Matthew A. Jennettedelawareestuary.s3.amazonaws.com/pdf/Summit15/PDE-Report...Figure 17. Site Specific Intensive Monitoring (SSIM) point locations at Christina River, Wilmington, DE:

ii Christina River Watershed Wetland Condition Report

Matthew A. Jennette

Alison B. Rogerson

Andy M. Howard

Delaware Department of Natural Resources and Environmental Control

Division of Watershed Stewardship

820 Silver Lake Blvd, Suite 220

Dover, DE 19904

LeeAnn Haaf

Angela Padeletti

Kurt Cheng

Jessie Buckner

Partnership for the Delaware Estuary

110 South Poplar Street, Suite 202

Wilmington, DE 19801

The citation for this document is:

Jennette, M.A., Haaf, L., A.B. Rogerson, A.M. Howard, A. Padeletti, K. Cheng, and J. Buckner.

2014. Condition of Wetlands in the Christiana River Watershed. Delaware Department of

Natural Resources and Environmental Control, Watershed Assessment and Management

Section, Dover, DE and the Partnership for the Delaware Estuary, Wilmington, DE.

Christina River Watershed Wetland Condition Report iii

ACKNOWLEDGEMENTS

Funding for this project was provided by EPA Region III Wetland Program Development

Grant Assistance # CD-96312201-0, EPA National Estuary Program, the Delaware Department

of Natural Resources, and the DuPont Clear into the Future program. Technical support and

field research was made possible by the contributions of many individuals. Tom Kincaid and

Tony Olsen with the EPA Office of Research and Development Lab, Corvallis, Oregon provided

the data frame for field sampling and statistical weights for interpretation. Kristen Kyler and

Rebecca Rothweiler participated in many field assessments under a myriad of conditions.

Maggie Pletta and Michelle Lepori-Bui were helpful with revisions on drafts of the report, as

well as producing the cover art for the final document.

iv Christina River Watershed Wetland Condition Report

CONTENTS

Executive Summary .........................................................................................................................1

Introduction ......................................................................................................................................3

Watershed Overview ........................................................................................................................5

2.1 Geology and Hydrogeomorphology ....................................................................................5

2.2 Watershed History and Land-use ........................................................................................6

2.3 Wetland Resources..............................................................................................................8

Section 1: Condition of Wetlands in the Christina River Watershed .....................................11

Methods..........................................................................................................................................11

3.1 Changes to Wetland Acreage ............................................................................................11

3.2 Field Site Selection ...........................................................................................................11

3.3 Data Collection .................................................................................................................12

3.3.1 Landowner Contact and Site Access....................................................................... 12

3.3.2 Assessing Tidal Wetlands ....................................................................................... 12

3.3.3 Assessing Non-tidal Wetland Condition ................................................................. 15

3.4 Presenting Wetland Condition ..........................................................................................18

Results ............................................................................................................................................20

4.1 Landscape Analysis of Changes in Wetland Acreage ......................................................20

4.2 Landowner Contact and Site Access.................................................................................21

4.3 Condition of Tidal Wetlands .............................................................................................22

4.4 Condition of Non-tidal Wetlands ......................................................................................24

4.4.1 Non-tidal Flat Wetland Condition .......................................................................... 24

4.4.2 Non-tidal Riverine Wetland Condition ................................................................... 27

4.4.3 Non-tidal Depression Wetland Condition ............................................................... 29

4.5 Overall Condition and Watershed Comparison ................................................................29

Management Recommendations ....................................................................................................30

Section 2: Establishment of a Permanent Intensive Monitoring Station in the Christina

River Watershed ..........................................................................................................................33

Summary ........................................................................................................................................33

Introduction ....................................................................................................................................33

Methods..........................................................................................................................................34

5.1 Components of Site Specific Intensive Monitoring Stations ............................................34

5.1.1 Surface Elevation Tables (SETs) and Marker Horizons (MH) ............................... 34

Christina River Watershed Wetland Condition Report v

5.1.2 Soil Quality and Biomass........................................................................................ 35

5.1.3 Vegetation Transects ............................................................................................... 35

5.1.4 Vegetation Data in Fixed Plots ............................................................................... 35

5.1.5 Water Quality .......................................................................................................... 36

