National Park Service

U.S. Department of the Interior

Natural Resource Stewardship and Science

Wetland Monitoring Protocol for Cuyahoga 

Valley National Park

Narrative

Natural Resource Report NPS/HTLN/NRR—2016/1336
ON THIS PAGE

Mud is an occupational hazard when monitoring wetlands. Photo by Doug Marcum.

ON THE COVER

Wetland monitoring in Cuyahoga Valley National Park. Tapes delineate 10-m x 10-m modules within a larger sampling array. 

Photo by Sonia Bingham.
Wetland Monitoring Protocol for Cuyahoga 

Valley National Park

Narrative

Natural Resource Report NPS/HTLN/NRR—2016/1336

1

Sonia N. Bingham, 

2Craig C. Young, 2

Jennifer L. Haack-Gaynor, 

2,3Lloyd W. Morrison, and 2Gareth 

A. Rowell

1National Park Service

Heartland Network

Cuyahoga Valley National Park

15610 Vaughn Road

Brecksville, Ohio 44141

2National Park Service

Heartland Network

Wilson’s Creek National Battlefield

6424 W. Farm Road 182

Republic, Missouri 65738

3Physical Address

Missouri State University

901 S. National Avenue

Springfield, Missouri 65897

November 2016

U.S. Department of the Interior

National Park Service

Natural Resource Stewardship and Science 

Fort Collins, Colorado
ii

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Please cite this publication as:

Bingham, S. N., C. C. Young, J. L. Haack-Gaynor , L. W. Morrison, and G. A. Rowell. 2016. 

Wetland monitoring protocol for Cuyahoga Valley National Park: Narrative. Natural Resource

Report NPS/HTLN/NRR—2016/1336. National Park Service, Fort Collins, Colorado.

NPS 644/135129, November 2016 
iii

Contents 

Page

Figures....................................................................................................................................................v

Tables...................................................................................................................................................vii

Appendices..........................................................................................................................................viii

Executive Summary..............................................................................................................................ix

Acknowledgments.................................................................................................................................xi

Acronyms.............................................................................................................................................xii

Introduction............................................................................................................................................1

Wetlands in the U.S. and Ohio are a Vulnerable and Protected Natural Resource ........................1

Wetlands Support Biodiversity in Healthy Landscapes.................................................................3

Wetlands Support Water Quality and Reduce Flooding Even in Degraded 

Landscapes .....................................................................................................................................4

Monitoring Data Improves Decision-making and Builds Consensus for Protection .....................5

Wetland Types in Cuyahoga Valley National Park........................................................................6

Riverine Mainstem Wetlands....................................................................................................6

Riverine Headwater Wetlands.................................................................................................10

Slope wetlands.........................................................................................................................11

Depressional wetlands.............................................................................................................12

Lacustrine fringe wetlands ......................................................................................................13

Methods................................................................................................................................................15

Survey Design Decisions..............................................................................................................15

Sampling Objectives.....................................................................................................................17

Overview of Ohio EPA Methods .................................................................................................18

Ohio Rapid Assessment Method .............................................................................................18

Vegetation Index of Biotic Integrity........................................................................................18

Sampling Procedures....................................................................................................................19

Reference Frame Development ...............................................................................................19

Sample Selection for Rapidly Assessed Wetlands..................................................................20

Sample Selection for Intensively Assessed Wetlands.............................................................20

Sample Selection for Sentinel Sites.........................................................................................27
iv

Contents (continued)

Page

Field Procedures...........................................................................................................................27

Field Methods for Rapidly Assessed Wetlands.......................................................................27

Sampling Terminology............................................................................................................27

Field Methods for Intensively Assessed Wetlands..................................................................27

Field Methods for Sentinel Wetlands......................................................................................30

Field Methods for Hydrological Monitoring...........................................................................32

Field Safety...................................................................................................................................33

Data Processing, Analysis, and Reporting...........................................................................................35

Databases......................................................................................................................................35

Data Entry.....................................................................................................................................35

Data Verification and Validation..................................................................................................35

Data Protection .............................................................................................................................35

Data Analysis and Reporting........................................................................................................36

Data analysis............................................................................................................................36

Reporting .................................................................................................................................38

Operational Requirements ...................................................................................................................39

Personnel Requirements...............................................................................................................39

Qualifications and Training..........................................................................................................39

Annual workload ..........................................................................................................................39

Hiring Seasonal Biotechnicians....................................................................................................39

Volunteers ....................................................................................................................................39

Annual budget ..............................................................................................................................40

Literature Cited ....................................................................................................................................41
v

Figures

Page

Figure 1. Distribution of wetlands throughout Cuyahoga Valley National Park (CUVA). ..................2

Figure 2. Cuyahoga Valley National Park (CUVA) is situated within the Erie Gorges 

ecoregion (Woods et al. 1998). ..............................................................................................................7

Figure 3. Cross-sectional view of dominant wetland hydrogeomorphic classes 

(depressional, riverine, slope) in Cuyahoga Valley National Park. .......................................................8

Figure 4. Typical bottomland hardwood forest during a flooded year in Fawn Pond, 

Cuyahoga Valley National Park.............................................................................................................9

Figure 5. Beaver dam affecting hydrological regime at Vaughn Road, Cuyahoga Valley 

National Park. ........................................................................................................................................9

Figure 6. Headwater slope wetland feeding a first order stream in Cuyahoga Valley 

National Park. ......................................................................................................................................10

Figure 7. Queen of the prairie (Filipendula rubra) found in a slope wetland in Cuyahoga 

Valley National Park............................................................................................................................12

Figure 8. Egg mass of Ambystomid salamander found in small pool on an abandoned 

floodplain terrace. ................................................................................................................................13

Figure 9. Lacustrine fringe wetlands at Virginia Kendall Lake, Cuyahoga Valley 

National Park. ......................................................................................................................................14

Figure 10. Map of watersheds in Cuyahoga Valley National Park (CUVA) mapped with 

low, medium, and high landscape development intensity (LDI) scores..............................................21

Figure 11. Watersheds in Cuyahoga Valley National Park ranked and ordered by 

landscape development intensity index (LDI). ....................................................................................24

Figure 12. The preferred sampling array for (A) emergent, aquatic bed, and scrub-shrub 

wetlands, (B) forested wetlands, and (C) an example alternative sampling array for 

situations where the preferred array was not possible because of wetland shape, size, or 

vegetative composition. .......................................................................................................................28

Figure 13. Flowchart for sampling array (SA) placement in intensively assessed 

wetlands.. .............................................................................................................................................29

Figure B1. Diagram of steps required to assess wetlands using the “Wetland Monitoring 

Protocol for Cuyahoga Valley National Park”.....................................................................................64

vii

Tables

Page

Table 1. Wetland number and area distribution among the five major Hydrogeomorphic 

(HGM) classes at CUVA. ......................................................................................................................4

Table 2. Intensive survey work, including the Vegetation Index of Biotic Integrity (VIBI) 

and water level monitoring is completed at Sentinel Sites (Wetlands of Management 

Concern and Reference Sites) and Intensive Survey Sites...................................................................16

Table 3. The 19 metrics associated with the Vegetation Index of Biotic Integrity (VIBI) 

for emergent, shrub, and forested wetlands. Each specific VIBI consists of ten individual 

metrics..................................................................................................................................................19

Table 4. Vegetation Index of Biotic Integrity scoring ranges for specific plant 

communities and landscape position (i.e., hydrogeomorphic [HGM] class).......................................19

Table 5. National Landcover Database (NLCD) land-use classes identified within 

Cuyahoga Valley National Park that correspond with the Florida land-use classes, and the 

Landscape Development Intensity (LDI) coefficients (i.e. emergy coefficients) that were 

used to calculate the LDI scores in this study (Mack 2006) ................................................................23

Table 6. Summary of the watershed and wetland inclusion probabilities for those selected 

in the two-stage sample using a generalized random tessellation stratified design in 

Cuyahoga Valley National Park for intensively assessed wetlands.....................................................24

Table 7. List of 60 Survey Sites (Plot ID) with number of Modules, dominant vegetation 

type, the azimuth of the centerline, array of modules, and coordinates (UTM Zone 19 N, 

NAD 1983)...........................................................................................................................................25

Table 8. List of 37 intensively-assessed wetland sites, identified by PlotID, and showing 

wetland name, sample set (reference or wetland of management concern [WOMC]), 

dominant vegetation type, azimuth of the center line, array shape of modules, and 

location coordinates (UTM Zone 19 N, NAD 1983). ..........................................................................31

Table 9. Location and number of monitoring wells, staff gauges, and digital EcotoneTM 

water level measuring devices in wetlands that we monitor in Cuyahoga Valley National 

Park. .....................................................................................................................................................33

Table 10. Estimated annual budget for required for wetland monitoring in Cuyahoga 

Valley National Park............................................................................................................................40

Table C1. Summary statistics (Median, lower 25%, and upper 75%) and frequency 

distribution categories of variables collected from wetlands in the Fraser (2004) study (n 

= 48 wetlands). ...................................................................................................................................58
viii

Appendices 

Page

Appendix A: Wetland Monitoring Reference Frame...........................................................................49

Appendix B: Site Establishment Datasheet for Wetland Monitoring ..................................................51

Appendix C: Wetland Studies from Cuyahoga Valley National Park .................................................55

Appendix D: Wetland Sampling Workflow for Intensively Sampled Wetlands.................................63
ix

Executive Summary

Photo 1. Volunteers assist with wetland monitoring in Cuyahoga Valley National Park. This protocol 

requires robust involvement of citizen scientists. Photo by Sonia Bingham.

Cuyahoga Valley National Park includes nearly 2,000 wetlands within its legislative boundary. These 

wetlands consist of three dominant types: 1) riverine wetlands located adjacent to river and stream 

courses, 2) headwater and slope wetlands where groundwater first emerges to the surface, and 3) 

depressional wetlands where the topography supports the accumulation of water from various 

sources. These wetlands provide important habitats and ecological services within the park. Because 

of their sensitivity to surrounding land use, we have chosen to monitor wetlands as part of the 

National Park Service’s vital signs monitoring program.

To monitor wetlands, we will use standard protocols that have been developed by the Ohio 

Environmental Protection Agency. The methods include options for rapid assessment and intensive 

assessment. We will rapidly assess 250 wetlands throughout the park. We will also intensively assess 

60 wetlands. These wetlands are located in watersheds that show varying relative levels of human 

disturbance (i.e., low, medium, and high). We hope that allocating our sample in this way will allow 
x

us to understand how changes in wetlands may result from surrounding land uses. Finally, using the 

intensive methods, we will sample eight wetlands of management concern and eight reference sites. 

Wetlands of management concern are important to the park for explicit management reasons, while 

reference wetlands reflect our perceptions of a very high quality, undisturbed example of a wetland. 

In addition to vegetation monitoring, we will also measure changes in hydrology (especially water 

depth) and water quality within each wetland. These data will be managed using standard GIS and 

database programs.

Over time, the results of this work should provide an overview of the condition of wetlands within 

Cuyahoga Valley National Park. In addition, we should be able to assess negative or positive changes

within these wetlands as measured by vegetation, hydrology, and water quality. These data will help

the park explain to surrounding communities how the actions in the surrounding landscape affect 

wetland systems within parks. While these data may indeed help to detect and diagnose changes in 

these park resources, we urge park managers to act boldly to protect park wetlands even in the 

absence of data that immediately identify effects of degradation. Monitoring and management work 

best as a unified rather than isolated approach to natural resource stewardship.
xi

Acknowledgments

We would like to acknowledge the hard work and dedication of numerous biological technicians who 

have assisted in pilot testing and conducting much of the field work required to develop this protocol: 

Doug Marcum, Brandon Piper, Luke Biscan, Bethany Wallace, and Alex Harnocz. We also thank 

Kevin Skerl for managing early protocol development and for providing critical guidance at key 

decision points in protocol development. We thank Meg Plona and Lisa Petit for their work on the 

early protocol development and for their commitment to and encouragement for the project. The 

partnership between the network and park has endured because of their leadership.
xii

Acronyms

CEQ: Council on Environmental Quality

CSE: Council of Science Editors

CUVA: Cuyahoga Valley National Park

DEM: Digital Elevation Model

EPA: Environmental Protection Agency

ESA: Ecological Society of America

FEMA: Federal Emergency Management Agency

FOM: Field Operations Manual

GIS: Geographic Information System

GLWQA: Great Lakes water Quality Agreement

GPS: Global Positioning system

GRTS: Generalized Random Tessellation Stratified 

HGM: Hydrogeomorphic Class

HTLN: Heartland Inventory and Monitoring Network

HUC: Hydrologic Unit Code

IJC: International Joint Commission

I&M: Inventory and Monitoring Program

km: kilometer

LaMP: Lake Erie Management Plan

LDI: Landscape Development Intensity Index

NCGICC: North Carolina Geographic Information Coordinating Council

NETN: Northeast Temperate Network

NLCD: National Landcover Dataset

NPS: National Park Service

NWCA: National Wetland Condition Assessment

NWI: National Wetland Inventory

OAC: Ohio Administrative Code

OEPA: Ohio EPA

ORAM: Ohio Rapid Assessment Method

PVC: Polyvinyl chloride

ROMN: Rocky Mountain Network

sej: solar emergy joules

SOP: Standard Operating Procedures

UCSA: Union of Concerned Scientists of America

USACE: United States Army Corps of Engineers

USEPA: United States Environmental Protection Agency

USGS: US Geological Survey

VIBI: Vegetation Index of Biotic Integrity

WOMC: Wetlands of Management Concern
xiii

Photo 2. Sonia Bingham and Doug Marcum following the final deployment of monitoring well in

Cuyahoga Valley National Park. Photo by Sonia Bingham. 

1

Introduction

A basic premise of biological monitoring is that organisms integrate the effects of numerous 

environmental variables over time. As such, the abundance or physiological condition of organisms, 

population structure, or composition of biological communities may serve as indicators that provide

more efficient and robust detection of environmental problems than examining variables in isolation. 

Taking such an approach to the measurement of “biological integrity” (Karr 1981) led us to monitor 

wetlands in Cuyahoga Valley National Park (CUVA) (Figure 1) following methods outlined by the 

Ohio Environmental Protection Agency (Mack 2001, Mack and Gara 2015). These methods are 

similar to those used for the National Wetland Condition Assessment (USEPA 2011).

Wetlands in the U.S. and Ohio are a Vulnerable and Protected Natural Resource

In 1827, the Ohio and Erie Canal became the first major source of transportation connecting the cities 

of Cleveland, Akron, and Canton to Columbus and the Ohio River. The canal paralleled the 

Cuyahoga River between Akron and Cleveland and created opportunities for trading and 

manufacturing along the river banks. While the industrial and municipal settlement was welcomed, 

environmental degradation was a consequence. Unregulated dumping of wastes such as oil, paint, 

metals and raw sewage were pervasive pollution sources in the river and resulted in numerous fires 

on the Cuyahoga River as early as 1868 (Ohio History Central 2016). River fires continued for nearly 

a decade before catching the attention of Time magazine in 1969 when the Cuyahoga River was 

declared as one of the most polluted rivers in the U.S. This national attention raised the awareness of 

the general public and contributed to the passing of the 1972 Clean Water Act (Latson 2015).

