitney test; p < 0.05).

Soil Characteristic Mitigation Reference soil reaction (pH) 5.89 (0.41) 5.23 (0.13) total phosphorus (μg/g) 8.60 (0.93) 44.98 (29.96)* total potassium (μg/g) 165.76 (25.87) 179.62 (15.92) total calcium (μg/g) 2031.66 (462.50) 3793.69 (811.67) total magnesium (μg/g) 396.73 (68.47) 619.18 (82.09) cation exchange capacity 21.24 (2.36) 41.78 (6.58)* % calcium 51.08 (13.27) 44.00 (4.08) % magnesium 16.30 (0.39) 12.67 (1.41) % potassium 2.04 (0.27) 1.20 (0.25) % nitrogen 0.25 (0.04) 0.84 (0.45) % carbon 2.40 (0.61) 10.47 (6.31) % organic matter 4.85 (0.10) 23.85 (13.46)

Table 3.6: Mean values (± 1 standard error) of physical attributes from mitigation and reference forested wetland plots. An asterisk indicates a significant difference in the mean values between mitigation and reference plots (Welch two-sample t-test; * p < 0.05).

Attribute Mitigation Reference depth of standing water 0.31 (0.31) 0.00 (0.00) litter depth 0.57 (0.26) 1.25 (0.43) number of tussocks 0.44 (0.36) 1.13 (0.24) number of hummocks 0.81 (0.81) 3.69 (1.08) number of macrotopographic depressions 2.25 (0.53) 3.75 (0.40) coarse woody debris 12-40 cm diameter 0.19 (0.19) 4.81 (1.36)* coarse woody debris >40 cm diameter 0.00 (0.00) 0.75 (0.45)

90

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Lindenmayer, D. B. and J. F. Franklin. 2002. Conserving Forest Biodiversity: A Comprehensive Multiscaled Approach. Island Press, Washington D.C.


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Mack, J. J. 2007b. Integrated wetland assessment program, part 9: field manual for the Vegetation Index of Biotic Integrity for Wetlands v. 1.4. Ohio EPA Technical Report WET/2007-6. Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio.


Matlaga, D.P. and C.C. Horvitz. 2009. Growth and survival across a gap-understory gradient: contrast in performance of sexually vs. clonally produced offspring. American Journal of Botany 96:439-447.

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McCune, B. and M.J. Mefford. 2006. PC-ORD. Multivariate analysis of ecological data. Version 5. MjM Software, Glenedon Beach, Oregon.

Micacchion, M. and B.D. Gara. 2008. An ecological and functional assessment of urban wetlands in central Ohio, volume 3: comparisons of the amphibian communities of urban and reference wetlands using level 1, 2 and 3 assessment tools. Ohio EPA Technical Report WET/2008-1. Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio.

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Mielke Paul W., J. and K. J. Berry . 2001. Permutation Methods: A Distance Function Approach. Springer Series in Statistics, New York, New York.

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Peet, R.K. and N.L. Christensen. 1987. Competition and tree death. Bioscience 37:586-595.

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95


Reed, P. B. J. 1988. National list of plant species that occur in wetlands: national summary. Department of the Interior, Fish and Wildlife Service, Washington D.C.

Riffell, S., T. Burton and M. Murphy. 2006. Birds in depressional forested wetlands: area and habitat requirements and model uncertainty. Wetlands 26:107-118.

Ruzicka, K.J., J.W. Groninger and J.J. Zaczek. 2010. Deer browsing, forest edge effects, and vegetation dynamics following bottomland forest restoration. Restoration Ecology 18:702-710.

Scanga, S.E. and D.J. Leopold. 2010. Population vigor of a rare, wetland, understory herb in relation to light and hydrology. Journal of the Torrey Botanical Society 137:297-311.

Schoenholtz, S.H., J.P. James, R.M. Kaminski, B.D. Leopold and A.W. Ezell. 2001. Afforestation of bottomland hardwoods in the Lower Mississippi Alluvial Valley: status and trends. Wetlands 21:602-613.





Van Pelt, R. and J.F. Franklin. 2000. Influence of canopy structure on the understory environment in tall, old-growth, conifer forests. Canadian Journal of Forest Research 30:1231-1245.

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Woods, A. J., J. M. Omernik, C. S. Brockman, T. D. Gerber, W. D. Hosteter and S. H. Azevedo 1998. Ecoregions of Indiana and Ohio, 1:1500000. Available at http://www.epa.gov/wed/pages/ecoregions/ohin_eco.htm. Accessed May 28, 2011.