Results ............................................................................................................................................36

5.2.1 Surface Elevation Tables (SETs) and Marker Horizons (MH) ............................... 36

5.2.2 Soil Quality and Biomass........................................................................................ 38

5.2.3 Vegetation Transects ............................................................................................... 38

5.2.4 Vegetation in Fixed Plots ........................................................................................ 39

5.2.5 Water Quality .......................................................................................................... 42

Intensive Monitoring Conclusions .................................................................................................42

Literature Cited ..............................................................................................................................44

Appendix A: Qualitative Disturbance Rating (QDR) Category Descriptions ...............................47

Appendix B: DERAP Stressor Codes and Definitions ..................................................................48

Appendix C: DERAP IWC Stressors and Weights........................................................................50

Appendix D: MidTRAM Raw Data and Metric Scores from Estuarine Sites in the Christina

River Watershed.............................................................................................................................52

Appendix E: DERAP Wetland Assessment Stressor Checklist for Non-tidal Flat Wetlands in the

Christina River Watershed .............................................................................................................55

Appendix F: DERAP Wetland Assessment Stressor Checklist for Non-tidal Riverine Wetlands in

the Christina River Watershed .......................................................................................................58

Appendix G: DERAP Wetland Assessment Stressor Checklist for Non-tidal Depression

Wetlands in the Christina River Watershed ...................................................................................61

Appendix H: Site Specific Intensive Monitoring plot Locations and Sampling Dates .................62

Appendix I: Vegetation Zone Dominance .....................................................................................65

vi Christina River Watershed Wetland Condition Report

FIGURES

Figure 1. Location of the Christina River Watershed and the major basins of Delaware.

Watersheds at the Hydrologic Unit Code 10 scale are outlined in gray. ........................................ 5

Figure 2. Land cover of the Christina River watershed based on 2005 (PA), 2007 (DE), and 2010

(MD) Land-use/Land-cover datasets. ............................................................................................. 6

Figure 3. Pre- and post-construction alignment of the Christina River following the completion

of Interstate 95. The pre-construction channel boundary is outlined in orange, and the current

alignment southeast of Newport is outlined in red. ........................................................................ 7

Figure 4. Distribution of wetlands in the Christina River watershed, based on 2007 mapping. .... 9

Figure 5. Standard assessment area, subplot locations, and buffer used to collect data for the

Mid-Atlantic Tidal Rapid Assessment Method Version 3.0. ........................................................ 12

Figure 6. Standard assessment area and buffer used to collect data for the Delaware Rapid

Assessment Procedure Version 6.0. .............................................................................................. 15

Figure 7. An example CDF showing wetland condition. The red line is the population estimate.

The orange and green dashed lines show the breakpoints between condition categories. ........... 19

Figure 8. Estimated historic and present wetland coverage in the Christina River watershed. .... 20

Figure 9. Ownership of sampled wetland sites in the Christina River watershed (left) and success

rates for sampling private wetland sites (right). ............................................................................ 21

Figure 10. Location of wetland assessments performed in the Christina River watershed in 2011.

....................................................................................................................................................... 22

Figure 11. Box and whisker plot of buffer, hydrology, and habitat attribute group scores from

tidal wetlands in the Christina River watershed. .......................................................................... 23

Figure 12. The cumulative distribution function for tidal wetlands in the Christina watershed.

The orange and green dashed lines signify the condition category breakpoints dividing severely

stressed from moderately and minimally stressed portions of the tidal wetland population. ....... 24

Figure 13. Cumulative distribution function for non-tidal flat wetlands in the Christina River

watershed. Condition scores for the wetland population are represented as the red line with 95%

confidence intervals (gray dashed lines). The orange and green dashed lines designate condition

category breakpoints dividing severely stressed, moderately stressed, and minimally stressed

wetlands. ....................................................................................................................................... 26

Figure 14. Cumulative distribution function for non-tidal riverine wetlands in the Christina River

watershed. Condition scores for the wetland population are represented as the red line with 95%

confidence intervals (gray dashed lines). The orange and green dashed lines designate condition

category breakpoints dividing severely stressed, moderately stressed, and minimally stressed

wetlands. ....................................................................................................................................... 28