Over half of global wetlands have been destroyed over the past two centuries (Fraser and Keddy 

2000, 2005), and many of the remaining habitats have been degraded by pollution and invasion by 

exotic species (Dahl and Allord 1996, Dahl and Johnson 1991, Kentula 1996, Tiner 1984). While 

wetland loss in the conterminous United States is estimated at nearly 51% (from 89 million to 43 

million ha or 220 to 107 million acres), wetland area within Ohio has diminished almost 90% (1.8 

million to 195,000 ha or 4.4 million to 481,900 acres) (Dahl 2006, Mitsch and Gosselink 2000). 

Furthermore, most extant Ohio wetlands have undergone some degree of physical, chemical or other 

biological alterations as a result of extensive deforestation during settlement (CEQ 1989) and current 

agricultural (52%) and urban (14%) landuse (Ohio Legislative Service Commission 2006). 

Many wetlands are water bodies of the United States (40 CFR 122.2, 40 CFR 230.3, and 40 CFR 

232.2) and protected under the Clean Water Act [Section 502(7)]. In Ohio, Ohio EPA wetland 

assessment methods can be used to determine restrictions on the use or development of particular 

wetlands. These anti-degradation categories include 1) limited quality wetland habitats (Category 1),

2) restorable wetland habitat (Modified 2), 3) wetland habitat (Category 2), and 4) superior wetland 

habitat (Category 3).
2

Figure 1. Distribution of wetlands throughout Cuyahoga Valley National Park (CUVA).
3

Wetlands Support Biodiversity in Healthy Landscapes

Most wetlands (77.7%) in the 2,106 km2(813 mi2) Cuyahoga River watershed are in good to 

excellent condition based on the Ohio Rapid Assessment Method (ORAM) for evaluating wetland 

integrity (Fennessy et al. 2007). The highest rates of degradation are documented in wetlands within 

the lower sub-basin of the Cuyahoga River, which includes CUVA. However, rapid assessments 

within CUVA boundaries to date indicate that 74% of the wetlands (n=595) and 89% of total wetland 

area within the park are still in good to excellent condition (Bingham, unpublished data). The 

protected landscape provides an important buffer for over 43 small watersheds that flow into the 

lower Cuyahoga River sub-basin. CUVA wetlands are an integral part of this landscape, as they 

intercept, retain, filter, and recharge surface and groundwater as it flows from the upper watersheds 

outside of the park, through the park and into its stream system. These wetlands are complex 

ecological systems that provide nursery, resting, feeding, and breeding grounds for numerous species 

of amphibians, birds, fish, and other wildlife. For example, CUVA wetlands are known to support at 

least 12 species of amphibians that spend a portion of their life cycle in water. The park supports 

populations of at least 4 partially aquatic mammals, including Mustela vison (American mink), 

Castor canadensis (beaver), Ondatra zibethicus (muskrat), and Lontra Canadensis, (Northern river 

otter), which was once extirpated from Ohio, have but have been reproducing in the Beaver Marsh 

since 2013. The Beaver Marsh, Fawn Pond, and several other important wetlands in the park are

known breeding grounds for over 45 different bird species (Marcum and Bingham 2017). More 

recently in 2016, we documented a second occurrence of the state threatened, Clemmys guttata

(spotted turtle) in the Beaver Marsh and nearby in an old canal wetland (Plona, unpublished). These 

species use a variety of wetland features such as dense vegetation, large woody debris (logs) deep 

water, hummock topography, and standing dead trees as their homes. 

The variety of wetland types in CUVA respond differently to environmental stress based on their 

water source and position in the landscape (i.e., hydrogeomorphic class or HGM). Ecological 

communities are impacted from air and water-born pollutants, physical damage, changes in 

groundwater levels or increased flooding, etc. (Brown and Vivas 2004). Nutrients and toxins carried 

in the runoff will negatively affect surface water and groundwater and any wetlands dependent on 

these water sources in affected watersheds (i.e., Slope and Riverine Wetlands), which include the 

majority of our wetlands (Table 1). Modifications to the inflow and outflow of surface and 

groundwater within a watershed will also result in measureable changes to wetland systems over time 

since wetland plant communities are sensitive to water level and water quality changes (Mitsch and 

Gosselink 2000). We expect that land-use practices will influence wetland integrity as a whole within 

CUVA boundaries more than any other environmental variable. Consequently, we will quantify landuse change over time using a Landscape Development Intensity Index (LDI), which is a land use 

based index of potential human disturbance (Brown and Vivas 2005). The LDI integrates land use 

into a single quantitative score. Emergy coefficients developed for the State of Florida and landcover 

datasets (30 m pixels) from the National Landcover Database (NLCD) in mapping zone 47 are used 

to calculate the LDI score. The NLCD data are available from the Multi-Resolution Land 

Characteristics Consortium website (MRLC 2016). New datasets will be available approximately 

every five years.
4

Table 1. Wetland number and area distribution among the five major Hydrogeomorphic (HGM) classes at 

CUVA.

HGM # Wetlands % Wetlands Area (acre / hectare) % Wetland Area

DP 402 25.28% 259.61 / 105.06 13.26%

SP 765 48.11% 794.75 / 321.62 40.59%

RM 174 10.94% 611.97 / 247.66 31.26%

RH 233 14.65% 220.13 / 89.08 11.24%

IMP 16 1.01% 71.40 / 28.89 3.65%

Total 1590 100.00% 1957.86 / 792.32 100.00%

Previous studies have shown how land use varies along the Cuyahoga River. Suburban development 

reduced forest and agricultural lands by nearly 25% in park subwatersheds between 1959 and 2002 

(aerial photos 1959 and 2000/2002) (Skerl, unpublished). Fortunately, forested land increased within 

the park boundaries as agricultural fields reverted to forest. Changes in land-use were minimal 

between 2001 and 2011 in park watersheds (Bingham, unpublished), according to the LDI. 

Wetlands Support Water Quality and Reduce Flooding Even in Degraded Landscapes

In addition to supporting biodiversity, wetlands function as “nature’s kidneys” because of their 

capacity to improve water quality, especially in degraded landscapes (Dahl 2006, Carter 1996, 

Mitsch and Gosselink 2000). Wetlands are capable of trapping and transforming heavy sediments, 

nutrients, trace metals, and organic materials that are transported in surface and ground water (Carter 

1996), which reduces agricultural and municipal pollution and the need for costly technological 

solutions (USEPA 1993a). Wetlands also assist in the retention of surface water. In densely 

populated watersheds, impervious surfaces create unusually high peak flows during storm events. 

These flows result in stream channel re-adjustments, severe erosion in some areas, heavy sediment 

deposition in others, extensive flooding, and aquatic habitat degradation. Wetlands provide storage to 

reduce the impact from peak flows. While wetlands can be resilient to small changes, cumulative 

impacts over time may permanently reduce the capacity of wetlands to provide these important 

ecosystem services (Brinson 1988). The “filtering” role of wetlands is prevalent in many areas of the 

park, particularly within the Cuyahoga River floodplain, where industry and urbanization were once 

prevalent or continue to persist. 

In 1972, the United States and Canada, through the International Joint Commission, established a 

Great Lakes Water Quality Agreement to identify pollution sources from both countries and to 

recommend remedial actions (IJC 2005). In a 1987 protocol amendment, the Cuyahoga River was 

designated as one of twenty-six areas of concern within the U.S that flow into the Great Lakes. The 

designation was given to the most severely degraded watersheds that failed to meet the objectives of 

the agreement (GLWQA 1987). Lake Erie was the first of the Great Lakes to experience major 

eutrophication problems, mostly from elevated nutrient levels (mainly phosphorus) that led to overproduction of algae and oxygen deprivation (Environment Canada and USEPA 2000). While 

phosphorus reduction has been a major goal since then, a Total Maximum Daily Load report for the 
5

Cuyahoga River watershed also lists organic enrichment, bacteria, and toxicity from industrial and 

municipal point sources as primary sources of impairment to the river and many of its tributaries 

(OEPA 2003), and ultimately, Lake Erie. Riverine wetlands are particularly important in offsetting 

these impacts, as their pulsing hydroperiod results in high rates of biomass production and 

decomposition, which increases nutrient cycling and can help to process pollutants quickly (Mitsch 

and Gosselink 2000, USEPA 2001). The protection and restoration of natural landforms and systems 

such as wetlands that mitigate the impacts of urban, industrial and agricultural land use are the most 

significant actions that can be taken to restore the Lake Erie ecosystem (Environment Canada and 

USEPA 2008). 

Riverine wetland restoration has been proposed in many Great Lakes watersheds as a viable solution 

to help combat eutrophication (Mitsch and Gosselink 2000). Within the Lower Cuyahoga River subbasin, CUVA includes some of the largest expanses of natural floodplain and protected riparian 

corridors (Holmes and Goebel 2008). These floodplains provide substantial flood storage and are

likely one of the most important regional nutrient and sediment sinks in the lower sub-basin of the 

Cuyahoga River. CUVA protects over 247.7 hectares (612.0 acres) of wetlands within the Cuyahoga 

River floodplain. A 6-inch rise in water over a 10-acre wetland places more than 1.5 million gallons 

of water in storage (Niering 1966, Goodwin and Niering 1975). CUVA is regarded regionally as a 

protected green corridor, but the benefits of its wetland resources are not fully understood. 

Monitoring Data Improves Decision-making and Builds Consensus for Protection

Wetland systems take time to understand. Hydrology is a fundamental driver in wetland 

communities, but can be difficult to study since water is often only present below ground and can 

change annually due to climate variation. The Sentinel Sites, including wetlands of management 

concern, were included in monitoring for the distinct purpose of diagnosing problems that may 

require management action. At these sites, management issues such high visitation, flooding, 

protection of rare plants and animals, and restoration of disturbed sites, require more attention and 

are monitored more closely, to make quicker decisions when needed.

Vegetation and water level monitoring data at intensively studied sites can be combined to develop 

plants species lists for targeted hydrologic regimes at restoration sites, leading to more successful 

projects. In wetlands with rare plant populations, monitoring data can be used to direct or prioritize 

EPMT efforts, if desired. Similarly, information learned from monitoring highly visited sites such as 

the Beaver Marsh can be used to assist with proper interpretation of our natural resources to the 

public through the Interpretation Education and Visitor Services division and help lead higher quality 

educational programs.

CUVA protects many cultural resources and historic infrastructure that often requires work in or near 

wetlands for maintenance. Working in or near wetlands with heavy equipment is especially difficult 

and can be very problematic in surface water systems that have sensitive aquatic communities. 

Insights gained from this effort can bring awareness to park staff and help divisions work together to 

develop solutions and SOP's for anticipated work such as invasive species removal, dam 

decommissioning, debris removal from flooding, etc. Larger scale planning efforts also benefit from 
6

awareness of the park’s wetland systems. Locations for proposed parking lots and trails are modified 

based on the spatial distribution and condition of wetlands in the park. We also expect that these data 

may be used to build consensus when working with stakeholders including consensus among coworkers, administrators, regulating agencies, and the public. For example, trends resulting from 

Survey Sites data may lead to scientific insights that link to wetland condition to watershed stress, 

which could lead to more specific research or lead to agreement on projects such as stream and 

wetland restoration projects.

Wetland Types in Cuyahoga Valley National Park

CUVA is situated within the Erie Gorges subregion of the Erie-Ontario Drift and Lake Plains 

ecoregion (Figure 2). The Erie Gorges are a transition zone between the Central Lowland plains to 

the west and the mountainous terrain of the Appalachian Plateau to the east (Woods et al. 1998). 

Steep slopes, rocky outcrops and high erosion rates characterize the plateau. CUVA protects a 

complex of fluvial landforms within this ecoregion, including a 35 km (22 mile) corridor of the 

Cuyahoga River, its floodplain, and adjacent ravines that contain over 300 km (186 miles) of 

perennial tributaries.

Topographic and hydrologic characteristics partition the park’s wetlands into four major 

hydrogeomorphic (HGM) classes (i.e., Riverine, Slope, Depressional, and Lacustrine Fringe) (Table 

1) which are defined by three fundamental factors: geomorphic setting, water source, and 

hydrodynamics (Brinson 1993, Smith et al. 1995) (Figure 3). These factors influence wetland 

vegetation and function since the quality and quantity of incoming water influences the 

biogeochemical properties of soil and water, and the location within the watershed will affect the 

extent to which the wetland transforms the water moving through it (Smith et al. 1995). Wetlands 

within each HGM class tend to have similar biological communities that respond similarly to 

disturbances (Fennessy et al. 2007, USEPA 2002f).

Riverine Mainstem Wetlands

Riverine mainstem wetlands cover 245.4 ha (606.3 acres) or 31% of the total wetland area, while 

only accounting for 11.3% of the number of wetlands (average size=1.5 ha/3.7 ac). Generally 

confined to lower elevations in the park, overbank flow from larger 4th order streams (Sagamore 

Creek, and Furnace Run below Rock Creek) and greater (5th order: Brandywine Creek, Chippewa 

Creek, and Yellow Creek; 6th order: Tinker’s Creek, 7th order: Cuyahoga River) dominates the 

hydrological regime of these wetlands for much of the year. This classification is different from 

Mack (2004), who also included wetlands along all 3rd order and 4th order streams. In CUVA, stream 

orders of 4 and less within CUVA generally have smaller drainage areas (between 2-3 square miles) 

as a result of the park’s steep gradients and numerous ephemeral stream channels. Streams with 

drainages less than 25.9 km2(10 mi2) are still classified as headwaters by the OEPA Division of 

Surface Water (1989). For this reason, we reserved the mainstem designation for the largest 

tributaries in the park. 
7

Figure 2. Cuyahoga Valley National Park (CUVA) is situated within the Erie Gorges ecoregion (Woods et 

al. 1998).
8

Figure 3. Cross-sectional view of dominant wetland hydrogeomorphic classes (depressional, riverine, 

slope) in Cuyahoga Valley National Park.

River discharge records from USGS gauging stations located upstream (Old Portage: 04206000) and 

downstream (Independence: 04208000) of the park indicate that the Cuyahoga River floods 0-2 times 

during most years for an average duration of 1 or 2 days, but can be much longer (Figure 4). In 

addition to these “flood pulses”, many wetlands in the Cuyahoga River floodplain are seasonally 

flooded from tributaries and groundwater seeps at the base of the valley walls. This is a switch from 

the overbank flow regime to a vertical hydrologic pathway (Figure 3). River reaches with natural 

bank levees may retain water from these pulses long enough to characterize large areas as wetlands 

(e.g., bottomland floodplain forests) (Mitch and Gosselink 2000). In other areas, the natural 

processes of sediment transport, erosion and deposition create sandy ridges, vernal pools, and 

oxbows that are common CUVA wetland landforms. Beavers (Castor canadensis) are also 

responsible for water retention in several wetlands in the park, including Ira Road Beaver Marsh, 

Kendall Lake, Fawn Pond, and Vaughn Road (Figure 5). 

Currently, most riverine forests in CUVA include silver maple (Acer saccharinum), red maple (Acer 

rubrum), box elder (Acer negundo), cottonwood (Populus deltoides), sycamore (Platanus 

occidentalis), and black willow (Salix nigra) as dominants in the canopy, and green ash (Fraxinus 

pennsylvanica), American elm (Ulmus americana), and Ohio buckeye (Aesculus glabra) as common 

understory dominants. The recent vegetation map using the National Vegetation Classification 

Standard (Hop et al. 2013), classified these communities as Midwestern cottonwood - Black willow 

Floodplain Forest (CEGL002018), and Silver maple - Elm Forest association (CEGL002586). This 

would also include gravelbar types, such as the very common Japanese Knotweed Temporarily 

Flooded Herbaceous Vegetation association (CEGL008472).
9

Figure 4. Typical bottomland hardwood forest during a flooded year in Fawn Pond, Cuyahoga Valley 

National Park.