97

CHAPTER 4

USING THE VEGETATION INDEX OF BIOTIC INTEGRITY TO COMPARE

FORESTED MITIGATION WETLANDS AND RELATIVELY UNDISTURBED

REFERENCE FORESTED WETLANDS

Abstract

The Vegetation Index of Biotic Integrity (VIBI) is currently used by regulatory

agencies in Ohio to assess wetland quality. It uses several plant community metrics,

tailored to specific wetland types, to quantitatively characterize wetland plant

communities for regulatory purposes. We utilized the VIBI for forested wetlands (VIBI-

Forest) to compare the plant communities of four mitigation forested wetlands with four

relatively undisturbed reference forested wetlands within the Erie/Ontario Drift and Lake

Plains and Eastern Corn Belt Plains ecoregions of Ohio. Mean values for VIBI-Forest

score (p = 0.01), Floristic Quality Assessment Index (FQAI) score (p = 0.03), percent

cover of sensitive species (p = 0.03), and importance values of subcanopy species (p =

0.03) were all significantly higher for reference plots compared to values calculated for

mitigation plots. NMDS ordinations indicated that reference plots were positively

associated with metrics related to FQAI score, number of seedless vascular plants,

number of plants that tolerate at least partial shade, importance of woody subcanopy

98

species, and percent cover of bryophytes, hydrophytes and sensitive plant species. The

ordinations also indicated an association between mitigation plots and metrics that reflect

the importance of woody canopy species, percent cover of plants with a wide range of

ecological tolerances and density of small trees. All four reference wetlands were

classified as Superior Quality Wetland Habitat based on Wetland Tiered Aquatic Life

Use categories. Three mitigation plots produced scores in the range of Restorable

Wetland Habitat and one qualified as Limited Quality Wetland Habitat. Our results were

consistent with those from other studies that used the VIBI for forested wetland

evaluation, and they provide additional support for the use of the VIBI-Forest for forested

wetland assessments in Ohio.

Introduction

Quantitative biological assessment methods have become important for regulatory

agencies responsible for characterizing ecosystems. Many of the methods currently in

use can be classified as indices of biotic integrity (IBIs) which use several metrics that,

when combined, characterize overall biotic integrity (Karr 1991). Some of the earliest

applications of IBIs focused on the assessment of fish and macroinvertebrate

communities (Karr 1981, Fausch et al. 1984, Kerans and Karr 1994). More recently, IBIs

were developed for use with other taxa including birds (O’Connell et al. 2000, DeLuca et

al. 2004), periphyton (Hill et al. 2000) and zooplankton (Lougheed and Chow-Fraser

2002). However, few IBIs have been developed that consider plant community

characteristics.

99

Beginning in 1996, ecologists at the Ohio Environmental Protection Agency

focused on developing biological assessment methods for wetlands. The goal was to

develop quantitative techniques that could be used by the regulatory community to assess

the biological integrity of wetlands. The Vegetation Index of Biotic Integrity for

Wetlands (VIBI) was first presented in the literature in 2000 (Mack et al. 2000), and

since that time it has been regularly used to assess wetland condition in Ohio (Mack and

Micacchion 2006, Micacchion and Gara 2008, Micacchion et al. 2010). Since the initial

protocol was published, additional testing iterations with larger datasets have resulted in

refined VIBI metrics and expanded applications to specific wetland types (Mack 2001,

Mack et al. 2008, Mack 2009). Furthermore, Wetland Tiered Aquatic Life Use

categories were developed that allow VIBI scores to be translated into specific designated

uses for wetlands (Mack 2004).

Applying the VIBI to forested wetlands in Ohio was particularly challenging

because: (1) forest canopies may remain intact even after the herb and shrub strata are

significantly disturbed, and (2) increases in diversity may be undesirable in forested

wetlands if the increase is due to the establishment of shade-intolerant species following

disturbance (Mack 2009). However, continued evaluation and revision of the plant

community metrics used in the VIBI for forested wetlands (VIBI-Forest) resulted in a set

of ten metrics that characterize forested wetland condition. Additionally, the modified set

of VIBI-Forest metrics corrected problems that plagued previous versions, most notably

the sensitivity of metrics to increases in diversity following disturbance (Mack 2004,

2009).

100

The VIBI is currently used by both federal and state regulatory agencies to

evaluate the condition of wetlands in Ohio. The most consistent use has been for the

assessment of wetland mitigation bank performance. The Interagency Review Team,

which consists of representatives from the U.S. Army Corps of Engineers, U.S. Fish and

Wildlife Service, U.S. Environmental Protection Agency, Ohio Department of Natural

Resources, Natural Resources Conservation Service and the Ohio Environmental

Protection Agency, uses VIBI data to assess the plant community characteristics of

mitigation banks and determine appropriate credit release schedules (U.S. Army Corps of

Engineers, personal communication 2011). Given the importance of the VIBI for

wetland assessment in Ohio, we used the VIBI-Forest to characterize both forested

mitigation wetlands and relatively undisturbed reference forested wetlands in Ohio. The

objective of this chapter is to characterize and compare mitigation and reference forested

wetlands using results from the VIBI-Forest.