Figure 15. Combined condition of non-tidal flat and riverine wetlands in the Christina River

watershed, compared to wetland condition in the St. Jones, Murderkill, Mispillion, Broadkill, and

Inland Bays watersheds................................................................................................................. 29

Christina River Watershed Wetland Condition Report vii

Figure 16. Example of Site Specific Intensive Monitoring sampling lay-out along the main

waterway. ...................................................................................................................................... 33

Figure 17. Site Specific Intensive Monitoring (SSIM) point locations at Christina River,

Wilmington, DE: permanent vegetation plots (PVs; groups of three) and random edge plots

(REs) distributed along shoreline of main water bodies. .............................................................. 34

Figure 18. Cumulative accretion height change for Christina River. Heights are given as the

difference in mean accretion height of all SETs between sampling dates, error bars are standard

error. Arrow marks the approximate date of the landfall of Hurricane Sandy in New Jersey

(October 29, 2012). Yearly SET height changes (mm) are 15.6, 12.5, 8.2 for SET 1, 2, and 3,

respectively, averaging 12.1 mm/yr. Linear models predict NS from slope =0. .......................... 37

Figure 19. Cumulative accretion height change for Christina River. Heights are given as the

difference in mean accretion height of all SETs between sampling dates, error bars are standard

error. Arrow marks the approximate date of the landfall of Hurricane Sandy in New Jersey

(October 29, 2012). Yearly accretion rate is 1.27 mm/yr. Slope NS from zero ........................... 37

Figure 20. Percentages of dominant vegetation type over all three RTK GPS transects in 2012. 38

Figure 21. Percentages of dominant vegetation type over all three RTK GPS transects in 2011. 38

Figure 22. Three year trend of Vegetation Zone Dominance (VZD) scores at PVs (near, mid, and

far) and REs. Lower VZD scores are communities whose structure suggest more tidal flooding.

....................................................................................................................................................... 40

Figure 23. Principal component analysis for Christina River. Red labels are main analysis

variables; blue arrows are covariant environmental variables. Vertices of polygons are labeled A,

B, and C, for near, mid, and far, respectively, followed by the sample year ................................ 41

TABLES

Table 1. Land use cover in the Christina River watershed based on 2005 (PA), 2007 (DE), and

2010 (MD) Land-use/Land-cover datasets. .................................................................................... 8

Table 2. Wetland acreage and proportion for each hydrogeomorphic wetland type in the

Christina River Watershed. ............................................................................................................. 9

Table 3. Metrics measured with the Mid-Atlantic Tidal Rapid Method Version 3.0. .................. 13

Table 4. Metrics measured with the Delaware Rapid Assessment Procedure Version 6.0. ......... 15

Table 5. Condition categories and breakpoint values for tidal and non-tidal wetlands in the

Christina River watershed as determined by wetland condition scores. ....................................... 18

Table 6. Composition of wetland condition classes (left) and the occurrence of common wetland

stressors (right) of non-tidal flat wetlands in the Christina River watershed. .............................. 25

Table 7. Composition of wetland condition classes (left) and the occurrence of common wetland

stressors (right) of non-tidal riverine wetlands in the Christina River watershed. ....................... 27

viii Christina River Watershed Wetland Condition Report

Table 8. Surface Elevation Table heights for Christina River. Data are given as the difference in

mean pin height between sampling dates, error values are standard error. .................................. 36

Table 9. Marker Horizon Heights for Christina River. Data are given as the mean accretion

height at sampling dates. Error values are standard error. ............................................................ 37

Table 10. Mean blade heights (cm) of first and second dominant species within near, mid, and far

PVs. ............................................................................................................................................... 39

Table 11. Percent of light penetration is given by light intensity at the bottom of the canopy

divided by the light intensity above the canopy. ........................................................................... 40

Table 12. Percentages of the first two most dominant species found in each plot, followed by the

diversity index and mean VZD score. Percentages may not equal 100% as species coverage in

plot may be highly overlapped or sparse. ..................................................................................... 40

Table 13. Average total alkalinity, turbidity, chlorophyll α concentration, total suspended solids

respectively, measured from water samples taken from the main water body of Christina River in