Figure 5. Beaver dam affecting hydrological regime at Vaughn Road, Cuyahoga Valley National Park.
10

As open systems that allow lateral and longitudinal exchange of energy and materials, riverine 

wetlands are susceptible to landscape and watershed degradation (Mitsch and Gosselink 2000, 

Tickner et al. 2001). Water contaminated with nutrients, pesticides and/or metals, along with

stormwater impacts such as erosion, sedimentation and altered flood regimes are common

disturbances within CUVA (Stewart et al. 1998, URS Corporation 1986, Skerl and Plona 2007).

Invasive plant species also threaten the sensitive native plants and wildlife communities that depend 

on these habitats (Vorac 2003, Dengg 2004, Djuren and Young 2007). Two of the common plant 

species in riverine wetlands in CUVA, reed canarygrass (Phalaris arundinacea) and purple 

loosestrife (Lythrum salicaria), are known to have high ecological impact based on NatureServe Irank subranks (Morse et al. 2004).

Riverine Headwater Wetlands

Riverine headwater wetlands maintain surface water connections to headwater streams, which in 

Cuyahoga Valley include 1st order – small 4th order streams (i.e., headwaters; Figure 6). Many 

CUVA headwater streams are referred to with names: Adam Run, Boston Run, Brookside Creek, 

Columbia Run, CMA Creek, Dickerson Run, Langes Run, Ira Creek, Haskell/Ritchie Run, Oak Hill 

Creek, Peninsula Creek, Railroad Creek, Robinson Run, Sagamore Creek, Salt Run, Sand Run, 

Slipper Run, Spring Creek, Stanford Run, Stone Road Creek, Woodward Creek. Others are referred 

to as Unnamed Tributaries 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15, 16 and RM 15.11.

Figure 6. Headwater slope wetland feeding a first order stream in Cuyahoga Valley National Park. 
11

Riverine headwater wetlands that depend only on surface water may dry intermittently, while stable 

groundwater tables may ensure perennial water supply to others (Richardson and Daheny 2007). 

These differences in water source also affect the size and composition of riverine headwater wetlands 

(Hupp and Osterkamp 1996). Covering 95.6 ha (236.2 acres) in the park with an average size of 0.61 

hectares (1.5 acres), steep ravines and slopes typically buffer these wetlands from development,

although entrenchment from flashy storm water has led to gradual drying in some areas (Bingham, 

per obs.). The topographic position on alluvial benches results in wetlands that are narrow and long. 

The dominant canopy species in riverine headwater wetlands include American elm, green ash, sugar 

maple (Acer saccharum), tulip tree (Liriodendron tulipifera), slippery elm (Ulmus rubra), sycamore, 

and yellow buckeye (Holmes and Goebel 2008). Spicebush (Lindera benzoin) and hawthorn species

(Craetegus spp.) are common understory trees. Herbaceous understory plant composition is highly 

variable in these systems. Many flooded and swamp forest communities are classified in CUVA as 

Tuliptree - Elm species - Green Ash Forests (CEPS006684) (Hop et al. 2013).

Slope wetlands

Slope wetlands, with an average size of 0.5 hectare (1.1 acre) in CUVA, are at the origin of and feed 

many 1

st order streams in the park (Figure 6). They can also be found at mid-slope along steep 

ravines and are very common on low-gradient slopes at the base of the valley. In contrast to riverine 

headwater wetlands, wetlands within the slope HGM class are groundwater dependent and do not 

have an obvious surface water connection with a river or stream. In CUVA, groundwater often 

intersects the surface at topographic breaks and stratigraphic layers, such as the base of the valley 

walls and at impervious rock or soil layers (Figure 3). These groundwater seeps create the hydrology 

needed for wetlands to develop along slopes. More wetlands (49%) fall within the slope HGM class 

than any of the other major classes and altogether cover nearly 311.6 hectares (770 acres) of land 

within park boundaries. Slope wetlands vary in size and plant composition depending on whether 

they are recharged by stable volumes of groundwater or more influenced by local water tables and 

precipitation (Stein et al. 2004). 

The bedrock geology can also influence the concentration of minerals in the groundwater, possibly 

leading to the formation of fens. The accumulation of peats characterizes fens, the influx of 

groundwater minerals and higher pH differentiates fens from other wetlands. Fens are groundwater 

dependent and fall within the slope HGM class.

Many species with high coefficients of conservatism (0= minimum; 10=maximum) show a high 

degree of fidelity to slope wetlands (Figure 7; Andreas 2004). These species include skunk cabbage 

(Symplocarpus foetidus,7), swamp thistle (Cirsium muticum,8), Queen of the prairie (Filipendula 

rubra, 8), American beak grass (Diarrhena americana, 7), various sedges such as the hairy fruited 

sedge (Carex trichocarpa, 8) and brome sedge (Carex bromoides, 7), and several less common 

understory shrubs such highbush blueberry (Vaccinium corymbosum, 6). Spicebush (Lindera 

benzoin), dogwoods (Cornus spp.) and viburnum (Viburnum spp.) are common native shrubs within 

slope wetlands in CUVA. Many wetlands within the slope HGM class are Skunk-cabbage Seepage 
12

Meadows (CEGL002385) or have a mixed sedge community typical of the Lake Sedge Herbaceous 

Vegetation (CEGL002256) association (Hop et al. 2013).

Figure 7. Queen of the prairie (Filipendula rubra) found in a slope wetland in Cuyahoga Valley National 

Park.

Depressional wetlands

With an average size of 0.3 ha (0.69 acre), depressional wetlands are found throughout the Cuyahoga 

Valley landscape and account for about 13.5% of the wetland area. These wetlands form in 

topographic depressions and have a closed contour that allows water to accumulate (Smith et al. 

1995). Isolated depressional wetlands often depend on a limited water supply, which limits the size 

of the wetland. As such, these wetlands are highly influenced by land-use changes that affect the 

quality and quantity of their surface water inputs. Depressional wetlands may form naturally in 

glacial depressions, but also and along floodplain terraces or in disturbed areas with poor drainage 

from broken drain tiles, settling after erosion, or from past construction activities. Hydrological 

regimes in these wetlands are often dominated by precipitation, surface water runoff, and 

evapotranspiration (Kirkman et al. 2000), although they can also be recharged by and discharge to 

groundwater. Smaller wetlands generally have less regulatory protection than larger wetlands, which 

leaves many depressional wetlands susceptible to draining or filling (Snodgrass et al. 2000, Kirkman 

et al. 2000). Mapped plant community associations that correspond with depressional wetlands in 

CUVA are numerous, including the higher quality Common Buttonbush / Sedge species Northern 
13

Shrubland (CEGL002190) and lower quality Common Reed Eastern North American Temperate 

Semi-natural Herbaceous Vegetation associations (CEGL004141) (Hop et al. 2013).

Intermittently inundated depressional wetlands have conservation value as breeding habitat for many 

amphibian species. Since many depressional wetlands are isolated and seasonal in nature, amphibians 

are at the top of the food chain, rather than fish and reptiles which dominate more permanent pools 

(Miccachion 2004). Stable amphibian populations also require upland buffers within their terrestrial 

home range, which is generally a165 m radius around the wetland, but can be up to 1 km for some 

species (Semlitsch 1998).

For example, the spotted salamander (Ambystoma maculatum, coefficient of conservatism = 9) 

cannot survive in depressional wetlands that are close to roads and without forest buffers 

(Miccachion 2004). Within CUVA, we found egg masses of Ambystomid salamanders in very small 

(<0.10 acre) pools within large, intact forested tracts (pers. obs, Figure 8).

Figure 8. Egg mass of Ambystomid salamander found in small pool on an abandoned floodplain terrace. 

Lacustrine fringe wetlands

Sixteen wetlands within CUVA are classified within the lacustrine fringe HGM class. These 

wetlands form along the edges of man-made ponds and impoundments where the water table is 

maintained primarily by the lake levels (Smith et al. 1995). At the headwaters of an impoundment, it 

is common for fringe wetlands to intergrade with slope and riverine headwater wetlands. Conditions 

become suitable for vegetation establishment along lake edges where light can penetrate into the 

sediment and the soil is occasionally exposed to oxygen (Smith et al. 1995). The vegetation in fringe 
14

wetlands is typically classified within the Cattail species Midwest Herbaceous Vegetation 

(CEGL002233) type (Hop et al. 2013).

Then water quality in lacustrine fringe wetlands is usually driven by surface water quality, although 

groundwater may contribute relatively unpolluted water. In Virginia Kendall Lake, river water and 

ground water are responsible for water level recharge (Figure 9). Fringe wetlands can have 

bidirectional flow (water flow direction changes as the impoundment is filling or draining) depending 

on the amount of incoming surface water. Multiple water sources create a variety of habitats and 

conditions for a wide range of plant and animal communities. Conversely, in the smaller man-made 

ponds in the park, water levels are controlled more by precipitation and evapotranspiration, usually 

resulting in less diverse habitats, higher water temperatures, tolerant plants with high biomass, and 

mid-summer algal blooms. 

Figure 9. Lacustrine fringe wetlands at Virginia Kendall Lake, Cuyahoga Valley National Park.
15

Methods 

Survey Design Decisions

Designing ecological surveys requires that we balance sampling objectives with respect to parameter 

variability and available effort. Like many complex decisions involving trade-offs, the final decision 

is usually worked out in an iterative rather than a linear manner. Below we outline several of the 

critical decisions that led to the final survey design for the wetland monitoring protocol.

Our first critical decision was to use two existing Ohio EPA wetland assessment protocols, which 

include: 1) the Ohio Rapid Assessment Method or ORAM, and 2) a protocol that intensively 

measures vegetation to calculate the Vegetation Index of Biotic Integrity or VIBI (Mack 2001, Mack 

and Gara 2015). We opted to use these wetland assessment protocols rather than develop indicators 

from scratch (see Fraser 2004). The primary advantage was that the early development work to test 

and calibrate these protocols was already complete. Secondly, as an Ohio-specific protocol, the 

methods used would match those of numerous other datasets in Ohio. Finally, the availability of 

rapid and intensive methods offered the opportunity to survey park wetlands extensively and 

intensively. 

Next, we opted to include multiple HGM types in our sample rather than a single HGM type. To 

reduce the natural variability associated with different wetland types, we opted to rely on the use of 

type-specific VIBI scores to relativize wetland condition amongst HGM types (Mack and Gara 

2015).

We also adopted the LDI as part of this protocol to quantify human disturbance across park 

watersheds (unit of influence) and use it as a basis for stratification to examine with wetland 

condition based on the theory that the condition of ecological communities within a landscape is 

strongly related to levels of human activity (Brown and Vivas 2005, Mack 2006). Landscape 

intensity values range from natural on the lower end of the scale to commercial on the opposite end 

of the scale. Both ends of this spectrum are represented in CUVA watersheds. Indices such as the 

ones we use in this protocol (ORAM and VIBI), have significant relationships with the LDI score, 

according to a large reference wetland dataset from Ohio (Mack 2006), particularly when analyses 

are completed within a 100-250 meter buffer of the wetland (Brown and Vivas 2005, Mack 2006, 

Fennessy 2007).

Our available sampling effort was a major limiting factor. The actual effort required over a five-year 

period is outlined in Table 2. 
16

Table 2. Intensive survey work, including the Vegetation Index of Biotic Integrity (VIBI) and water level 

monitoring is completed at Sentinel Sites (Wetlands of Management Concern and Reference Sites) and 

Intensive Survey Sites. This table shows the number of sampling arrays (SAs) allocated among four

annual rotating panels at these sites as well as the number of extensive survey sites, where the Ohio 

Rapid Assessment Method (ORAM) is completed. 

Wetland Location # SAs Panel 1 Panel 2 Panel 3 Panel 4

Sentinel Site: Wetland of 

Management Concern

Pleasant Valley 5+ 5+

Fawn Pond 5 5

Stumpy Basin 3 3

Kendall Lake 3 3

Beaver Marsh 5 5

Rockside Mitigation + +

Krejci Remediation 2 2

Stanford Rd 4 4

Sentinel Site: Reference

Fitzwater Bridge 1 1+

Snowville Rd 1 1

Columbia Rd 1 1

Boston Mills Rd 1 1

Stumpy Basin 1 1

Kendall Lake 1 1

Langes Run 1 1

Bath Rd 1 1

Survey Site: Intensive

Stanford Run Ws 7 4 3

Railroad Creek Ws 7 3 4

Ira Creek Ws 4 3 1

100-yr Floodplain 7 4 3

CMA Creek Ws 7 3 4

Chippewa Creek Ws 7 3 4

Brandywine Creek Ws 7 3 4

Tinker’s Creek Ws 7 3 4

Unnamed 7 Ws 3 1 2

Unnamed 15 Ws 4 3 1

Total Intensive Sites (VIBI) 95 35 30 30

Total Extensive Sites (ORAM) 250 250

*Wetland is classified as both a Wetland of Management Concern and a Survey Site.

+Wetland is included in the probabilistic sample.

Ws= watershed

We also considered our sampling design relative to several assumptions about spatial variability and 

sample size. To account for geographic variation, we employed a generalized random tessellation 

stratified (GRTS) design with reverse hierarchical ordering (Stevens and Olsen 2004) for the 250 

wetlands to be assessed rapidly (i.e., with the ORAM). We chose this design because summary 
17

statistics can be calculated much like a simple random sample to provide a measure of wetland 

condition across CUVA. Since we capture approximately 17% of park wetlands with ORAM 

methods, wetland condition summaries based on variables such as HGM class and watershed 

disturbance will be possible. For the wetlands that we sample intensively (i.e., with the VIBI), we 

chose to cluster our 60+ samples within watersheds with varying levels of disturbance, based on our 

assumption that differences in watershed disturbance account for much of the variability in wetland 

condition. The clustering up to 6 wetlands per watershed will also allow us to look more 

comprehensively at averages aggregated up to the watershed level of organization as well. To guide 

sample stratification, we calculated the level of anthropogenic disturbance in each watershed using 

the Landscape Development Intensity index (LDI). 

While we realized that probabilistic designs were required to extrapolate our findings across the park, 

we also recognized that some wetlands in the park warranted study, even if not included in our 

sample. Collectively referred to as sentinel sites, these sites are further categorized as “reference” 

wetlands and “wetlands of management concern”. Reference wetlands reflect our perceptions of a 

very high quality, undisturbed example of a particular HGM type. Wetlands of management concern 

are generally large wetland complexes with multiple contiguous HGM and plant community types.

Wetlands of management concern were selected for at least one of the following reasons:

1. The wetland provides habitat for rare plant or animal species.

2. The wetland reflects a history of restoration efforts, including invasive plant control.

3. The wetland is listed in the Cuyahoga Valley National Park degraded wetland restoration plan 

(NPS 2005).

4. The wetland is covered in the Cuyahoga Valley National Park pond management plan (NPS 

1993).

5. The wetland may be the subject of local planning efforts focusing on flood management.

Although not required for either rapid or intensive wetland assessments, we decided to collect basic 

hydrological data and water chemistry data. These ancillary data may more directly or immediately 

reflect changes in environmental factors, including those that may change in response to human 

activity. It is also possible that changes in these factors may help to explain changes observed in 

wetland vegetation. Although perhaps a longer-term prospect, hydrological monitoring may also 

detect diminished water levels resulting from climate change. Such changes are projected to affect 

streams and wetlands in the Great Lakes region (Kusler 2006, USEPA 2014).