Study Area and Methods

Chapters 2 and 3 of this thesis provide detailed descriptions of the four mitigation

study areas and the four relatively undisturbed reference study areas (Figure 4.1). Table

4.1 summarizes the characteristics of the eight study area locations. Detailed descriptions

of the methods used to collect herbaceous vegetation data (Chapter 2), woody vegetation

data (Chapter 3) and soil and physical attribute data (Chapters 2 and 3) are provided in

earlier chapters. A complete description of the VIBI data collection protocol which was

followed for this study is provided in the VIBI field manual (Mack 2007b).

101

Vegetation Data Reduction and Metric Calculations

To calculate a VIBI-Forest score for each plot, a series of data reductions and

metric calculations were necessary as outlined in the VIBI field manual (Mack 2007b).

Data reduction calculations were completed using the Microsoft Excel spreadsheet

created by Mack (2007a) specifically for VIBI data analysis. Detailed descriptions of the

metrics used to produce VIBI-Forest scores are included in Table 4.2.


Differences in the mean values of overall VIBI-Forest scores and individual

metric values were determined using Welch’s two-sample t-tests and Mann-Whitney

nonparametric tests for group differences. Prior to testing for significant group

differences, a Shapiro-Wilk test for normality was used to determine whether a

parametric or nonparametric procedure was most appropriate for each analysis. A

significance level of 0.05 was used to evaluate if differences between groups were

statistically significant.

Nonmetric multidimensional scaling analysis (NMDS) was used to examine

herbaceous and woody plant community composition in relation to the VIBI-Forest

metric scores (Figure 4.2). NMDS is generally the most effective ordination method to

use on ecological data sets and its advantages include: (1) avoiding assumptions of linear

relationships among variables, (2) relieving the “zero truncation problem” associated

with all ordinations of community data sets, and (3) allowing the use of any distance

measure or relativation (McCune and Grace 2002). NMDS uses an iterative search to

102

converge on a solution that minimizes “stress” in the configuration. Stress is a function

of a linear monotone transformation of observed dissimilarities and ordination distances.

The NMDS procedure was performed using the VEGAN metaMDS function (Oksanen et

al. 2010) in the R statistical computing program (R Development Core Team 2009). The

relationship between plant community composition and the VIBI-Forest metric values

was examined by fitting vectors representing the ten VIBI-Forest metrics to the

ordinations (Figure 4.2).

Results

VIBI Metrics

The results of the tests of group differences indicated that there are significant

differences between mitigation and reference plots in overall VIBI-Forest score (p =

0.01), FQAI score (p = 0.03), percent cover of sensitive plant species (p = 0.03), and the

importance of woody subcanopy species (p = 0.03) (Figure 4.3, Table 4.3).


The NMDS analysis of herbaceous plant species relative cover values converged

on a stable solution after five tries with a final stress value of 5.98. The NMDS analysis

of woody plant species importance values converged on a stable solution after one try

with a final stress value of 3.53. The ordination plots depict sample plot positions

relative to vectors representing VIBI-Forest metric values (Figure 4.2). The vectors point

in the direction of the most rapid change in the metric value scores. The ordination of

103

herbaceous plant species relative covers (Figure 4.2a) and the ordination of woody plant

species importance values (Figure 4.2b) showed similar trends. In both ordinations,

reference plots were generally associated with metrics related to the occurrence of shade-

tolerant species (shade), FQAI score (FQAI), importance of subcanopy species

(subcanopy IV), relative cover of seedless vascular plants (SVP), and percent cover

values for bryophytes (% bryophyte), hydrophytes (% hydrophyte) and sensitive species

(% sensitive). Mitigation plots were generally associated with metrics related to the stem

density of small trees (small tree), percent cover of species with a wide range of

ecological tolerances (% tolerant) and the importance of canopy tree species (canopy IV)

(Figure 4.2). A detailed description of the metrics is provided in Table 4.2.

Discussion

VIBI Scores

Our results indicate that the metrics developed for these study areas using VIBI-

Forest are accurate indicators of forested wetland biological integrity. The significantly

higher mean VIBI-Forest score for reference wetlands (Figure 4.3) compared with the

score for mitigation wetlands agrees with the results from other studies that used the VIBI

to compare natural and mitigation wetlands in Ohio (Mack and Micacchion 2006,

Micacchion and Gara 2008, Micacchion et al. 2010,). Additionally, the significantly

higher values of FQAI, SVP and % sensitive metrics for reference plots suggests that

reference plots may provide habitat that favors the establishment of seedless vascular

plants (i.e., ferns and fern allies) and plants thought to have a narrow range of ecological

104

tolerances (Andreas et al. 2004). Though not statistically significant, mean values for

shade, % bryophyte and % hydrophyte metrics were higher for reference plots. This

trend suggests that conditions in reference forested wetlands may be more favorable to

the establishment and persistence of shade-tolerant plants, mosses and some liverworts,

and plants adapted to wet environments than conditions in mitigation plots.