2013. Error values are standard error, n = 5. ................................................................................ 42

Table 14. Average temperature, salinity (uS/cm and ppt), dissolved oxygen (percent and

concentration), and pH respectively, measured from YSI meter from the main water body of

Christina River. Error values are standard error. N = 5, except on 1/9/2011, only one

measurement was taken for values with no error due to an equipment malfunction, otherwise

n=2. ............................................................................................................................................... 42

Christina River Watershed Wetland Condition Report 1

EXECUTIVE SUMMARY

The Delaware Department of Natural Resources and Environmental Control (DNREC)

and the Partnership for the Delaware Estuary (PDE) documented wetland trends and ambient

condition of wetland resources in the Christina River Watershed in 2011. The goal of this

project was to identify historic and recent changes in wetland acreage, assess the condition of

tidal and non-tidal wetlands throughout the watershed, identify prevalent wetland stressors, and

make watershed specific management recommendations. We will utilize information on wetland

losses and sources of wetland degradation in the Christina Watershed to guide future protection

and restoration activities and educate the public on watershed stewardship.

The Christiana River watershed encompasses 78 square miles (20,000 ha) of northern

New Castle County, Delaware, northeastern Cecil County, Maryland, and southern Chester

County, Pennsylvania. The Christina River drains four additional watersheds from the Piedmont

Physiographic Province, which collectively forms the Christina River Basin. The Christina

River originates in Landenburg, PA and flows 35 miles (55 km) eastward through Newark,

Christiana, and Newport, DE before emptying into the Delaware River through the Port of

Wilmington. Approximately 10% of the watershed (5,000 acres) is covered by wetlands, namely

non-tidal headwater flats (40%), riverines (23%), depressions (16%), and tidal estuarine marshes

(19%).

We estimated historic and recent wetland losses in the watershed based on historic hydric

soil maps and past wetland mapping efforts. The Christina River and its tidal wetlands have

been altered significantly since European settlement, including channel modifications and

extensive diking for agriculture. As populations grew and heavy industries expanded, many

wetlands in the eastern half of the watershed were filled to allow for development.

Approximately 46% of the wetlands in the watershed have been filled or otherwise lost,

primarily in Wilmington, southern Newark, and along the Interstate 95 corridor. Despite stricter

wetland regulations in New Castle County, compared to Kent and Sussex counties, Delaware,

approximately 81 acres of wetlands were converted between 1992 and 2007. These wetland

losses were largely associated with residential development, road expansions, and disposal of

dredged material along the Delaware River.

To assess wetland condition and identify stressors affecting wetland health we conducted

rapid assessments at random wetland sites throughout the watershed. Wetland assessments were

performed in 30 tidal wetlands using the Mid-Atlantic Tidal Rapid Assessment Method Version

3.0 and in 40 non-tidal riverine wetlands, 32 non-tidal flat wetlands, and two non-tidal

depression wetlands using the Delaware Rapid Assessment Method Version 6.0. Assessed

wetland sites were located on public and private property and randomly selected utilizing a

probabilistic sampling design with the assistance of the Environmental Protection Agency’s

Ecological Monitoring and Assessment Program.

Estuarine wetlands in the Christina River watershed were primarily freshwater marshes

dominated by emergent vegetation, which cover almost 1,000 acres of land. The buffers

surrounding tidal marshes were generally in poor condition, with human disturbance found

surrounding nearly every wetland. Point source pollution inputs from these developed areas

2 Christina River Watershed Wetland Condition Report

were found in 40% of the wetlands. Invasive plant species were pervasive in the watershed and

found in a majority of the freshwater marshes.

Non-tidal flat wetlands were typically found in the western half of the watershed near the

headwaters, primarily in low-lying forested areas. Of the 2,000 acres of flat wetlands estimated

to be in the Christina River watershed, only 14% were found to be in a minimally stressed

condition, while 41% of flats were severely stressed. Altered plant communities were found in a

majority of flat wetlands, including invasive plant cover and recent forestry activities. Ditching,

filling, and disturbed wetland buffers were also common stressors found in non-tidal flat

wetlands.