Sampling Objectives

The survey design decisions outlined allowed us to more carefully explain the sampling 

objectives of the wetlands monitoring protocol for CUVA:

1. Using a rapid assessment method (i.e., ORAM), understand temporal and, possibly, spatial 

patterns of average wetland condition across CUVA.
18

2. Using intensive methods (i.e., VIBI), identify temporal changes in wetland condition across a 

watershed disturbance gradient in CUVA. 

3. Construct hydrological signatures for water depth and water quality that characterize the 

wetlands and may identify impacts to wetlands and explain changes in vegetation.

Overview of Ohio EPA Methods

Ohio Rapid Assessment Method

The Ohio Rapid Assessment Method (ORAM, Mack 2001) is a rapid assessment that is primarily 

used to categorize wetlands with respect to Ohio’s Wetland Anti-degradation Rule (May 1, 1998).

The rule categorizes wetlands based on their function, sensitivity to disturbance, rarity, and 

irreplaceability, and places restrictions on development ranging from avoidance to minimization to 

mitigation. The categories are as follows:

Category 1: Wetlands with minimal wetland function and/or integrity.

Category 2: Wetlands with moderate wetland function and/or integrity.

Category 3: Wetlands with superior wetland function and/or integrity.

As shorthand, these categories can be thought of as poor, good, and excellent, respectively. An 

implied fourth category is a modified Category 2, which reflects fair condition. In this protocol, we 

use ORAM scores to spatially characterize the condition of wetlands across the park. 

The ORAM is calculated based on six categorical metrics – 1) wetland size; 2) buffers and 

surrounding landuse; 3) hydrology; 4) habitat alteration and development; 5) special wetland 

communities; and 6) vegetation, interspersion and microtopography – that characterize wetland 

condition (SOP 3). The protocol has been validated for use with other independent measures of 

ecological condition such as macroinvertebrate, bird, amphibian, and vegetation data (Mack 2001, 

Andreas et al. 2004, Micacchion 2004, Stapanian et al. 2004, Stein et al. 2009). 

Vegetation Index of Biotic Integrity

The VIBI analyzes vegetation metrics as integrators and indicators of cumulative anthropogenic 

disturbance impacting wetlands. To customize the VIBI for different wetland types, we use three 

VIBI types: the VIBI-Emergent, the VIBI-Forest, and VIBI-Shrub. Each VIBI is designed to be used 

for wetlands dominated by emergent, forest, or shrub vegetation, respectively. Among the three 

VIBIs, there are 19 metrics, although each VIBI type consists of only 10 metrics (Table 3). 

Individual metrics can receive a score of 0, 3, 7, or 10. The VIBI is calculated by summing the 10 

metric scores, yielding a potential score of between 0 and 100 (Mack and Gara 2015, SOP 10). 

Generally speaking, scores above 57 and 70 fall within the Category 2 and 3 anti-degradation ranges

discussed above, respectively. However, reference ranges are specific to HGM type and plant 

community (Table 4).
19

Table 3. The 19 metrics associated with the Vegetation Index of Biotic Integrity (VIBI) for emergent, 

shrub, and forested wetlands. Each specific VIBI consists of ten individual metrics.

VIBI-Emergent VIBI-Forest VIBI-Shrub

Percent sensitive Percent sensitive Percent sensitive

Percent tolerant Percent tolerant Percent tolerant

FQAI score FQAI score FQAI score

Carex Shade Carex

Hydrophyte, native Seedless Vascular Plants Hydrophyte, native

Dicot, native Percent hydrophyte Dicot, native

Shrub, native, wetland Pole timber density Shrub, native, wetland

Annual/Perennial Ratio Subcanopy IV Subcanopy IV

Percent invasive graminoid Canopy IV Seedless Vascular Plants

Biomass Percent bryophyte Percent bryophyte

Table 4. Vegetation Index of Biotic Integrity scoring ranges for specific plant communities and landscape 

position (i.e., hydrogeomorphic [HGM] class). Limited quality wetland habitat (LQWLH) corresponds with 

the anti-degradation Category 1, restorable wetland habitat (RWLH) corresponds with the antidegradation category Modified 2, wetland habitat (WLH) corresponds with the anti-degradation Category 

2, and superior wetland habitat (SWLH) or Category 3.

HGM Class Plant Community

Category 1 

(LQWLH)

Modified

Category 2 

(RWLH)

Category 2 

(WLH)

Category 3 

(SWLH)

Depressional Swamp Forest, Marsh, Shrub swamp 0-30 30-60 61-75 76-100

Meadow 0-29 30-59 60-75 76-100

Slope Wet meadow, fen, forest seep 0-29 30-59 60-75 76-100

Riverine Wet meadow 0-29 30-59 60-75 76-100

Other communities (headwater) 0-27 28-56 57-69 70-100

Other communities (mainstem) 0-29 30-56 57-73 74-100

Impoundment Marsh, Shrub swamp 0-26 27-52 53-66 67-100

Wet meadow 0-29 30-59 60-75 76-100

Sampling Procedures

The sections below describe in greater detail the procedures that were required to implement the 

survey design.

Reference Frame Development

We developed a reference frame of 1,476 wetlands within the Cuyahoga Valley National Park 

legislative boundary. Multiple data sources that we refer to as “constituent files” (Appendix A)

contributed to the development of the reference frame. Wetlands were often mapped rapidly and not 

necessarily in accordance with the requirements of a jurisdictional delineation. However, observers 
20

used the classic delineation criteria - hydric soils, hydrophytic vegetation, and wetland hydrology –

to map all wetland boundaries.

Sample Selection for Rapidly Assessed Wetlands

To monitor wetlands using the ORAM, we selected 250 wetlands using a generalized random 

tessellation stratified (GRTS) design with reverse hierarchical ordering for spatial balance (Stevens 

and Olsen 2004). We excluded, however, wetlands that we cannot access safely, that we do not have 

permission to access, or that no longer exist. The number of wetlands selected for each HGM class 

was roughly proportional to the estimated abundance of that class throughout the park. A similar 

ORAM study at the scale of the entire Cuyahoga River watershed indicated that means, variances, 

and confidence intervals of ORAM scores improved minimally beyond 100 samples (Fennessy et al. 

2007). We completed 250 samples, however, to increase our chances for meaningful post-hoc 

analyses.

Sample Selection for Intensively Assessed Wetlands

Our use of a disturbance gradient to stratify intensively sampled wetlands required that we first map

watersheds within CUVA and then calculate the degree of human disturbance in each watershed

before selecting our sample.

Watershed Mapping

We developed a set of watersheds that matched the scale of management interest on the park (Skerl 

2004). We attempted to use USGS basins at the HUC-14 scale. HUC-14s range in size from 4,000 

acres to 50,000 acres. Basins at this scale are generally useful for “project activities, resource 

inventories, and reporting conservation activities” (NCGICC 2014). Nine HUC-14 basins covered 

our study area. We retained five of these that delineated the five major tributaries to the Cuyahoga 

River (i.e., Brandywine Creek, Chippewa Creek, Furnace Run, Tinkers Creek, and Yellow Creek).

Using the spatial analyst basin tool in ArcGIS 9.1, we further divided four basins in to 38 smaller 

watersheds that drained directly in to the Cuyahoga River. We mapped these watersheds using 30 m 

x 30 m digital elevation models with each watershed at least 51.75 ha (127.9 acres) in size. Each of 

these watersheds contained a single, relatively small tributary, so we mapped these watersheds from 

the main stem back to the headwaters. Grouping these small watersheds within HUC-14s would have 

obscured local factors affecting wetlands within the park. 

Landscape Development Intensity Index for Watersheds

To characterize the impact of human activities in each watershed, we calculated the Landscape 

Development Intensity Index (LDI) for watersheds using ArcGIS (SOP 1, Figure 10). The LDI 

characterizes the human-disturbance gradient on a scale from relatively natural (score 1) to highly 

impacted (score 7) by human activities. The index is calculated using emergy values that quantify the 

amount of non-renewable energy needed to maintain each landcover type on a per area (hectare) and 

per unit of time (year) basis, expressed as solar emergy joules (sej) per hectare-year. Non-renewable 

energy sources include electricity, fuels, fertilizers, pesticides, and water. 
21

Figure 10. Map of watersheds in Cuyahoga Valley National Park (CUVA) mapped with low, medium, and 

high landscape development intensity (LDI) scores
22

To calculate this index, land cover classes from the national landcover dataset (2001) were matched 

with emergy coefficients. For this study, we used the emergy coefficients developed for Florida 

(Brown and Vivas 2005), given that LDIs developed for different states generally agree with each 

other (Campbell 2010, Table 5). To calculate and compare LDI in each watershed (n=43), we 

multiplied the percentage of area within each landcover type by the appropriate emergy coefficient, 

and summed the resulting products. The equation for this is as follows: LDI Total = ∑ % LUi ∗ LDIi, 

where %LUi = percent of the total area of influence in land use and LDIi = landscape development 

intensity coefficient for land use i.

After completing these calculations, we then used natural breakpoints to rank-order watersheds in 

high (> 4.7), medium (2.7 – 4.7) and low (< 2.7) LDI categories (Figures 10 and 11). This approach 

prevented subsequent selection of watersheds on the border of a category and only a few hundredths 

of an LDI score away from another category as might be the case with quartile or standard deviation 

methods. An LDI Index score of 2.0 was proposed as a conservative breakpoint to classify a 

minimally affected reference condition by Brown and Reiss (2006).

Sample Selection

Selecting wetlands for intensive assessment using the VIBI required a two-stage sample (Table 6). 

Prior to selection, we excluded 11 watersheds with fewer than 5 wetlands from the sample. From the 

remaining 32 watersheds, we selected a stratified random sample of three watersheds from the low, 

medium, and high LDI classes (Figure 11). The watersheds selected ranged in drainage area from 

0.96 km2/0.37 mi2(Railroad Creek) to 248.95 km2/96.12 mi2(Tinker’s Creek) and contained from 5 

wetlands (Unnamed 15 and 7) to 84 wetlands (Stanford Run) within park boundaries (Table 4). We 

expected a low capture of wetlands within the riverine mainstem HGM type because riverine 

mainstem wetlands are spatially limited and less numerous than other HGM types. To compensate 

for this, we delineated the 100 year floodplain (FEMA 2014) and included the wetlands in this area 

as a tenth “floodplain” stratum. In the second stage, we selected 7 wetlands from 9 of the selected 

watersheds using a generalized random tesselation stratified (GRTS) design. In the two watersheds 

that contained only 5 wetlands within park boundaries (Unnamed 7 and Unnamed 15), we selected 

all wetlands. This led to the inclusion of 66 wetlands in our probabilistic design. 

Next we evaluated each wetland in the field to verify that the wetland was suitable for sampling. We 

chose not to eliminate wetlands in the sample population based on size as over 40% of CUVA 

wetlands are smaller than 0.1 acre (0.04 ha), and 60 % are smaller than 0.25 acre (0.1 ha). Wetlands 

were not suitable if we could not sample the wetland safely, did not have permission to access from 

landowners, or could not fit at least a single 10 x 10 m module in the wetland. We replaced each 

excluded wetland with the next available wetland in the oversample list. In three watersheds, 

however, we exhausted all available samples, resulting in six fewer wetlands in those watersheds 

than originally expected. We segregated the final sample of 60 (Table 7) wetlands into 2 panels 

(Table 2). 
23

Table 5. National Landcover Database (NLCD) land-use classes identified within Cuyahoga Valley National Park that correspond with the Florida 

land-use classes, and the Landscape Development Intensity (LDI) coefficients (i.e. emergy coefficients) that were used to calculate the LDI scores 

in this study (Mack 2006)

NLCD land-use classes

LDI

coefficients

NLCD Land-use 

Code Florida land-use classes Definition

1. Deciduous Forest, 

2. Evergreen Forest, 

3. Mixed Forest, 

4. Woody Wetlands, 

5. Emergent Herbaceous Wetlands

6. Palustrine Aquatic Bed

1.00 41, 42, 43, 90, 95, 99 Natural system Open water, upland, or wetland with very low 

manipulations (i.e. state parks, refuges, 

preserves and other protected lands). Does not 

include tree plantations and reservoirs.

Open Water 1.00 11 Natural open water

Shrub/Scrub 2.02 52 Woodland pasture (with livestock) Native rangeland and woodland pasture with 

presence of livestock.

Grassland/Herbaceous 3.41 71 Low intensity pasture (with livestock) Areas where the natural vegetation has been 

altered by drainage, irrigation, etc., for the 

grazing of domestic animals with a density of 

less than 1.2 animals/ha.

Pasture/Hay 3.74 81 High intensity pasture (with livestock) Areas where the natural vegetation has been 

altered by drainage, irrigation, etc., for the 

grazing of domestic animals with a density of 

more than 1.2 animals/ha.

Cultivated Crops 4.54 82 Row crops Areas devoted to the production of all types of 

vegetables usually grown in rows, whether 

producing or not.

Developed, Open Space 6.92 21 Recreational/open space (high intensity) Applies to stadiums not associated with 

institutions such as schools and universities, 

golf courses, and racetracks (horse, dog, car).

Developed, Low Intensity 7.47 22 Single family residential (medium density) Areas that are predominantly residential units 

with a density between 10 and 20 units/ha.

Developed, Medium Intensity 7.55 23 Single family residential (high density) Areas that are predominantly residential units 

with a density of more than 20 units/ha.

Barren Land (Rock/Sand/Clay) 8.32 31 Industrial Land uses include manufacturing, assembly or 

processing of materials/products and 

associated buildings and grounds. Also 

includes extractive areas and mining 

operations, water supply plants, and solid

wastes disposal facilities.

Developed, High Intensity 9.42 24 Central business district (average 2 stories) Central business districts with an average of 2 

stories.
24

Figure 11. Watersheds in Cuyahoga Valley National Park ranked and ordered by landscape development 

intensity index (LDI). Index scores range from 1 to 7, representing low to high anthropogenic energy 

inputs. Dashed lines indicate the natural breakpoints that separate the low, medium, and high disturbance 

classes.

Table 6. Summary of the watershed and wetland inclusion probabilities for those selected in the twostage sample using a generalized random tessellation stratified design in Cuyahoga Valley National Park 

for intensively assessed wetlands. The watershed drainage area, landscape development index (LDI) 

category and score are also shown in the table.

Watershed

LDI Category 

(Score)

Inclusion Probability 

(watershed)

Inclusion Probability 

(wetland)

Drainage Area

(mi2)

Railroad Low (1.3) 0.15 0.70 0.37

CMA Low (2.0) 0.15 0.28 3.25

Stanford Low (2.2) 0.15 0.14 2.17

Ira Creek Medium (2.9) 0.25 0.58 0.66

Unnamed 15 Medium (3.3) 0.25 1.00 1.83

Chippewa Medium (4.4) 0.25 0.44 18.02

Brandywine High (5.2) 0.60 0.21 27.20

Tinkers High (5.2) 0.60 0.26 96.12

Unnamed 7 High (6.5) 0.60 1.00 4.05

Floodplain NA 1.00 0.10 NA
25

Table 7. List of 60 Survey Sites (Plot ID) with number of Modules, dominant vegetation type, the azimuth 

of the centerline, array of modules, and coordinates (UTM Zone 19 N, NAD 1983).