Mitigation plots had higher mean values for the % tolerant, small tree and canopy

IV metrics. These higher values indicate a tendency for mitigation forested wetlands to

support more species believed to have a wide range of ecological tolerances (Andreas et

al. 2004) and more trees in smaller diameter classes. The higher mean canopy IV metric

value indicates that tree species that would be dominant canopy species at maturity are

more important in mitigation plots than in reference plots. As we discussed in Chapters 2

and 3, two of the mitigation plots, Hale Road (HR) and Big Island (BI) are young forests

in the stem exclusion stage of forest development. This stage is characterized by many

young trees competing for position in the canopy (Peet and Christensen 1987, Oliver and

Larson 1996). The canopies of these two plots are dominated by Fraxinus

pennsylvanica. The dominance of this species and the corresponding high canopy IV

metric value that characterizes young, disturbed forested wetlands has been noted by

Mack (2004, 2009).

In Chapters 2 and 3, we discussed how physical attributes, physiographic factors

and landscape position may have influenced plant community composition and structure

of the sample plots. Given that the VIBI-Forest metric values are directly influenced by

plant community composition and structure, the same plot characteristics we discussed in

105

the earlier chapters (e.g., forest development stage and physical habitat attributes) likely

influenced the VIBI-Forest metric scores. The significantly higher number of seedless

vascular plants in reference plots can be attributed to the availability of suitable

microsites (e.g., coarse woody debris and hummocks) that are important for fern

colonization in forests and can be lacking in areas recovering from agricultural land use

(Flinn 2007). In addition, the presence of physical attributes that provide elevated

microsites may help explain the significantly higher subcanopy IV metric values of

reference plots. Other authors have documented the influence of microsites on woody

plant establishment in forested wetlands (Huenneke and Sharitz 1986, Titus 1990, Blood

and Titus 2010), and this topic is discussed in detail in Chapter 3 of this thesis.

The NMDS ordinations of herbaceous plant species relative covers and woody

plant species importance values (Figure 4.3) show the relationship between the species

compositions of sample plots and the VIBI-Forest metric scores. Several of the metrics

(shade, FQAI, % hydrophyte, % sensitive and % tolerant) are influenced by both

herbaceous and woody plant species while others like small tree, subcanopy IV and

canopy IV capture important aspects of woody plant community structure. Both

ordinations generally illustrate the same trend: reference plots were associated with

higher shade, SVP, % bryophyte, % hydrophyte, % sensitive and subcanopy IV metric

values, and mitigation plots were associated with small tree, % tolerant and canopy IV

metric values (Figure 4.2). These ordinations support results reported by Mack (2009)

who showed that high quality forested wetlands were associated with shade, FQAI, %

106

hydrophyte, % sensitive, SVP metrics and subcanopy IV metrics and that low quality

wetlands were associated with small tree, % tolerant and canopy IV metrics.

Wetland Tiered Aquatic Life Use Designations

One goal that drove the development of quantitative wetland assessment methods

for Ohio wetlands was the desire to relate biological integrity to specific uses for

wetlands (Mack 2001, 2004). In response, Mack (2004) published Tiered Aquatic Life

Use (TALU) categories for Ohio wetlands. The wetland TALU categories were proposed

using plant community, landscape position and ecoregional considerations (Mack 2004).

The general categories are: Superior Wetland Habitat (SWLH), Wetland Habitat (WLH),

Restorable Wetland Habitat (RWLH) and Limited Quality Wetland Habitat (LQWLH)

(Table 4.4). These categories correspond to wetland antidegradation categories (i.e.,

Category 1, 2 or 3) as specified by Ohio Administrative Code 3745-1-54 (Mack 2004).