Riverine wetlands were found along the upper reaches of the Christina River, as well as

its tributaries, and are vital for flood attenuation and improving surface water quality.

Approximately 1,200 acres of non-tidal riverine wetlands were found in the watershed, with only

8% in a nearly undisturbed condition and 40% highly disturbed. Invasive plant species were

found in nearly every riverine wetland, with almost half of the wetlands dominated by invasives.

Alterations to the stream channel, as either channelization or stream incision, occurred along

60% of riverine wetlands, with another 53% of riverine wetlands partially filled with yard waste

or spoil material.

Compared to five watersheds previously assessed in Delaware, non-tidal wetlands in the

Christina River watershed are in considerably worse condition. The Christina River watershed

contained the highest proportion of severely stressed wetlands, as well as the lowest proportion

of high-condition than any watershed in southern Delaware.

An intensive monitoring station was also established in freshwater tidal marshes along

the Christina River. Several permanent fixtures were installed at the Christina River SSIM

station including surface elevation tables (SETs) and permanent vegetation plots. These fixtures

were monitored from 2011 to 2013, along with a suite of other metrics including water quality

and plant biomass. Three years of data, however, is inadequate to make conclusions about long-

term trends. Yet, data reported here suggest that between 2011 and 2013, the marsh platform had

increased in elevation at SET benchmarks. These elevation changes are most likely the result of

subsurface swelling. Some disparate results were observed between SET benchmark and plant

community data; overall, however, these data suggested an increasing trend, but at least 5 years

of data is needed to determine the inherent natural variation.

Based on the findings of this study we propose seven wetland management

recommendations and needs for further data. One, preserve the unique and regionally rare

Delmarva Bays remaining in the watershed. Two, incorporate wetland restoration and

preservation into community development and urban revitalization plans. Three, explore options

for beneficially re-using dredged sediment for wetland creation and management. Four,

encourage living shorelines and other natural methods as an alternative to bulkheads and rip-rap

for shoreline stabilization. Five, educate landowners on the benefits of maintaining natural

buffers along surface waters and wetlands, and incentivize landowners who preserve extensive

buffers. Six, control the extent and spread of the invasive common reed (Phragmites australis)

through state- and federally-funded programs. Seven, update tidal wetland regulatory maps

using current aerial photography and georeferencing tools to aid landowners and regulators.


INTRODUCTION

The Christina River drains

land in Delaware, Maryland, and

Pennsylvania and the watershed is

covered by various land-use types.

Wetlands in the Christiana River

Watershed provide many benefits

to people, support natural

processes, and provide habitats that

are an integral part of the

landscape. Wetlands act as the

transition between terrestrial and

aquatic habitats and are one of the

most productive ecosystems in the

world. Wetlands provide multiple

ecosystem services including

minimizing flooding from storms

by storing excess water, controlling

erosion through vegetation

stabilization, and improving water quality by removing excess nutrient runoff and pollutants

from non-point sources. Sediment loads may increase with agricultural practices, land clearing,

construction, and bank erosion. Wetlands throughout the watershed serve as a buffer by

removing and retaining suspended sediment from waters before they enter tidal and nontidal

waterways. They also have substantial cultural and economic value as a source of recreation

(e.g. hunting, fishing, birding) and livelihood (e.g. fishing, crabbing, fur-bearer trapping). Tidal

wetlands are biologically rich habitats and are a critical resource for migrating shorebirds and

wintering waterfowl, and serve as

nurseries for commercial fish and

shellfish species. Freshwater

wetlands process and funnel

ground and surface waters into our

waterways, and provide wildlife

habitat for a wide array of species.

Available data suggest that these

wetlands continue to be lost and

threatened by continued

development and conversion,

degradation, sea level rise, sudden

marsh dieback and a host of other

factors. From 1996-2006, the

estimated loss of tidal wetlands

across the Delaware Estuary was

Non-tidal riverine wetland in the Christina River Watershed.

Non-tidal flat wetland in the Christina River Watershed.


Diagram of MACWA tiers

3% (Partnership for the Delaware Estuary 2012).