Plot Number PlotID Module # Dominant Type Azimuth Array X_Coord Y_Coord

1 28 4 emergent 0° 2 x 2 451777.581025 4556542.604980

2 29 4 emergent 0° 2 x 2 451744.953057 4556417.829690

3 30 4 forested 0° 2 x 2 451567.317857 4556481.756470

4 32 4 emergent 0° 2 x 2 451701.756513 4556610.150200

5 39 1 emergent 154° 1 x 1 450669.051872 4558116.003000

6 43 4 emergent 0° 2 x 2 451012.282593 4558823.953720

7 56 1 emergent 0° 1 x 1 450705.570784 4558052.159670

8 192 8 forested 0° 2 x 4 454624.701284 4568648.855750

9 305 4 scrub-shrub 0° 2 x 2 451364.481121 4563056.698940

10 306 10 forested 0° 2 x 5 451638.873953 4562915.342010

11 307 4 scrub-shrub 0° 2 x 2 451649.450593 4563096.249790

12 309 4 emergent 0° 1 x 4 452040.173794 4562811.307580

13 310 10 forested 0° 2 x 5 451875.898721 4562600.682300

14 311 4 forested 24° 1 x 4 451654.102625 4562718.043840

15 317 1 emergent 13° 1 x 1 452850.252642 4563056.045760

16 343 4 emergent 35° 1 x 4 452409.010402 4562153.677500

17 351 4 emergent 15° 1 x 4 453854.080355 4565508.463170

18 363 4 emergent 0° 2 x 2 451921.862369 4560185.249210

19 413 4 emergent 0° 2 x 2 451164.030689 4571937.473220

20 416 4 forested 0° 2 x 2 451122.701409 4572508.956740

21 422 3 forested 47° 1 x 3 450723.714912 4572133.494470

22 623 10 forested 90° 2 x 5 456680.706150 4566818.020030

23 627 10 forested 0° 2 x 5 456457.029478 4566936.435270

24 669 4 emergent 282° 1 x 4 456382.839782 4567221.824830

25 676 6 forested 90° 2 x 3 455713.429093 4566435.258940

26 692 2 forested 19° 1 x 2 455246.545893 4568347.035330

27 697 1 emergent 100° 1 x 1 455021.954148 4569147.234500

28 743 4 forested 72° 1 x 4 450104.692704 4570065.576640

29 748 1 forested 142° 1 x 1 450527.364320 4570521.015490

30 867 5 forested 342° 1 x 5 453773.423843 4569071.938760

31 885 4 emergent 0° 2 x 2 454596.012004 4570635.722050

32 887 10 forested 90° 2 x 5 454423.620708 4570653.665730
26

Table 7 (continued). List of 60 Survey Sites (Plot ID) with number of Modules, dominant vegetation type, 

the azimuth of the centerline, array of modules, and coordinates (UTM Zone 19 N, NAD 1983).

Plot Number PlotID Module # Dominant Type Azimuth Array X_Coord Y_Coord

33 889 10 forested 280° 2 x 5 454343.165028 4571188.327370

34 927 10 forested 30° 2 x 5 450161.365984 4573601.061580

35 939 10 forested 90° 2 x 5 449017.253343 4571730.991810

36 941 4 emergent 290° 1 x 4 450207.123808 4572417.442500

37 947 4 emergent 0° 2 x 2 448799.970398 4574860.206660

38 950 4 forested 48° 2 x 2 449111.307487 4574445.562570

39 951 2 emergent 335° 1 x 2 449294.112991 4574414.774220

40 953 1 emergent 0° 1 x 1 449873.339743 4573945.344460

41 957 4 emergent 0° 2 x 2 447986.716766 4573651.887050

42 970PV 10 forested 320° 2 x 5 449911.815264 4578266.878800

43 1007 4 emergent 331° 2 x 2 453053.023714 4580239.639370

44 1017 3 emergent 43° 1 x 3 454069.788643 4580087.319760

45 1034 1 forested 34° 1 x 1 452024.840162 4580873.768780

46 1036 4 forested 0° 2 x 2 454279.313892 4582030.014420

47 1058 4 forested 0° 2 x 2 449573.221087 4579978.605010

48 1068 4 emergent 0° 2 x 2 448833.754591 4580355.474000

49 1069 4 emergent 0° 2 x 2 448920.449247 4580681.236810

50 1070 1 emergent 317° 1 x 1 448956.769247 4580865.931210

51 1079RS2 4 emergent 0° 2 x 2 447083.081309 4582432.915790

52 1188 4 emergent 0° 2 x 2 455060.180708 4566663.475140

53 1196 4 emergent 0° 2 x 2 455166.456292 4566309.502780

54 1205 1 forested 162° 1 x 1 450575.992032 4558387.495350

55 1221 4 emergent 0° 2 x 2 453719.447523 4569918.084040

56 1351 4 forested 0° 2 x 2 452025.379682 4572319.294020

57 1364 4 emergent 0° 2 x 2 450720.061664 4574064.138700

58 1468 4 emergent 0° 2 x 2 449315.453535 4573663.379270

59 1485 4 emergent 352° 2 x 2 452570.421730 4581354.468300

60 1104(1620) 3 emergent 38° 1 x 3 449206.282591 4579936.302030

We marked plot corners using 1-m lengths of PVC and rebar that we drove into the ground to the 

point of refusal. The length of PVC above the ground was typically 30 to 60 cm. A china marker was 

used to write the wetland ID on each corner stake and an NPS arrowhead decal was affixed to each 

stake. All set up details were recorded on a site establishment datasheet (Appendix B).
27

We recognize that we must be cautious in basing our sample on strata that are subject to change over 

time (National Park Service 2012). In comparing LDI scores of park watersheds between 2001 and 

2006, LDI scores changed minimally and did not result in any changes in strata assignment. We will 

continue to reassess land-use changes in the watersheds as the NLCD is updated. We expect few 

changes as lands outside of park boundaries are largely built out with residential, commercial, and 

industrial uses, while conservation easements protect most remaining green spaces. We accept the 

risk that if LDI scores change substantially in the future, we will lose the ability to use the strata to 

reduced variability in analyses. In the short-term, however, understanding changes in wetlands in 

terms of watershed condition will be more important for guiding park management. 

Sample Selection for Sentinel Sites

We purposively selected 35 “sentinel” sites, including 8 reference sites and 27 sites in 8 wetlands of 

management concern (Table 2). Comparing metrics from our probabilistic sample against metrics of 

reference wetlands may provide a means to assess wetland quality against the “best attainable 

condition” (Stoddard et al. 2006). 

Field Procedures

Field Methods for Rapidly Assessed Wetlands

The Ohio Rapid Assessment Method (Mack 2001) requires an observer to walk the perimeter of a 

wetland, categorizing and scoring six metrics – wetland size; buffers and surrounding landuse; 

hydrology; habitat alteration and development; special wetland communities; and vegetation, 

interspersion and microtopography – that characterize wetland condition (SOP 3). The approximate 

wetland perimeter should also be surveyed with a GPS unit for a more accurate depiction of the 

wetland size and location (SOP 4). We conduct these surveys between April 15 and June 1, prior to 

intensive vegetation surveys. 

Sampling Terminology

For the intensively assessed wetlands and sentinel sites, we explain the terminology needed to 

understand the field procedures. The primary sampling unit is the wetland. For all intensively 

assessed sites and reference wetlands, a single sampling array is sampled to characterize the 

vegetation. Each sampling array consists of between 1 and 10 modules. All modules are 10-m x 10-m 

in size (i.e., 100 m2squares). The shape of the array is described in terms of the length and width of 

the modules. For example, a 2x5 array is 2 modules wide and 5 modules long, resulting in a sampling 

array of 10 modules (Figure 12).

Field Methods for Intensively Assessed Wetlands

Sampling Array Placement

To place sampling arrays within intensively assessed wetlands, we first delineated each wetland 

(n=60) in the sample frame using vegetation, water, and soil indicators according to the criteria 

required for a jurisdictional delineation (USACE 1987). We then mapped the perimeter using a GPS 

unit. We completed baseline delineations between 3/12/2012 and 05/15/2013. Delineation work was 
28

completed year-round as long as the ground was free of snow and not frozen. In all cases, the 

delineation resulted in a change to the wetland as originally delineated in the reference frame.

A flowchart summarizes the decisions we made to guide the random placement of a sampling array 

within the delineated wetland (SOP 5). The preferred sampling array was a 2 x 2 array of 100 m2

square modules in aquatic bed, emergent, and shrub-scrub wetlands and a 5 x 2 array of 100 m2

square modules in forested wetlands (Figure 12). We visited up to 10 random points to assess the 

“fit” of the preferred array around that point. The point was evaluated as a possible location for the 

sampling array if: 1) work could be conducted safely, and 2) the sampling array reflected the 

dominant vegetation type. If the waypoint met these conditions, we attempted to fit the preferred 

sampling array using the waypoint as the centroid and with the long axis oriented in the following 

directions: 1) north to south, 2) east to west, or 3) a customized azimuth when the other options did 

not work (i.e., any azimuth that worked). We did allow a centroid shift up to half the distance of the 

long axis of the array in any direction to find the best “fit” possible for the array. If the array included 

a high cover of uplands, we moved to the next available waypoint. If all 10 waypoints were 

exhausted without successful placement of the preferred array, we repeated the process using arrays

of different sizes as shown in Figure 13, in order of decreasing preference, until at least a single 100 

m

2 module was placed. The four sampling array corners were marked using 1” PVC stakes and the 

spatial location was recorded with a GPS unit. Alternative arrays and customized azimuths were 

common. We documented all sampling array placement decisions on the site establishment datasheet 

(SOP 5).

Figure 12. The preferred sampling array for (A) emergent, aquatic bed, and scrub-shrub wetlands, (B) 

forested wetlands, and (C) an example alternative sampling array for situations where the preferred array 

was not possible because of wetland shape, size, or vegetative composition.
29

Figure 13. Flowchart for sampling array (SA) placement in intensively assessed wetlands. Up to ten 

points were visited in random order. We first followed the flow chart using the preferred SA of 10m x 10m 

modules for emergent/shrub or forested wetlands. If all points were exhausted and the preferred array did 

not fit within the wetland, we followed the same procedure using arrays of different sizes in a preferred 

order until we found a point that met the desired SA conditions.
30

Vegetation Sampling

We use at least a 2-person team to sample vegetation. In accordance with Mack et al. (Mack and 

Gara 2015), we conduct this work between June 15th and October15th when key plant identification 

characteristics are most visible. Identifying sedge (Carex spp.) and willow (Salix spp.) species may 

require additional visits as these species are difficult to identify outside of the flowering and/or 

fruiting stages. The cover of all vascular plant species, the density and identity of woody stems less 

than 3 ft. tall, the diameter and identity of all woody stems greater than 3 feet tall, and biomass (for 

emergent wetlands only – SOP 11) are measured within the assessment area and recorded separately 

within each 100 m2 module. Because VIBI calculations require highly accurate identification of 

plants to the species-level, we voucher unknown specimens for later identification in the lab or in 

consultation with an experienced plant taxonomist. A working reference collection of wetland plant 

specimens also assists with plant identification (SOP 12). The reference specimens are in a herbarium 

cabinet located at the HTLN satellite field office at Cuyahoga Valley National Park. 

Field Methods for Sentinel Wetlands

Sampling Array Placement in Wetlands of Management Concern 

Based on a review of aerial photography and field work, we delineated the wetlands and mapped the 

plant communities and HGM classes for eight wetland complexes (see Table 1 for wetland listing, 

Table 8 for plot locations). We then purposively sampled one assessment area in each HGMdominant plant community type combination in these wetlands, resulting in multiple arrays per 

wetland. We will complete the eighth and final wetland complex in 2015. We attempted to control 

the bias to some extent by first attempting to place the sampling array near a randomly selected 

location.

In the field, we adjusted sampling array locations slightly as necessary to avoid transitional areas and 

areas that were clearly not representative (e.g. minor, localized areas of disturbance). Sampling 

arrays were established and baseline vegetation surveys were completed between 2008 and 2014. 

Sampling Array Placement in Reference Sites

A single sampling array was purposively selected and established in each reference wetland to 

represent the highest quality vegetation community within the HGM class of interest (SOP 7). 

Vegetation Sampling in Sentinel Sites

Vegetation sampling in sentinel sites followed that used in intensively sites (see Vegetation Sampling

in Intensively Assessed Wetlands above).
31

Table 8. List of 37 intensively-assessed wetland sites, identified by PlotID, and showing wetland name, 

sample set (reference or wetland of management concern [WOMC]), dominant vegetation type, azimuth 

of the center line, array shape of modules, and location coordinates (UTM Zone 19 N, NAD 1983).

Plot 

No PlotID WetlandName SampleSet

Dominant 

Veg. 

Type

Azimuth

Center 

Line Array X_Coord Y_Coord

1 1427 Bath Reference forested 90° 2 x 5 451956.239329 4557257.599160

2 683 BostonMills Reference shrubshrub0° 2 x 5 455170.534500 4567589.166150

3 554 Columbia Reference emergent 0° 1 x 4 452285.982562 4568972.745800

4 970 Fitzwater Reference forested 0° 2 x 5 449863.854815 4578175.339980

5 124 Langes Reference shrubscrub0° 2 x 2 453847.660515 4562127.533240

6 398 Snowville Reference emergent 290° 1 x 4 451543.095649 4570712.221000

7 526 Stumpy Reference emergent 0° 2 x 5 454408.745700 4567140.493890

8 241K Virginia 

Kendall

Reference/WOMC emergent 90° 2 x 2 456153.332325 4562776.202940

9 970PV Pleasant Valley Survey / WOMC forested 144° 2 x 5 449911.815264 4578266.878800

10 1079RS2 Rockside Survey / WOMC emergent 0° 2 x 2 447083.081309 4582432.915790

11 365BM3 Beaver Marsh WOMC emergent 0° 2 x 2 451391.660641 4559358.737340

12 365BM2 Beaver Marsh WOMC emergent 0° 2 x 2 451198.660833 4559529.238070

13 365BM4 Beaver Marsh WOMC forested 180° 2 x 5 451522.806753 4559879.213630

14 365BM5 Beaver Marsh WOMC emergent 330° 2 x 2 451519.373793 4560095.169720

15 365BM6 Beaver Marsh WOMC emergent 302° 2 x 4 451670.046817 4560334.262460

16 977FP1 Fawn Pond WOMC forested 90° 2 x 5 450005.749088 4576513.516750

17 977FP2 Fawn Pond WOMC emergent 0° 2 x 2 449807.231839 4576607.576390

18 977FP3 Fawn Pond WOMC emergent 0° 2 x 2 450018.577504 4576854.829130

19 977FP4 Fawn Pond WOMC emergent 0° 2 x 2 449929.178976 4577019.816780

20 977FP5 Fawn Pond WOMC forested 0° 2 x 5 449938.991456 4577374.911440

21 968PV968 Pleasant Valley WOMC emergent 42° 2 x 5 449100.999519 4578760.580940

22 969 Pleasant Valley WOMC emergent 120° 2 x 5 449697.402975 4578825.406030

23 1043 Pleasant Valley WOMC emergent 355° 2 x 5 448684.948318 4578711.000270

24 1047 Pleasant Valley WOMC emergent 323° 2 x 5 448901.416287 4579130.870090

25 1049 Pleasant Valley WOMC forested 95° 2 x 5 449676.377567 4579000.803530

26 526SB3 Stumpy Basin WOMC forested 0° 2 x 5 454333.341924 4566853.348930

27 526SB2 Stumpy Basin WOMC forested 0° 2 x 5 454293.667428 4567145.250500

28 526SB1 Stumpy Basin WOMC emergent 0° 2 x 2 454278.286180 4567261.859910

29 241VK4 Virginia 

Kendall

WOMC emergent 102° 1 x 3 455927.332453 4562814.243520
32

Table 8 (continued). List of 37 intensively-assessed wetland sites, identified by PlotID, and showing 

wetland name, sample set (reference or wetland of management concern [WOMC]), dominant vegetation 

type, azimuth of the center line, array shape of modules, and location coordinates (UTM Zone 19 N, NAD 

1983).