Table 4.5 provides depressional forested wetland VIBI scoring breaks for the TALU

categories. Scoring breaks for all other wetland types are provided by Mack (2004, Table

11). It is important to note that wetlands within the Erie/Ontario Drift ad Lake Plains

(EOLP) ecoregion are held to higher scoring standards than wetlands within all other

ecoregions. This is due to the significantly higher VIBI scores found for EOLP wetlands

when compared with wetlands from other ecoregions (Mack 2004). All four reference

forested wetland plots in this study produced overall VIBI-Forest scores within the

scoring range of SWLH. One mitigation plot (BI) produced a VIBI-Forest score in the

range of LQWLH while the other three were categorized as RWLH (Table 4.6). The

107

very low score for BI (9) was likely driven by the dominance of the invasive, non-native

plant Lysimachia nummularia in the herbaceous layer. Our results are consistent with

those from a study of Ohio wetland mitigation banks in which more than 90% of the

wetland areas evaluated were categorized as either LQWLH or RWLH and none of the

sampled mitigation wetlands produced scores in the range of SWLH (Mack and

Micacchion 2006).

Conclusions

Our results provide support for the use of the VIBI-Forest in assessing biological

integrity of forested wetlands in Ohio. The ten metrics presented by Mack (2009)

characterize plant community attributes associated with forest understories as well as

forest canopies and appeared to separate relatively undisturbed forested wetlands from

those that have been disturbed or manipulated by humans. To reiterate our conclusions

from the previous chapters, the techniques used to restore, establish and (or) enhance

forested wetlands for use as wetland mitigation generally result in plant communities in a

range of development stages that have inherently different compositions and structures.

As the plant communities of forested mitigation wetlands continue to develop and new

mitigation projects are completed, there will be increased opportunity to study plant

community development in these areas. Nevertheless, the results presented in this

chapter provide support for the VIBI-Forest as a way of assessing the plant communities

of forested wetlands in Ohio and represents a point from which additional research can be

conducted.

108

Figure 4.1: Locations of the four forested wetland mitigation plots (black dots) and the four relatively undisturbed reference forested wetland plots (open circles).

109

(a)

(b)

Figure 4.2: Nonmetric multidimensional scaling ordinations of mitigation and reference forested wetland plots based on (a) herbaceous species relative cover values and (b) woody plant species importance values. Vectors represent VIBI-Forest metric values. A complete description of the VIBI-Forest metrics is provided in Table 4.2.

NMDS 1

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

NM

DS

2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8


TC

PA

HR

BI

CS

FW

AC

BB

can IV

% tolsmall tree

% bry% sen

SVP

sub IVFQAI % hydshade

NMDS 1

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

NM

DS

2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8


TC

PA

HR

BI

CS

AC

FW BB

can IV

% tol

small tree

shade

% bry

% hydr% sen

sub IV

FQAI

SVP

110

Figure 4.3: Mean (± 1 standard error) VIBI-Forest scores for mitigation and reference forested wetland plots. Means of the two plot types were significantly different (p = 0.01).


Mea

n VI

BI-F

ores

t Sco

re

0

20

40

60

80

100


Plot name and Symbol




(AC)

40.20569

-82.94516

Eastern Corn Belt

Plains

Condit silt loam, 0-1

percent slopes (CnA)


reference

None

Brown’s Lake Bog SNP

(BB)

40.68223 -82.06538 Erie/Ontario Drift and Lake Plains

Carlisle muck (Ch) palustrine, forested reference None

Fowler Woods SNP

(FW)

40.97188 -82.47080 Erie/Ontario Drift and Lake Plains


slopes (BnA)


Pallister SNP

(PA)

41.63452

-80.90465

Erie/Ontario Drift and Lake Plains

Caneadea-Canadice

silt loam, 0-2 percent slopes

(CeA)


reference

None

Big Island

(BI)

40.57825

-83.29400

Eastern Corn Belt

Plains

Latty silty clay (La) and Paulding clay

(Pa)


mitigation (bank)


Columbia

Station (CS)

41.31612

-81.92192


Chagrin silt loam

(Ch)


mitigation

(consolidated)


Hale Road

(HR)

41.28845

-82.16198


Mahoning silt loam, 0-2 percent slopes

(MgA)


mitigation (bank)


Trumbull Creek (TC)

41.65917

-81.00477


Platea-Darien silt loams complex, 0-2 percent slopes (PtA)


mitigation (bank)


111

112

Table 4.2: A summary of the VIBI-Forest metrics from Mack (2009).