Wetlands have a rich history across the region and their aesthetics have become a symbol

of the Mid-Atlantic Coast. The State of Delaware remains committed to improving wetlands

through protection and restoration efforts, education, and effective planning to ensure that

wetlands will continue to provide these services to the citizens of Delaware (DNREC 2008). In

addition to assessing changes in wetland acreage over time, monitoring wetland condition and

functional capacity is necessary to guide management and protection efforts. The Delaware

Department of Natural Resources and Environmental Control (DNREC) has developed a wetland

assessment and monitoring program to evaluate the health of wetlands. DNREC is also part of

the Mid-Atlantic Coastal Wetlands Assessment (MACWA) program, a larger collaborative effort

with the Partnership for the Delaware Estuary and Drexel University, to study wetland health

throughout the Delaware Estuary. Evaluating wetland health, or condition, and documenting the

stressors that are degrading wetlands and preventing them from working at their full potential on

a watershed scale provides useful information that watershed organizations, state planning and

regulatory agencies, and other stakeholders can use to improve wetland restoration and

protection efforts. Protection efforts through acquisition or easements can be directed towards

wetland types in good condition, allowing restoration efforts to target altered and degraded

wetland types to increase functions and services.

Wetland assessment information identifies specific

stressors that are impacting wetlands, and can

direct restoration projects and set priorities.

The MACWA consists of a 4-tiered

strategy to provide rigorous, comparable data

across the Mid-Atlantic region: examining

wetlands from the landscape level to site-specific

studies. Two of these four tiers consist of active

wetland monitoring—the Rapid Assessment

Method (RAM, Tier 2) and the Site Specific

Intensive Monitoring (SSIM, Tier 4). Tier 1

consists of satellite imagery or landscape analyses,

and Tier 3 is cross over studies between various

tiers.

DNREC and its partners have developed, and continue to refine, scientifically valid

methods to assess the condition of wetlands on a watershed scale. These methods are used to

generate an overall evaluation of the ambient condition of wetlands in a watershed, as well as to

identify common stressors by wetland type. In this report, we review the changes in wetland

acreage, highlight potential changes in wetland function, summarize the condition of tidal and

freshwater wetlands, identify common stressors degrading wetlands, provide recommendations

for improving the wetlands of the Christina River watershed, and introduce preliminary results

from long-term monitoring sites in the Christina River watershed.


WATERSHED OVERVIEW

The Christina River watershed is primarily

located in New Castle County, Delaware with the

upper headwater reaches extending into

northeastern Cecil County, Maryland and southern

Chester County, Pennsylvania (Figure 1). The

Christina River watershed is approximately 78

square miles (20,000 ha) in size and is primarily

urban and suburban land-use with isolated areas of

forest and agriculture. The Christina River

originates in Landenburg, PA and flows 35 miles

(55 km) eastward through Newark, Christiana, and

Newport, DE before emptying into the Delaware

River through the Port of Wilmington. In addition

to the main branch of the Christina River, the

watershed also includes the Muddy Run and Little

Mill Creek subwatersheds.

The Christina River watershed is bordered

by the small Army Creek, Red Lion Creek, Dragon

Run Creek, and Chesapeake & Delaware Canal

watersheds of the Delaware River Basin to the

south. To the north of the watershed are the White

Clay Creek, Red Clay Creek, Brandywine Creek,

and Shellpot Creek watersheds which also drain

into the Christina River and are collectively form

the Christina Basin in the Piedmont region. The

watershed is bound to the west by the Elk River

watershed of the Chesapeake Bay Basin.

2.1 Geology and Hydrogeomorphology

The Christina River watershed lies primarily within the Atlantic Coastal Plain

physiographic province with portions of the upper Christina River and Little Mill Creek

extending north into the Appalachian Piedmont physiographic province. The boundary between

these two provinces, known as the Fall Line, is located just north of the Interstate 95 corridor.

The hills of the Piedmont are formed by remnant metamorphic rocks from the Appalachian

Mountains, which are overlain by coastal and marine sediments forming the Coastal Plain (Plank

and Schenck 1998). Groundwater for the Christina River watershed is supported by fractures in

the crystalline bedrock of the Piedmont and pore spaces within unconsolidated sedimentary

deposits of the Coastal Plain (Hodges 1984).

Figure 1. Location of the Christina River

Watershed and the major basins of Delaware.