Plot 

No PlotID WetlandName SampleSet

Dominant 

Veg. 

Type

Azimuth

Center 

Line Array X_Coord Y_Coord

30 242VK1 Virginia 

Kendall

WOMC emergent 86° 2 x 2 456096.224485 4563161.566650

31 242VK2 Virginia 

Kendall

WOMC forested 110° 2 x 5 456306.969061 4563191.892160

32 540SF1 Stanford WOMC emergent 0° 2 x 2 453130.287675 4569393.440660

33 540SF2 Stanford WOMC emergent 0° 2 x 2 453113.750898 4569463.449830

34 540SF3 Stanford WOMC emergent 0° 2 x 2 453209.960396 4569427.511180

35 540SF4 Stanford WOMC emergent 0° 2 x 2 453244.011986 4569513.765510

36 1622KR1 Krejci WOMC emergent 0° 2 x 2 454954.070943 4568242.931670

37 1627KR2 Krejci WOMC emergent 0° 2 x 2 454728.741149 4568433.300940

Field Methods for Hydrological Monitoring

Well Deployment and Soil Analysis

Within all sampling arrays, we established a shallow groundwater monitoring well (n=89) (SOP 8). 

The well was deployed at or near the sampling array centroid. The well locations were recorded 

using GPS and sketched on Site Deployment Sheets (SOP 8). During well installation, we used the 

soil profile that was removed to characterize soil stratigraphy (SOP 9). We recorded the soil 

horizons, matrix and mottle color, and soil texture on a detailed well deployment log that also 

included details on the well depth and backfill materials. Matrix color and mottle characteristics are 

secondary indicators of the hydrologic regime. Mottles often occur in soils that alternate between 

anaerobic (reduced) and aerobic (oxidized) condition. Strongly gleyed soils indicate exposure to 

prolonged anaerobic conditions, created by the presence of water.

Hydrological Monitoring

To characterize the hydrological regime of the monitored wetlands, we measure shallow groundwater 

in each of the 88 groundwater wells (Table 9). Manual measurements are made at least biweekly

from early spring (mid-March) through late fall (late October) using a hydrolite (SOP 8). A hydrolite 

consists of a 'sensor', a measuring tape, and an LED light or buzzer that flashes or buzzes when the 

sensor makes contact with the water surface. We also measure ground water depths in 8 reference 

wetlands using digital loggers (EcotonesTM) (SOP 8). EcotonesTM digitally record water depth 

automatically at 12 hour intervals. Surface water depths are measured at 4 staff gauges, in areas 

where surface water frequently precludes visitation.

Water Chemistry Monitoring

In wells within wetlands with a water level less than 1 m below the surface during the early growing 

season, we measure water temperature, pH, dissolved oxygen, and conductivity using a Eureka 
33

Manta multiprobe. Standing water is bailed out and wells are allowed to refill before extraction and 

measurement. These measurements are made monthly from March through October in ground water 

or surface water wells (SOP 13). We expect 90-100% of the wetlands in the intensive survey to be 

measurable during the early growing season (March, April, May) with roughly 60% of the wetlands 

measurable through October.

Field Safety

Safety is paramount during all field operations of NPS staff, partners, and volunteers. Standing dead 

trees, downed woody debris, thick vegetation, and mucky soils can make walking through wetlands a 

daunting task. Trapped heat and high humidity can increase the odds of heat stroke and dehydration

when working in wetland environments. Ticks, mosquitos, and chiggers can also be prevalent near 

wetland areas. Field personnel need to be aware of the common hazards, follow the network safety 

plan (Heartland Network 2012), wear or use the appropriate personal protective equipment, and be 

able to respond and communicate with park staff or first responders in case of an emergency (SOP 

16).

Table 9. Location and number of monitoring wells, staff gauges, and digital EcotoneTM water level 

measuring devices in wetlands that we monitor in Cuyahoga Valley National Park. 

Complex Well # Staff Gauge EcotoneTM Initial monitoring date

Pleasant Valley 6 1 - September 12, 2008

Fawn Pond 5 1 - July 3, 2009

Stumpy Basin 3 - - March 15, 2011

Virginia Kendall 4 1 - July 6, 2009

Beaver Marsh 5 1 - June 25, 2009

Rockside 1 - - April 2013

Krejci 0 2 - 2015

Stanford 4 - - 2015

Survey Sites 60 - - 2014

Reference Sites - - 8 November 2010

Total 88 6 8

Data Processing, Analysis, and Reporting

Data processing typically involves the following steps: data entry, data verification, data validation, 

and backups/storage (see SOP17). The wetland biologist in coordination with the data manager is 

responsible for all phases of data processing. 

Databases

Data is managed in four databases that are designed to store and display specific types of monitoring 

data: 1) a geodatabase with spatially referenced wetland and watershed locations (wetlnd.gdb), 2) an 

Access database that stores LDI scores; data associated with the Ohio Rapid Assessment Method and 

Vegetation Index of Biotic Integrity; and hydrological data (wetlnd.mdb), 3) an Aquarius database 

that stores automated digital water level data collected at 8 reference sites, and 4) NPSTORET.mdb, 

a water quality database that contributes data to the national repository of water quality monitoring 

data in a common format accessible on-line, as required in The Natural Resources Management 

Guideline (NPS 1991). Spatial data will be managed in accordance with NPS standards, which 

includes the use of FGDC compliant metadata.

Data Entry

Traditional field data forms are used in the field for ORAM and VIBI data collected, and data is 

entered in the office. However, we hope to eventually enter data directly into the wetland.mdb using 

the Trimble YUMA 2 field computer (SOP 14). The wetlnd.mdb user interface is organized in an 

intuitive way with digital dataforms that closely mimic the original field forms. Data entry using 

NPStoret and Aquarius require close adherence to SOP 14.

Data Verification and Validation

Data verification is required immediately following data entry and involves checking computerized 

records against the original source, usually paper field records. The paper forms are archived and the 

electronic version is used for subsequent analyses. 

Data validation involves checking the accuracy of data against independent controls or specifications. 

There are three types of controls used in wetland.mdb. They are: 1) checks for referential integrity, 2) 

limited pick lists for nominal data, and 3) controls to only allow reasonable values for continuous 

data. For example, data entry forms in the database contain check boxes and lists that link to 

referenced lookup tables so that only valid names or measures may be entered. Data verification is 

also an integral step for preparing water level and water quality data for entry into the STORET.mdb 

and Aquarius database. 

Data Protection

Frequent backups are critical for preventing data loss. Full backup copies of the monitoring project 

will be stored at an off-site location, including on the Heartland Network’s server and Cuyahoga 

Valley National Park’s server. The Heartland Network uses a RAID design to protect data (Rowell et 

al. 2005). 
36

Data Analysis and Reporting

Data analysis

A primary objective of this monitoring protocol is to detect change in wetland conditions over time. 

In our analyses, we focus on detection of change rather than trend per se (where change can be 

defined as any alteration of an attribute over any time period, whereas a trend can be defined as a 

gradual increase or decrease in an attribute over time). It can be difficult to reliably detect trends 

because of the uncertainties in distinguishing a true directional trend from multiyear cyclical 

variability or erratic fluctuations. Additionally, wetlands may reveal an abrupt change due to a single 

event such as a natural disturbance or management action, rather than a gradual increase or decrease 

over time. Thus in environmental monitoring programs it is usually preferable to search for change 

rather than attempt to detect trends per se (Rowland and Vojta 2013).

In determining the appropriate statistical approaches for this monitoring protocol, it is important to 

take into account the primary audience of the various reports that will result. This audience will 

consist of park resource managers, park superintendents, and other park staff. Park resource 

managers and staff may not have an in-depth background in statistical methods, and park 

superintendents may have limited time to devote to such reports. Additionally, protocols such as this 

may provide much data on many different types of variables. To the extent possible, the core data 

analyses and presentation methods should provide a standard format for evaluation of numerous 

variables, be relatively straightforward to interpret, be quickly updated whenever additional data 

become available, and be used for many different types of indicators, whether univariate or 

multivariate. Additionally, the type and magnitude of variability or uncertainty associated with the 

results should be easily discernible, and a threshold for potential management action ideally will be 

indicated. 

There are three main statistical approaches that could be employed with data from long-term 

monitoring projects: (1) hypotheses testing, (2) parameter estimation, and (3) application of Bayesian 

methods. When analyzing ecological data, statisticians predominantly employ frequentist methods, 

so many resource managers are not familiar with the interpretation of Bayesian approaches. Bayesian 

methods are not widely used because they are often difficult to apply, and many researchers are not 

comfortable specifying subjective degrees of belief in their hypotheses (Utts 1988, Hoenig and 

Heisey 2001). Thus, we do not advocate a Bayesian approach as our main method of data analysis. 

Most hypothesis testing approaches involve a null hypothesis of no difference or no change. The 

problem with such approaches is that the hypothesis under test is often trivial (Cherry 1998, Johnson, 

1999, Anderson et al. 2000, 2001). No populations or communities will be exactly the same at 

different times. As such, we are not really interested in whether these are changing per se, but rather 

in the magnitude of change, and whether it represents something biologically important. Null 

hypothesis significance testing relies heavily on P-values, and results primarily in yes – no decisions 

(reject or fail to reject the null hypothesis). P-values are strongly influenced by sample size, however, 

and one may, with a large enough sample size, obtain a statistically ‘significant’ result that is not 

biologically important. Alternatively, with a small sample size, one may determine that a biologically 
37

important result is not statistically significant (Yoccoz 1991). We aim for our analyses to be more 

concerned about whether the data support our scientific hypotheses and are biologically significant 

(Kirk 1996; Hoenig and Heisey 2001).

Parameter estimation, including the use of confidence intervals, provides more information than 

hypothesis testing, is more straightforward to interpret, and easier to compute (e.g., Steidl et al. 1997,

Gerard et al. 1998, Johnson 1999, Anderson et al. 2000, 2001, Colegrave and Ruxton 2003, 

Nakagawa and Foster 2004). Parameter estimation emphasizes the magnitude of effects and the 

biological significance of the results, rather than making binary decisions (Shaver 1993, Stoehr, 

1999). There is no formal classification of error associated with parameter estimation. It is assumed 

that the estimate is not accurate, and the width of the confidence interval provides information on the 

degree of uncertainty (Simberloff 1990). One of the primary recommendations from a workshop on 

environmental monitoring organized by the Ecological Society of America was that trend studies 

should focus on description of trends and their uncertainty, rather than hypothesis testing (Olsen et al.

1997). The use of confidence intervals was recommended over statistical testing when comparing 

two monitoring periods in a technical wildlife monitoring guide published by the USDA (Rowland 

and Vojta 2013). 

We will also employ control charts in data organization and analysis. Control charts, developed for 

industrial applications, indicate when a system is going ‘out of control’, by plotting through time 

some measure of a stochastic process with reference to its expected value (e.g., Beauregard et al.

1992, Gyrna 2001, Montgomery 2001). Control charts may be univariate or multivariate and can 

represent many different types of variables. Control charts have been applied to ecological data 

(McBean and Rovers 1998, Manly 2001), including fish communities (Pettersson 1998, Anderson 

and Thompson 2004) and natural resources within the I&M program (Atkinson et al. 2003). Control 

charts contain upper and lower control limits specifying thresholds beyond which variability in the 

indicator reveals that a biologically important change is occurring. Control limits can be set to any 

desired level.

We did not conduct a formal power analysis for this protocol for three reasons: (1) In many analyses 

sample size will equate with number of years; in this case, analyses will simply become more 

powerful over time. (2) Statistical power is dependent upon the hypothesis under test and the 

statistical test used. Over the course of this long-term monitoring program, we will be interested in 

many different questions and could potentially evaluate a number of different hypotheses. Thus there 

is no single ‘power’ relevant to the overall protocol. Estimating power at this point in the context of 

such a long-term, multifaceted monitoring program could be potentially misleading, as the test this 

power is based upon may rarely (or never) actually be employed. (3) Most of our data analyses will 

take the form of parameter estimation with associated confidence intervals, rather than null 

hypothesis significance testing. When estimating parameters, there is no associated statistical power. 

It is assumed that the estimate is not accurate, and the width of the confidence interval provides 

information on the degree of uncertainty. In general, statistical power analyses are frequently misused and misinterpreted in ecological contexts (Morrison 2007), and alternative approaches to 
38

evaluating the degree of uncertainty associated with our data will be evaluated and used when 

applicable.

Although our primary approach to organizing and analyzing data will consist of parameter estimation 

with associated confidence intervals, combined with the use of control charts, we do not entirely rule 

out the use of any statistical methods at this time. Depending upon the question of interest to resource 

managers, a hypothesis testing framework may be used. Because of the nature of this long-term 

monitoring program, other approaches (some of which may not have even been developed yet) may 

be appropriate at different points in time, depending upon the needs of the resource managers and 

questions of interest. 

The protocol is designed to allow the calculation of the ORAM and VIBI metrics, and these scores 

will be among the primary results. Analyses will be focused on the distribution of ORAM scores and 

categories, providing an overall review of wetland condition. The large sample size associated with 

the ORAM data will provide more opportunities to examine variables post hoc. Data analyses from 

the intensive survey will include a combination of species diversity metrics, as well as several 

metrics developed for the Vegetation Index of Biotic Integrity (Mack and Gara 2015) (Table 2). For

the status report, we will use descriptive statistics to describe wetland condition and compare species 

metrics (richness and evenness, FQAI score, cover weighted C of C, % sensitive, % invasive species) 

across all wetlands. These values will also be averaged for watersheds and compared among 

watersheds and LDI levels (poor, fair, good) using regression analysis and repeated measures 

ANOVA. Change analyses comparing metrics over time will be performed after 2 cycles of intensive 

sampling. Relationships with explanatory variables such as wetland size, average water levels, water 

type, HGM class, and dominant plant community will also be explored. 

Reporting

We will produce a status report after all survey wetlands have been sampled. Long-term changes are 

reported every five years thereafter. The report will follow the standard format required in the CSE 

Style Manual (2014) (SOP 16). The natural resource technical report template for NPS documents is 

also required (http://www.nature.nps.gov/publications/NRPM/index.cfm). 
39

Operational Requirements

Personnel Requirements

The network’s wetland biologist serves as the project manager and normally as the crew leader for 

this protocol. The wetland biologist is duty-stationed at Cuyahoga Valley National Park, but is 

supervised by the network coordinator or his/her designee in coordination with the park’s chief of 

resource management. Data management is the shared responsibility of the project manager and 

network data manager(s). Typically, the project manager oversees data collection, data entry, data 

verification and validation, as well as data summary, analysis, and reporting. The data manager 

designs the database and oversees data validation procedures, security, archiving, and dissemination. 