Metric Abbreviation Type Description Shade

Richness

Number of native shade or shade facultative species

SVP Richness Number of seedless vascular plant species

FQAI Richness index The Floristic Quality Assessment Index score calculated according to Andreas et al. (2004)

% Bryophyte Dominance ratio Relative cover of bryophytes

% Hydrophyte Dominance ratio Sum of relative cover values of shade or partial shade-tolerant FACW and OBL plant species in the herb and shrub stratums

% Sensitive Dominance ratio Sum of relative cover of plant species in herb and shrub stratums with a Coefficient of Conservatism of 6-10 (Andreas et al. 2004)

% Tolerant Dominance ratio Sum of relative cover of plant species in herb and shrub stratums with a Coefficient of Conservatism of 0-2 (Andreas et al. 2004)

Small tree Density ratio Sum of relative density of tree species in 10-15 cm, 15-20 cm and 20-25 cm diameter classes

Subcanopy IV Importance value The sum of the mean importance values for native shade-tolerant subcanopy species plus the mean importance value of facultative shade subcanopy species

Canopy IV Importance value The mean importance value of canopy species (species which at maturity will occupy the upper canopy of the forest even if at the time of sampling they are growing in the subcanopy

Table 4.3: A summary of the VIBI-Forest metric values for mitigation and reference plots. An asterisk indicates a significant difference in the mean metric value between mitigation and reference plots (p < 0.05). A detailed description of the metrics is provided in Table 4.2 and study plot abbreviations are included in Table 4.1.

VIBI-Forest Metrics Mitigation Plots Reference Plots BI CS HR TC mean AC BB FW PA mean Shade 6.00 26.00 10.00 17.00 14.80 35.00 28.00 21.00 22.00 26.50 SVP 0.00 1.00 0.00 3.00 1.00 1.00 5.00 2.00 5.00 3.25 FQAI 17.10 23.00 15.10 22.10 19.33 25.10 25.30 24.20 31.60 26.55* % Bryophyte 0.02 0.00 0.01 0.07 0.03 0.08 0.03 0.20 0.05 0.09 % Hydrophyte 0.01 0.20 0.01 0.15 0.09 0.45 0.90 0.12 0.50 0.49 % Sensitive 0.01 0.02 0.30 0.04 0.09 0.38 0.64 0.60 0.83 0.61* % Tolerant 0.59 0.72 0.06 0.46 0.46 0.24 0.28 0.05 0.01 0.15 Small Tree 0.24 0.2 0.03 0.25 0.18 0.19 0.13 0.11 0.04 0.12 Subcanopy IV 0.00 0.04 0.00 0.06 0.03 0.07 0.17 0.08 0.14 0.12* Canopy IV 0.22 0.16 0.26 0.19 0.21 0.16 0.16 0.16 0.16 0.16

113

114

Table 4.4: Descriptions of the Wetland Tiered Aquatic Life Use categories (TALUs) from Mack (2004)

Designation Code Definition

Superior Wetland Habitat

SWLH Wetlands that are capable of supporting and maintaining a high quality community with species composition, diversity, and functional organization comparable to the vegetation IBI score of at least 83% (five-sixths) of the 95th percentile for the appropriate wetland type and region.

Wetland Habitat WLH Wetlands that are capable of supporting and maintaining a balanced, integrated, adaptive community having a species composition, diversity, and functional organization comparable to the vegetation IBI score of at least 66% (two-thirds) of the 95th percentile for the appropriate wetland type and region.

Restorable Wetland Habitat RWLH Wetlands which are degraded but have a reasonable potential for regaining the capability of supporting and maintaining a balanced, integrated, adaptive community of vascular plants having a species composition, diversity, and functional organization comparable to the vegetation IBI score of at least 33% (one-third) of the 95th percentile distribution for the appropriate wetland type and region.

Limited Quality Wetland Habitat

LQWLH Wetlands which are seriously degraded and which do not have a reasonable potential for regaining the capability of supporting and maintaining a balanced, integrated, adaptive community having a species composition, diversity, and functional organization comparable to the vegetation IBI score of less 33% (one-third) of the 95th percentile for the appropriate wetland type and region.

115

Table 4.5: A summary of the VIBI-Forest scoring breakpoints for Wetland Tiered Aquatic Life Use categories (TALUs). LQWLH = limited quality wetland habitat, RWLH = restorable wetland habitat, WLH = wetland habitat and SWLH = superior wetland habitat. A complete description of the TALUs is provided in Table 4.8.

Ecoregion LQWLH RWLH WLH SWLH

EOLP

0 - 30

31 - 60

61 - 75

76 - 100

all others 0 - 24 25 - 50 51 - 62 63 – 100

116

Table 4.6: A summary of the ecoregion, VIBI-Forest score, study area type and Tiered Aquatic Life Use categories that characterize the study plots. A complete description of the Tiered Aquatic Life Use categories is provided in Table 4.8.