Watersheds at the Hydrologic Unit Code 10

scale are outlined in gray.


Hydrogeomorphology differs considerably between the Piedmont and Coastal Plain

physiographic provinces due to topography, geology, and soil characteristics. Wetlands in the

rolling hills of the Piedmont are primarily confined to riparian floodplains and few, isolated

depressions within the landscape. The Atlantic Coastal Plain portion of the Christina River

watershed can be further divided into two distinct regions: the inner coastal plain, and

beaches/tidal marshes. When compared to the rest of Delmarva’s Coastal Plain, the inner coastal

plain region has significant topographic relief which is typified by fewer headwater flat wetlands

with more incised stream channels (Fretwell et al. 1996). Tidal wetlands can be found along the

Christina River, from DE Route 1 east to the Delaware River.

2.2 Watershed History and Land-use

The first permanent European settlement in Delaware was established by the Swedes in

1638 along the Christina River after the failed settlement in Lewes in 1631. Shortly after the

establishment of Fort Christina, European settlers began altering the landscape significantly with

dikes and impoundments to reclaim land for agriculture (Phillipp 1995; Figure 2). Wetlands

Figure 2. Land cover of the Christina River watershed based on 2005 (PA), 2007 (DE),

and 2010 (MD) Land-use/Land-cover datasets.


throughout the region were ditched and filled to allow for transportation corridors and growing

populations. During this period of reclamation, Phillipp (1995) estimated that nearly the entire

tidal reach of the Christina River was diked and over 2,000 acres of tidal marshes were converted

to upland. Marshes adjacent to navigational channels were filled and used as disposal sites for

dredged material throughout the 1800s which allowed for the establishment and expansion of

industries in Wilmington and northern Delaware. Dike maintenance declined during the Great

Depression and World War II resulting in many of these systems falling into disrepair and

ultimately returning to tidal marshes (Sebold 1992).

The main channel of the Christina River has also undergone significant modifications.

The Christina River’s confluence with the Delaware River has been channelized and dredged to

accommodate cargo traffic, and a majority of the shoreline in Wilmington is armored with

bulkheads and rip-rap revetments. One of the most notable impacts in recent history to the

River, and surrounding tidal marshes, occurred with the construction of Interstate 95 in the

1960s. To create a direct route from Baltimore to Philadelphia, the Delaware Turnpike portion

of the highway was built just south of the Fall Line along the Christina River floodplain. The

alignment of the highway required a segment of the Christina River near Newport to be

redirected 5 km northwest of its original location (Figure 3). Churchman’s Marsh was also

impounded during construction and converted to open-water. During construction over 400

hectares (1,000 acres) of tidal marsh were filled or otherwise impacted, primarily in

Churchman’s Marsh and Newport Marsh (Phillipp 1995).

Pre-construction (1961) Current alignment (2012)

Figure 3. Pre- and post-construction alignment of the Christina River following the completion of Interstate

95. The pre-construction channel boundary is outlined in orange, and the current alignment southeast of

Newport is outlined in red.

Coastal industries in Wilmington flourished during the Industrial Revolution and supplied

much of the nation with goods during the Civil War and World War I. In 1913, planning and

construction began for the Port of Wilmington, located at the mouth of the Christina River. The

first marine terminal was completed in 1923 which opened the port to international trade. The

port expanded considerably over the following decades to meet the needs of various companies,

Churchman’s

Marsh


including Volkswagon and Del Monte (Baumbach et al. 2013). Businesses in Wilmington’s

downtown and Riverfront areas are significant contributors to the state’s economy today,

including DuPont, Gore, and many major financial institutions. Based on the most recent 2010

census, 186,680 people reside in the Christina River watershed, which grew by 20,245 people

since 2000 (Baumbach et al. 2013).

Table 1. Land use cover in the Christina River watershed based on 2005 (PA), 2007

(DE), and 2010 (MD) Land-use/Land-cover datasets.