Qualifications and Training

The lead wetland biologist must be skilled in plant identification and practiced in wetland ecology, 

quantitative vegetation surveys, GPS navigation, GIS, data entry, and basic data analysis. The 

wetland scientist should have or receive training in ORAM and VIBI methodology through the Ohio 

Environmental Protection Agency. All field observers must be well-organized, work well as a team 

member, be comfortable in the field, and work methodically under difficult conditions.

Training is essential for developing competent observers (see SOP 1). Observers will review wetland 

plant identification using herbarium specimens, keys, and photographs. The wetland biologist will 

provide training to all biological technicians, interns, and volunteers (SOP 11). Observers will be 

tested frequently on their ability to identify plant species in the field.

Annual workload

Monitoring will require a two- to three-person crew each year. Annual field work varies over a 5-

year period and is outlined in Table 2. In addition, we complete hydrological monitoring between 

March 15 and October 15. In years in which we complete rapid assessments, the ORAM Survey

begins in May, but can be conducted through late October. Intensive surveys (i.e., VIBI) surveys 

begin on June 15 and end by October 15. In preparation for the field season, the project leader must 

oversee and organize seasonal hiring; volunteer recruitment; permit preparation; equipment testing, 

calibration, and maintenance; procurement; and training (SOP 14). The remainder of the year is 

reserved for voucher handling, data analysis, reporting, and administrative requirements.

Hiring Seasonal Biotechnicians

At least one seasonal biotechnician is hired annually to assist with the hydrological monitoring and 

vegetation surveys. The job recruitment announcement for this seasonal wetland biotechnician 

should be prepared and posted in November of the previous year to make sure the employee is 

cleared to begin work in late February or early March (SOP 14). New employees will need special 

job-specific training that will be provided as they enter on duty.

Volunteers

The wetland monitoring project relies on a cadre of dedicated volunteer “wetland monitors”. The 

volunteer effort began in 2009 (SOP 15) with volunteers since then dedicating thousands of hours
40

toward hydrological monitoring. As of 2016, many volunteers are returning for a sixth year to 

monitor the same location, while we have recruited to re-fill other volunteer positions. Wetland 

Monitors:

 Measure water levels once a week in groundwater wells or at staff gauges

 Complete presence / absence surveys for frogs and toads in our breeding wetlands using Frog 

Watch USA protocols

 Maintain our plant reference collection

 Assist with data entry

New volunteer recruitment and training begins early in the year through the assistance of the 

Conservancy for Cuyahoga Valley National Park. Space is reserved for Wetland Monitors at the 

Savacoal House, where volunteers can use an access code to enter the building at any point during 

the day. At this location, volunteers find park radios, field supplies, and data forms to guide their 

field work. The project leader seeks to maintain the volunteer’s productivity, interest, and morale 

through regular updates on the impacts of their information, in-field training, response to questions, 

and through an annual social gathering or workshops.

Annual budget 

Annual personnel, equipment, and training costs required to run the wetland monitoring program are 

nearly $160,000.00 (Table 10). The funding source is primarily from the Heartland Network’s Vital 

Signs funds, with in-kind support (~$27,100) provided from the host park for overhead expenses in 

the form of an office space, a vehicle, computer, GPS, radios, and other equipment. Personnel 

expenses for field work are based on a portion of the supervisor’s salary and 100% of the salary of 

permanently stationed field crew members: one full-time botanist or wetland ecologist (GS-9) and 

one to two seasonal biological technicians (GS-5). Administrative and data management expenses are 

staff personnel costs associated with the network coordinator, administrative officer, and data 

managers. Data analysis and reports are produced collaboratively with these individuals. Equipment

costs were estimated using five year, annualized start-up costs. 

Table 10. Estimated annual budget for required for wetland monitoring in Cuyahoga Valley National Park. 

Category Cost

Personnel $108,350

Administrative $8,500

Data Management $10,000

Equipment (5-year annualized) $3,665

CUVA In-Kind Contributions (office, truck, gps, etc.) $27,100

Certifications / Training $ 2,700

TOTAL $160,315.00
41

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49

Appendix A:

Wetland Monitoring Reference Frame

The following constituent files were used to create the reference frame for wetland monitoring in Cuyahoga Valley National Park. This 

reference frame was used to randomly select wetlands for inclusion in the sample of intensively- (and rapidly) assessed wetlands. Files from 

three major versions are listed. The names of the original constituent files were shortened for inclusion in the attribute table of the original 

reference frame (compare Original File Name and HTLN File Name). Other attribute information available for each constituent file listed in 

includes the year of the wetland survey, if wetlands were delineated using U.S. Army Corps of Engineers methodology (yes/no/in-part), and 

whether Ohio Rapid Assessment Method (ORAM) data were collected.

Original File Name HTLN File Name Original Projection Year (of survey)

File 

Version Source

Delineated? 

(Y/N/IP) ORAM

OldFarmWetlands_added120208 FarmWetland UTM Zone 17N, NAD1983 2002-2004 2009 NPS IP Y

urs_wetlands_1183_rev120908 UrsWetland UTM Zone 17N, NAD1927 2002-2004 2009 URS IP Y

wet_determ_offering2005 WetDeterm UTM Zone 17N, NAD1927 2004 2009 NPS IP Y

wetlands_081806_corrected_120108 RuralWetland State Plane, Ohio North, NAD 

1983, feet

2006 2009 NPS IP Y

Davey wetlands 060801 Davey UTM Zone 17N, NAD1927 1999-2000 2009 Davey N N

Brookside2008_wetpolyUTM Brookside2008 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

FawnPond1041_wetpoly2009UTM FawnPond1041 UTM Zone 17N, NAD1983 2009 2010 NPS Y Y

FawnPond1041slope_wetpoly_2009UTM FawnPond1041Slope UTM Zone 17N, NAD1983 2009 2010 NPS Y Y

IRA343_wetpoly_2008UTM Ira343 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

Kendall195_wetpoly_2008UTM Kendall195 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

Kendall205_2008wetpoly_UTM Kendall205 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

Kendall206_2008wetpoly_UTM Kendall206 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

Kendall206b_2008wetpoly_UTM Kendall206b UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

PleasantValley1045_2008wetpoly_UTM PleasantValley1045 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y
50

Original File Name HTLN File Name Original Projection Year (of survey)

File 

Version Source

Delineated? 

(Y/N/IP) ORAM

PleasantValley1046_2008wetpoly_UTM PleasantValley1046 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

PleasantValley1106_2008wetpoly_UTM PleasantValley1106 2008 2010 NPS Y Y

PleasantValley1108_2008wetpoly_UTM PleasantValley1108 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

PleasantValley1110_2008wetpoly_UTM PleasantValley1110 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

PleasantValley1112_2008wetpoly_UTM PleasantValley1112 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

PleasantValley1128_2008wetpoly_UTM PleasantValley1128 UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

RocksideRestoration_2008wetpoly_UTM RocksideRestoration UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

StumpyBasin2008_wetpoly_UTM StumpyBasin UTM Zone 17N, NAD1983 2008 2010 NPS Y Y

USACE_Wetlands USACE State Plane, Ohio North, NAD 

1983, feet

2009 2010 ES IP Y

SummitMP_Wetlands_2010Clip SummitMP State Plane, Ohio North, NAD 

1983HARN, feet

2003-2010 2010 MPSSC Y Y

CombinedWL0506_2010CVNPClip CombinedWL0506 State Plane, Ohio North, NAD 

1983, feet

2005-2006 2010 CMP N Y

CUVADelineations2010 CUVADelineations2010 UTM Zone 17N, NAD1982 2010 2013 NPS Y Y

CUVADelineations2012 CUVADelineations2012 UTM Zone 17N, NAD1983 2012-2013 2013 NPS Y Y

MPSSC_2013updates MPSSC_2013updates State Plane, Ohio North, NAD 

1983HARN, feet

2010-2013 2013 MPSSC Y Y
51

Appendix B:

Site Establishment Datasheet for Wetland Monitoring
52
53
54
55

Appendix C:

Wetland Studies from Cuyahoga Valley National Park

1996+ Great Lakes Marsh Monitoring Program

With financial support from the United States Environmental Protection Agency and the Great Lakes 

Protection Fund, the Great Lakes Marsh Monitoring Program (MMP) was launched as a bi-national

program between the United States and Canada in 1995 (Crewe et al. 2006). The program relies on 

partnerships between citizen scientists, foundations, governments, and non-governmental 

organizations to implement bird and amphibian monitoring in marshes within the Great Lakes 

Region. The MMP protocol was first used in Cuyahoga Valley National Park (CUVA) in 1996. Since 

then, the protocol has been used to monitor bird and amphibian populations in five marshes: 

Brookside, Heron Pond, Jaite, Lock 29, and Ira Beaver Marsh. 

Between 1995 and 2004, 166 bird routes and 170 amphibian routes were surveyed in the Lake Erie 

Region. At these sites, populations of mallards (Anas platyrhynchos) and yellow warblers 

(Dendroica petechial) increased. Area-sensitive nesters, on the other hand, such as American coot 

(Fulica americana), least bittern (Ixobrychus exilis), swamp sparrow (Melospiza georgiana) and 

Virginia rail (Rallus limicola) declined, as did obligate marsh-nesting species such as the marsh wren 

(Cistothorus palustris) and common moorhen (Gallinula chloropus) (Bird Studies Canada 2006). 

During the same time period, spring peeper (Pseudacris crucifer) and chorus frog (Pseudacris 

triseriata) populations increased at sites within the Lake Erie Basin, whereas bullfrogs (Rana 

catesbeiana) and northern leopard frogs (Rana pipiens) declined (Crewe et al. 2006).

Regional trends from 1995 to 2012 within the entire Great Lakes Region also indicated that marsh 

breeding birds were declining (Tozer 2013). Ten of 19 marsh-associated breeding species (53%)

showed population declines across the Great Lakes basin. Amphibian populations appeared stable

overall. Marsh ecosystem health in the region was lower in coastal marshes and high priority 

conservation areas (i.e., “areas of concern”) than in inland marshes and lower priority areas (Tozer 

2013).

1999-2000 Wetland Survey

Davey Resources Group mapped 1,217 wetlands covering 1,669 acres in CUVA during a 1999-2000 

survey to identify potential mitigation and restoration sites (Davey 2001). Surveyors focused on 

wetlands greater than 1 acre in size in all fee-owned areas to identify opportunities for restoration or 

mitigation in the park. Information such as wetland size, accessibility, types of impacts, and 

dominant species was collected for each wetland to estimate restoration feasibility. Compatible land 

use parcels and other parcels leased from the park service or privately-owned were surveyed when 

permitted. As polygons were digitized while in the field, the accuracy in the estimated size, shape, 

and location depended on the accuracy of the aerial photographs, topographic layers, and the field 

biologist’s visual estimate of the wetland boundary. As designed, the survey provided a broad 

overview of wetland location, size, and distribution within the park, but was not comprehensive. 
56

2003 Rural Landscape Management Program Survey (Countryside Initiative)

Wetland surveys were completed as part of the Rural Landscape Management Program (National 

Park Service 2003). To protect wetlands within the vicinity of agricultural areas, park staff surveyed 

approximately 210 wetlands between 2000 and 2006. This included 121 new wetlands not previously 

identified during the Davey Resource Group inventory (Davey 2001). Information collected for each 

wetland conformed to the NPS Procedural Manual 77-1 guidelines (National Park Service 2008). 

Wetlands located within a 50 m (164 feet) buffer of agricultural fields were delineated using a submeter accurate GPS, and the Ohio Rapid Assessment Method (ORAM) was used to determine 

quality. Large wetlands that extended beyond the 50 m boundary were not mapped to their entirety

and other data sources (wetland inventory, NWI, or aerial photos) were used to estimate the total 

wetland size. Specific setback criteria were used as a guideline for buffer zone establishment 

depending on wetland quality (ORAM category/setback distance+: 1/25 to 50 ft.; 2a/50-125 ft.; 

2b/125-200 ft.; 3/>200 ft.

2005 Degraded Wetland Restoration Plan

Cuyahoga Valley National Park prepared a Degraded Wetland Restoration Plan in 2005 (National 

Park Service 2005), following existing legislation and NPS policies that emphasize an obligation to 

restore degraded wetlands on National Park Service lands to the extent possible, including NPS 77 

(1991), Executive Order 11990 (1977), D.O. 77-1 (2008), CVNP’s General Management Plan

(1977), and CVNP’s Resource Management Plan (1999). The Degraded Wetland Restoration Plan 

provides guidance for site selection, planning, project approval and compliance. The plan is 

categorically excluded from NEPA compliance.

The restoration assessment completed by Davey Resource Group (Davey 2001) identified a number 

of wetlands that were degraded by invasive species, fill, modified hydrology, or dumping. Resource 

Management staff used the 2001 assessment to develop immediately actionable restoration sites in 

CUVA as of 2004 (Rockside Road, Pleasant Valley Road, Stone Road, Tinker’s Creek Road, and 

Hillside Road). The plan also listed some sites listed that did not seem actionable at the time because 

of natural recovery (Fawn Pond, Lock 29, Terra Vista) and sites that should be added to the Small 

Disturbed Sites action plan (Snowille, Bedford, and Volkert) and prioritized for restoration under that 

plan. The Degraded Wetland Restoration Plan should be appended from time to time, and newly 

discovered restoration sites should be treated in a manner consistent with the plan (2005).

2005-2006 Assessment of Wetlands in the Cuyahoga River Watershed of 

Northeast Ohio

In 2005 and 2006, Ohio Environmental Protection Agency (Ohio EPA) investigators and partners 

completed an assessment of wetlands in the Cuyahoga River watershed (Fennessy et al. 2007). A 

total of 243 wetlands, including 10 wetlands within CUVA, were assessed for condition and land-use 

impacts. Three different approaches were used to assess wetland quality: 1) a landscape analysis, 

calculated using remotely sensed data, as a “landscape development intensity index” (LDI), 2) 

qualitative, rapid, field-based indices, including the ORAM, and 3) detailed plant and animal data 

used to calculate Vegetation Index of Biotic Integrity (VIBI) and the Amphibian Index of Biotic 
57

Integrity (AmphIBI). The Ohio Wetland Inventory was used as the sample frame for the population 

of wetlands in the watershed, and the generalized random tesselation stratified (GRTS) survey design 

was used for site selection. The assessment was designed to allow comparison of wetland quality 

across different wetland types. The study included depressional (n = 87), riverine (n = 93), man-made 

or beaver-made impoundments (n = 16), slope wetlands (n = 35), fringing wetlands (n = 9), and bogs 

(n = 3).

The average ORAM score for all wetlands in the study was “good” (55.6, Category 2). Across the 

entire Cuyahoga River watershed, the ORAM showed that 9.1% of individual wetlands were in poor 

condition (Category 1), 13.2% in fair condition (Modified 2), 51.0% in good condition (Category 2), 

and 26.7% in excellent condition (Category 3). There were no statistically significant differences in 

average ORAM scores by condition category for HGM class or plant community. However, 

depressional wetlands tended to score lower than other HGM types. The percentage of wetlands in 

the highest quality Category (3) declined from the upper basin to the middle basin and also the lower 

basin where CUVA is located. 