Study area Ecoregion VIBI-Forest Score

Study Area Type Tiered Aquatic Life Use Category

Alum Creek State

Park (AC)

Eastern Corn Belt

Plains

77

Reference

SWLH



88 Reference SWLH

Fowler Woods SNP (FW)


84 Reference SWLH

Pallister SNP (PA) Erie/Ontario Drift and Lake Plains

94 Reference SWLH

Big Island (BI)

Eastern Corn Belt Plains

9 Mitigation (Bank) LQWLH

Columbia Station (CS)


44 Mitigation (Consolidated)

RWLH

Hale Road (HR) Erie/Ontario Drift and Lake Plains

33 Mitigation (Bank) RWLH

Trumbull Creek (TC)


49 Mitigation (Bank) RWLH

117

REFERENCES



DeLuca, W.V., C.E. Studds, L.L. Rockwood and P.P. Marra. 2004. Influence of land use on the integrity of marsh bird communities of Chesapeake Bay, USA. Wetlands 24:837-847.

Fausch, K.D., J.R. Karr and P.R. Yant. 1984. Regional application of an index of biotic integrity based on stream fish communities. Transactions of the American Fisheries Society 113:39-55.


Hill, B.H., A.T. Herlihy, P.R. Kaufmann, R.J. Stevenson, F.H. McCormick and C.B. Johnson. 2000. Use of periphyton assemblage data as an index of biotic integrity. Journal of the North American Benthological Society 19:50-67.


Karr, J.R. 1991. Biological integrity - a long-neglected aspect of water resource management. Ecological Applications 1:66-84.

Kerans, B.L. and J.R. Karr. 1994. A benthic index of biotic integrity (B-IBI) for river of the Tennessee Valley. Ecological Applications 4:768-785.

Lougheed, V.L. and P. Chow-Fraser. 2002. Development and use of a zooplankton index of wetland quality in the Laurentian Great Lakes basin. Ecological Applications 12:474-486.

118

Mack, J. J. 2001. Vegetation index of biotic integrity (VIBI) for wetlands: ecoregional, hydrogeomorphic, and plant community comparisons with preliminary wetland aquatic life use designations. Final Report, U.S. EPA Grant No. CD985875-01, Volume 1. Ohio Environmental Protection Agency, Division of Surface, Wetland Ecology Group, Columbus, Ohio.

Mack, J. J. 2007a. Automated spreadsheets for calculating and reporting the vegetation index of biotic integrity (VIBI) metrics and scores v. 1.0.1. Ohio EPA Technical Report WET/2007-2. Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio.


Mack, J.J. 2009. Development issues in extending plant-based IBIs to forested wetlands in the Midwestern United States. Wetlands Ecology and Management 17:117-130.

Mack, J. J., L. J. Augusta, M. Micacchion and G. R. Sablak. 2000. Vegetation indices of biotic integrity (VIBI) for wetlands and calibration of the Ohio rapid assessment method for wetlands v. 5.0. Final Report, U.S. EPA Grant No. CD985276, Interim Report, U.S. EPA Grant No. CD985875,Volume 1. Ohio Environmental Protection Agency, Division of Surface, Wetland Ecology Group, Columbus, Ohio.


Mack, J.J., N.H. Avdis, E.C. Braig and D.L. Johnson. 2008. Application of a vegetation-based index of biotic integrity for Lake Erie coastal marshes in Ohio. Aquatic Ecosystem Health and Management 11:91-104.

McCune, B. and J. B. Grace. 2002. Analysis of Ecological Communities. MJM Software Design, Gleneden Beach, Oregon.

Micacchion, M. and B. Gara. 2008. An ecological and functional assessment of urban wetlands in central Ohio,volume 3: comparisons of the amphibian communities of urban and reference wetlands using level 1, 2 and 3 assessment tools. Ohio EPA Technical Report WET/2008-1. Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio.

119


O'Connell, T.J., L.E. Jackson and R.P. Brooks. 2000. Bird guilds as indicators of ecological condition in the central Appalachians. Ecological Applications 10:1706-1721.

Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, R. G. Ohara, G. L. Simpson, M. H. H. Stevens and H. Wagner. 2010. vegan: community ecology package. R package version 1.17-0. Available online at: http://CRAN.R-project.org/package=vegan. Accessed April 16, 2011.





U.S. Army Corps of Engineers. 2011. Personal communication.

120

CHAPTER 5

APPLICATIONS AND LIMITATIONS

The results of this study may aid the understanding of how plant community

characteristics differ between relatively undisturbed reference forested wetlands and

those that have been restored, established or enhanced to mitigate impacts to natural

wetlands. The plant community and physical habitat differences between the two study

area types can be used by regulatory agencies, policy makers and scientists to develop

ecological targets for future mitigation projects. Specifically, the results may be used to

identify species that characterize reference sites and that may be desirable additions to

forested wetland mitigation areas. Furthermore, the association of particular species with

certain physical habitat attributes may prove to be important for encouraging desirable

plant community development in forested mitigation areas and for better assessing the

loss of functions that accompanies wetland impacts. Plant community composition and

structure that more closely resembles those of relatively undisturbed areas could lead to

more efficient replacement of ecological functions and greater confidence in the wetland

mitigation process.