An analysis of variance suggested that the predictive power of the LDI was low for assigning 

condition categories to individual wetlands. However, at the population level, the LDI provided good 

estimates for the number of wetlands that fell within the three major condition categories. Land uses 

within relatively narrow buffer distances from the wetlands (100 m and 250 m buffers) were 

important indicators of wetland condition, and wetlands with lower intensity land uses within the 100 

m buffer corresponded with better condition. LDI scores at greater buffer distances tended to 

homogenize. Fennessy et al. recommended limiting un-natural land uses within the 100 m buffer 

zone for wetland protection. Not all wetlands reacted similarly to the same land uses and HGM 

classes appeared to be an important variable for understanding wetland response to stressors. 

The low sample size of the VIBI and AmphiBI (n = 20) and the lack of Category 1 wetlands inhibited 

the ability to calibrate the LDI and rapid methods with the VIBI data. However, there was 60% 

agreement between the VIBI and the ORAM. The ORAM over-categorized in all cases, usually 

within one condition class, raising a “fair” VIBI score (modified Category2) to a “good” ORAM 

score (Category 2). 

2003 Developing Indicators of Wetland Health

In 2003, researchers from the University of Akron initiated a study to identify potential ecological 

indicators of wetland health in park wetlands (Fraser 2004). Using the Davey Resource Group study 

as the sample frame, investigators selected 48 wetlands based on wetland type, size (<1 acre or >1 

acre), and disturbance. A range of ecological variables from five major categories (water, vegetation, 

soils, biology, and landscape) were monitored at each wetland between July 2003 and March 2004

(Table A1). Frequency graphs and quartile tables were developed for each ecological parameter to 

determine normal ranges and identify values that may be potential warning signs of degradation. 

Three-way ANOVAs were used to examine the effects of wetland type, wetland size, and distance to 

disturbance on each variable. 
58

Potential indicators were grouped into good, fair, and poor categories based on their frequency 

distributions. Properties that fell within the “good” frequency category were considered good 

indicators because the data were normally distributed with a median at the midway point of the range 

of values and with evident tails at the lower and upper range. The ANOVAs (p < 0.1) and correlation 

matrix indicated relationships (p < 0.05) between several key parameters and measures of 

disturbance. 

Based on this analysis, the studied identified the following functional relationships. 

 Invasive species cover and species richness were higher in wetlands that were closer to a 

disturbance. 

 Conductivity, total dissolved solids, and phosphate (PO4) levels were lower and ammonium 

(NH4) values were higher in wetlands located further away from disturbance. 

 Conductivity, total dissolved solids, and soil magnesium (Mg) values within wetlands 

increased with watershed disturbance. 

 Biomass measurements were generally always higher in marsh and wet meadow wetlands.

 ORAM scores were highest in large wetlands and wetlands furthest from disturbance.

Researchers recommended devoting more attention to nine ecological parameters: soil Mg, soil 

nitrogen, plant species per m2, plant biomass , size of watershed, percent of watershed classified as 

disturbed, water depth, ammonium, conductivity, and total dissolved solids (ppm) for wetland vital 

signs monitoring programs. 

Table C1. Summary statistics (Median, lower 25%, and upper 75%) and frequency distribution categories 

of variables collected from wetlands in the Fraser (2004) study (n = 48 wetlands). For future monitoring 

efforts, Fraser suggested that measurements found below the 25th lower limit and above the 75th upper 

limit are thought to be outside the normal range and should be used to direct further observation and 

management. Parameters were classified into good, fair, and poor frequency distribution categories 

based on whether they were normally distributed.

PARAMETERS LOWER 25% MEDIAN UPPER 75%

Frequency Distribution 

Category

Soils

Moisture (%) 34.37 40.98 53.37 Good

CEC (meq/100g) 12.7 13.95 17.0 Good

K Saturation (%) 1.1 1.3 1.6 Fair

*Mg Saturation (%) 10.15 11.95 14.20 Good

Ca Saturation (%) 57.25 71.00 81.10 Fair

pH 6.25 6.80 7.10 Good

Organic Matter (%) 1.85 2.20 3.30 Fair

P (lbs/A) 10.0 16.5 21.0 Good

K (lbs/A) 116.5 137.5 166.0 Fair

*Mg (lbs/A) 329.5 388.5 500.0 Good

Ca (lbs/A) 2775.0 4136.0 5653.5 Poor

*N (%) 0.190 0.265 0.380 Fair
59

Table C1 (continued). Summary statistics (Median, lower 25%, and upper 75%) and frequency 

distribution categories of variables collected from wetlands in the Fraser (2004) study (n = 48 wetlands). 

For future monitoring efforts, Fraser suggested that measurements found below the 25th lower limit and 

above the 75th upper limit are thought to be outside the normal range and should be used to direct further 

observation and management. Parameters were classified into good, fair, and poor frequency distribution 

categories based on whether they were normally distributed.

PARAMETERS LOWER 25% MEDIAN UPPER 75%

Frequency Distribution 

Category

Vegetation

Total # of species 19.0 31.5 41.5 Poor

*# of species per m23.0 6.0 8.0 Fair

*Biomass/m2 146.01 326.33 428.82 Fair

Landscape

ORAM 52.25 58.25 63.00 NA

Watershed size (acres) 2.595 12.870 34.890 Poor

Biology

Decomposition 1 (%) 38.50 49.06 64.17 Good

Decomposition 2 (%) 83.43 88.75 96.38 Poor

Water JULY 2003

*Depth (inches) -0.08 1.33 4.10 Good

pH 6.67 6.88 7.16 Poor

*Conductivity (mS) 323.33 465.50 899.67 Fair

*TDS (ppm) 152.33 236.83 455.00 Fair

DO (%) 0.733 1.800 2.825 Fair

NH4 (ppm) 0.123 0.270 0.530 Fair

*NO3 (ppm) 0.037 0.207 0.368 Poor

PO4 (ppm) 0.673 2.668 5.180 Fair

Water OCTOBER 2003

*Depth (inches) 1.83 5.78 11.63 Fair

pH 6.42 6.76 7.15 Good

*Conductivity (mS) 365.3 592.3 823.5 Good

*TDS (ppm) 169.2 297.3 397.5 Good

DO (%) 0.350 0.933 1.717 Fair

NH4 (ppm) 0.178 0.462 1.425 Fair

*NO3 (ppm) 0.022 0.078 0.167 Fair

PO4 (ppm) 1.733 4.000 5.833 Fair

Water MARCH 2004

*Depth (inches) -1.67 -0.46 0.83 Good

pH 6.36 6.78 7.18 Good

*Conductivity (mS) 333.5 483.7 742.0 Good

*TDS (ppm) 170.3 273.5 524.8 Good

DO (%) 0.850 1.417 2.783 Fair

NH4 (ppm) 0.028 0.108 0.310 Fair

*NO3 (ppm) 0.127 0.177 0.282 Good

PO4 (ppm) 0.233 1.200 3.133 Fair

*Identified as an important indicator for wetland vital signs monitoring as a result of the study
60

2006 Hydrology Study

A graduate student at the University of Akron studied relationships between hydrology, plant 

community structure, and watershed disturbance in 16 wetlands in CUVA (Manning 2006). The 

wetlands were selected from the 48 wetlands in the Fraser et al. (2004) study. He selected four 

wetlands from each Cowardin (1979) type (emergent, forested, shrub, and wet meadow), and for 

each type, he selected two wetlands from each of two watershed disturbance groups (high or low).

Mapped disturbances included roads, golf courses, housing developments, and agricultural land. The 

wetlands were classified into high or low categories of disturbance after calculating the percentage of 

the watershed that was considered disturbed. 

The results of this study indicated that the effects of disturbance on water levels, biomass, and 

conductivity were only significant in combination with wetland and watershed size, suggesting that 

the effects of disturbance may be mitigated or exacerbated by wetland and watershed size. In 

combination, however, watershed size and disturbance explained 99.6% of the variation in water 

level and biomass, while neither component strongly predicted water level individually. Wetland 

type, size, disturbance, and watershed size all affected the plant species richness, which was higher at 

disturbed sites (44.3) than non-disturbed sites (29.4). Larger wetlands and emergent types also had 

higher species richness. Invasive, non-native plants were among the top three dominant species in 10 

of the 16 sites.

2004 Vernal Pool Study

Researchers from Case Western Reserve University examined the changes in physicochemical 

properties related to disturbance in vernal pools in (n = 10) and around (n = 20) CUVA (CarrinoKyker and Swanson 2007). Specifically, researchers examined dissolved oxygen, pH, conductivity, 

and average water depth monthly, from snow melt (March) to dry down (June). They also measured 

nitrate, phosphate, ammonia, nitrogen, chloride, and silica in May and June. The overriding 

hypothesis was that the water quality of the vernal pools within disturbed watersheds would be 

affected by winter pollutants such as deicing salts and spring/summer fertilizers and pesticides due to 

recharge by surface runoff and groundwater that may contain these pollutants. The percentage of 

agriculture/grass, urban, suburban, open water/stream, wooded, shrub/scrub, and barren land uses 

were calculated within the study watersheds to examine the effects of land use. 

From March to June, mean temperatures increased from 6.4°C, 13.6°C, 17.9°C, and 17.4°C, 

respectively, and were accompanied by a decrease in dissolved oxygen from 9.05 mg/L in March, 

3.94 mg/L in April, 3.30 mg/L in May, to 2.19 mg/L in June. The temperature increase likely 

resulted from a temperature increase in the water recharge source (snowmelt vs rain water). Of the 

30 pools, six dried in May, 17 dried in June, and seven did not dry during the study period (CarrinoKyker and Swanson 2007). Conductivity and pH changes over time were relatively minor, although 

conductivity measurements were higher in pools near agriculture. Water depth in the pools was 

highest in March and April. Water depth was negatively correlated with urban development and 

positively correlated with agriculture. Most nutrients were present in low concentrations, however, 

chloride and silica values were considered high in May and June, ranging from 8–485 mg/L, and 0.8–
61

15 mg/L. Elevated chloride could be the influence of more runoff from icing salts, but elevated silica 

concentrations may be regional, influenced by the presence of Berea sandstone, which is a natural 

source of silica.

Floristic Surveys

Several vegetation surveys dating back to 1969 provided useful information regarding rare plant 

occurrences and community types within area in CUVA that included wetlands. Affiliates of the 

Kent State University, University of Akron, and Cleveland State University completed the majority 

of these studies (Andreas 1986, Brown et al. 1981, DeWitt 1990, Holeski 1969, Kroonemeyer 1990, 

Mazzer 1980, Mazzer 1989, Wilder and McCombs 1995, Wilder and McCombs 1999).

References

Andreas, B.K. 1986. Botanical surveys on the Cuyahoga Valley National Recreation Area with 

special emphasis on state-listed rare species. Unpublished Report for the National Park 

Service. 410 pp.

Carrino-Kyker, S.R. and A.K. Swanson. 2007. Seasonal Physicochemical characteristics of thirty 

northern Ohio temporary pools along gradients of GIS-delineated human land-use. Wetlands 

27:749–760.

Cowardin, L.M., C. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of Wetlands and 

Deepwater Habitats of the United States. FWS/OBS-79/31. U.S. Fish and Wildlife Service, 

Washington D.C. 

Crewe T.L., S.T.A Timmermans, and K.E. Jones. 2006. The Marsh Monitoring Program 1995 to 

2004: A Decade of Marsh Monitoring in the Great Lakes Region. Published by Bird Studies 

Canada in cooperation with Environment Canada. 28 pp.

Davey Resource Group. 2001. GIS Wetlands Inventory and Restoration Assessment: Cuyahoga 

Valley National Park, Cuyahoga and Summit Counties, Ohio. Report prepared by Davey 

Resource Group for HNTB Ohio, Inc. 38 pp.

DeWitt, C.B. 1990. Evaluation of wetlands north and south of Sanitation Road. Unpublished 

Report to National Park Service. 4 pp.

Executive Order No. 11990, 42 FR 26961, 3 CFR, 1977 Comp., p. 121, Protection of Wetlands.

Fennessy, M. S., J. J. Mack, E. Deimeke, M. T. Sullivan, J. Bishop, M. Cohen, M. Micacchion 

and M. Knapp. 2007. Assessment of wetlands in the Cuyahoga River watershed of northeast 

Ohio. Ohio EPA Technical Report WET/2007-4. Ohio Environmental Protection Agency, 

Division of Surface Water, Wetland Ecology Group, Columbus, Ohio.

Fraser, L.H. 2004. Developing indicators for the assessment of wetland health in the Cuyahoga 

Valley National Park. Unpublished Report for the National Park Service.

Holeski, P.M. 1969. A Floristic Survey of Stumpy Basin. Thesis. University of Akron, OH. 46 

pp.
62

Brown, F., K. Ceroky, M. Dahn, J. Dubsky, R. Eyerly, W.Hance, L.Reinhold, D. Sears. 1981. 

CVNRA Pond Survey Water Analysis and Observations. Research Projects for Science 

Teachers. 3370:490/590-903. Oak Hill Center, University of Akron, OH.

Kroonemeyer, K.E. 1990. Untitled Report to J. Debo, Jr. (National Park Service) concerning the 

studied region of Original Lot 97. Unpublished. 3 pp.

Manning. N. 2006. Long term hydrology patterns and plant community composition of disturbed 

and undisturbed wetlands in the Cuyahoga Valley National Park, Ohio, USA. Thesis.

University of Akron, Ohio.

Mazzer, S.J. 1980. A preliminary survey of the bryophytic flora of the Cuyahoga Valley National 

Recreation Area. Unpublished Report to the National Park Service. 22 pp.

Mazzer, S.J. 1989. Untitled report to David Kopchak (National Park Service) concerning the 

studied region of Original Lot 97. Unpublished Report to the National Park Service. 6 pp.

National Park Service. 1991. NPS-77 Natural Resources Management Guidelines. 

National Park Service. 1977. Cuyahoga Valley National Recreation Area General Management 

Plan. 

National Park Service. 1999. Cuyahoga Valley National Recreation Area Resources

Management Plan

National Park Service. 2002. National Park Service Director's Order # 77-1, Wetland Protection. 

National Park Service. 2003. Rural Landscape Management Program, Environmental Impact 

Statement. Cuyahoga Valley National Park, Ohio.

National Park Service. 2008. National Park Service Procedural Manual #77-1: Wetland

Protection.

Tozer, D. C. 2013. The Great Lakes Marsh Monitoring Program 1995-2012: 18 years of 

surveying birds and frogs as indicators of ecosystem health. Published by Bird Studies 

Canada. 10 pp.

Wilder, G.J. and McCombs, M.R. 1999. A Floristic Study of Fawn Pond and Surrounding 

Territory (Cuyahoga Valley National Recreation Area and Brecksville, Ohio). Castanea

64(1):50-63.

Wilder, G.J. and McCombs, M.R. 1995. An inventory of vascular plants situated in and 

surrounding a beaver pond within the Cuyahoga Valley National Recreation Area. 

Investigators Annual Report 1993.
63

Appendix D:

Wetland Sampling Workflow for Intensively Sampled 

Wetlands

The wetland monitoring protocol for Cuyahoga Valley National Park includes a number of relatively 

complex steps. These steps include watershed ranking, wetland selection, wetland delineation, plot 

placement, well installation, vegetation measurement, and hydrological measurement. The diagram 

below (Figure B1) is designed to provide a heuristic that visually summarizes these complex steps. 

Field workers involved in implementing the protocol may benefit from using the diagram to review 

the steps and for facilitating communication about various protocol steps.
64

Figure B1. Diagram of steps required to assess wetlands using the “Wetland Monitoring Protocol for Cuyahoga Valley National Park”.
The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific 

and other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities.

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www.nature.nps.gov

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