Although much useful information has been generated from this study, it certainly

has limitations that must be addressed. First, our sample consisted of eight forested

121

wetlands. This small sample size represents only a fraction of the variability in plant

community composition and structure that exists among forested wetlands throughout the

two ecoregions in which we sampled. Even within individual ecoregions, slight

differences in environmental conditions may result in unique plant species assemblages.

However, the sample size also highlights a very important consideration: there are

currently very few forested wetland mitigation areas in northern Ohio. In addition,

wetland mitigation areas, particularly mitigation banks, can be very large heterogeneous

areas. The data collected for this study may not be representative of other wetlands

within the mitigation areas that are currently used for forested wetland mitigation. Given

the limited sample size of this study, the results must be used cautiously during the

assessment and design of both natural and mitigation forested wetlands in Ohio and

elsewhere.

Another important limitation to consider is that we collected data during a single

growing season. Both plant community characteristics and physical habitat attributes

(e.g., depth of standing surface water), particularly in mitigation areas that have been

subjected to human manipulation, are likely to change in response to changing

environmental conditions and management activities. Collecting and analyzing data

across several growing season would provide the additional information needed to fully

understand the trajectories of the sampled areas.

Finally, we recognize the limits associated with only comparing mitigation areas

to mature, high-quality forested wetlands. Given the long history of landscape

disturbance throughout Ohio resulting from agricultural, residential and commercial

122

development, there is now a wide range of “natural” conditions associated with forested

wetlands in the state. High-quality reference forested wetlands certainly provide

ecological targets for restoration, but comparisons against natural wetlands across a range

of disturbance classes may be more useful.

The most important contribution of this study is the basis it provides for future

research on forested wetland mitigation. Despite the limitations, the results do provide

insight into the current ecological condition of some of Ohio’s forested mitigation

wetlands. As opportunities to collect data from a larger sample of mitigation areas

increase, our understanding of the plant community development of these areas will

improve.

123

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Appendix A: Study Area Descriptions

135

Study Area General description of stand characteristics, landscape setting and land use history


(AC)

High-quality elm-ash-maple wetland situated in a depression within a mature, second-growth beech-maple forest. The sampled area is within the boundaries of Alum Creek State Park, Delaware County, Ohio.


High-quality maple-dominated forested wetland adjacent to a kettle-hole bog. The 99-acre preserve was purchased by The Nature Conservancy in 1966 and has been protected from direct human disturbance since that time.

Fowler Woods SNP (FW)

High-quality elm-ash-maple wetland within old-growth beech-maple forest. The sampled area is within 187-acre Fowler Woods State Nature Preserve in Richland County, Ohio.

Pallister SNP (PA)

High-quality maple-dominated wetland situated in a depression within a mature, second-growth beech-maple forest. The sampled area is within Pallister State Nature Preserve in Ashtabula County, Ohio. The preserve contains mostly beech-maple woods, but also supports species typically found in the Hemlock-White Pine-Northern Hardwoods forest region.

Big Island

(BI)

Green ash-dominated forest within the 5,722-acre Big Island Wildlife Area. The sampled area was a fallow agricultural field when wetland mitigation construction activities began in the mid-1990s. Based on the presence of a young dense canopy and the lack of a well-developed understory, the sampled area appeared to be in the stem exclusion stage of stand development.*

Columbia

Station (CS)

Elm-ash-maple forest in an area of abandoned river channels (i.e., oxbows) within the historic floodplain of the West Branch of the Rocky River. The sampled area is within the Lorain County Metro Parks’ 409-acre Columbia Reservation. Historically, the area was logged and put into agricultural production. The sampled area had existing forest cover at the time of hydrologic restoration (i.e., drain tile destruction and ditch plugging).

Hale Road

(HR)

Green-ash dominated forest within the Lorain County Metro Parks’ 1,917-acre Carlisle Reservation. The sampled area was an abandoned agricultural field that had been ditched and tiled. Hydrologic restoration included drain tile destruction and ditch plugging. The sampled area is currently in the stem exclusion stage of forest development.*

Trumbull

Creek (TC)

Maple-dominated forested wetland within 462-acre Trumbull Creek Wetlands Mitigation Bank, which is located near the Geauga/Ashtabula County line in northeast Ohio. Prior to wetland mitigation construction activities, the area consisted of agricultural fields and some forest areas. The sampled area had existing forest cover with pockets of wetland at the time of hydrologic enhancement which was meant to encourage the development of more wetland habitat through berm and dam construction. The increased inundation resulted in some canopy tree mortality near the sampled area.

* Based on descriptions of stand development stages by Oliver and Larson (1996)

an assessment of plant community - ohiolink

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