Land-use affects the health of wetlands directly through filling and conversion to other

land uses, or indirectly via runoff from intensive land cover. The Christina River watershed is

currently among the most urbanized in Delaware, with over half of the watershed in residential

or industrial development (Figure 2; Table 1). Extensive impervious surface cover and poorly-

managed stormwater causes stream destabilization and soil erosion, while reducing groundwater

recharge potential. Agricultural production is rare and found primarily along the western edge of

the watershed in Maryland and Pennsylvania. Few large tracts of forest remain in the watershed,

most notably being Iron Hill Park and Sunset Lake. Transitional land cover in the watershed

encompasses land that has been cleared for development or areas that are frequently filled,

including landfills.

Environmental contamination is a significant issue in the Christina River watershed, with

four Superfund sites in the watershed and dozens of Brownfield sites in Wilmington.

Sedimentation and runoff from impervious surfaces (including oil, salt, and heavy metals) all

contribute to water quality concerns. The Christina River and its major tributaries exceed the

Total Maximum Daily Load of pollutants and are listed as impaired due to nutrients from

nonpoint source runoff and adjacent Superfund sites (USEPA 2006).

2.3 Wetland Resources

Wetlands, and the ecosystem services they provide, are crucial for the health of the

Christina River watershed. Economic benefits from consumptive wetland products (fish,

wildlife, timber, etc.) and the ecosystem services from water resources within the Christina River

watershed exceeds $7 billion annually (Baumbach et al. 2013). Within this urbanized watershed,

tidal and non-tidal wetlands are imperative for improving water quality, attenuating floodwaters,

and providing habitats for wildlife. Many species of amphibians, reptiles, waterfowl, mammals,

and insects rely on wetlands during various stages of their lives. Wetlands maintain and improve

water quality by trapping sediments, nutrients, heavy metals, and pathogens, which benefits

surface water and groundwater supplies. During storm events, tidal wetlands along the Christina

Land Use Category Percent Coverage

Developed 60

Scrub-Shrub and Forest 20

Agriculture 11

Wetlands 5

Open Water 2

Transitional 2


River act as a buffer by absorbing wave energy and storing excess floodwaters to protect coastal

businesses and residential properties. Non-tidal wetlands in urbanized areas of the watershed

also act as reservoirs during storm events protecting properties downstream. Wetlands are also

valuable recreational and educational resources, and downtown Wilmington is home to one of

the nation’s first urban wildlife refuges, the Russell Peterson Wildlife Refuge, located adjacent to

the Christina River. Civic groups in downtown Wilmington and conservation partners are also

experimenting with incorporating wetland restoration into a community revitalization plan to

remedy chronic flooding in the neighborhood of Southbridge.

Table 2. Wetland acreage and proportion for each hydrogeomorphic wetland type in the Christina River

Watershed.

Based on National Wetland Inventory (NWI) maps and Delaware’s State Wetland

Mapping Project (SWMP) maps, wetlands cover approximately 10% of the Christina River

watershed. Wetland inventory

maps are created by digitizing

orthophotos and supplemented with

historical aerial imagery and soil,

topography, and hydrology

datasets. The wetland acreage

identified using this mapping

criteria is 5% greater than the

wetland acreage identified by

coarser land-use/land-cover

datasets and reflects differences in

mapping standards and data

resolution. Non-tidal flats and

riverines are the most common

wetland type found in the Christina

River watershed (Table 2).

Tidal wetlands along the

Christina River extend from its

confluence with the Delaware

River west to Delaware Route 1 in

Christiana (Figure 4). A majority

of the non-tidal riverine wetlands

are found south of Interstate 95

Wetland Type Hectares (Acres) Proportion

Estuarine 395 (977) 19

Non-tidal Flat 815 (2,014) 40

Non-tidal Riverine 481 (1,191) 23

Non-tidal Depression 330 (817) 16

Non-tidal Lacustrine 35 (87) 2

Total 2,058 (5,085)

Figure 4. Distribution of wetlands in the Christina River

watershed, based on 2007 mapping.


along much of the Christina River and its tributaries. Non-tidal flat wetlands are also found

primarily south of Interstate 95 in southern Newark within forested headwaters areas. Natural

and man-made depressions make up a significant portion of the wetland acreage and can be

found throughout the watershed. Of particular note are Delmarva Bays, one of the region’s most

unique and irreplaceable wetland types. These isolated depressions are shallow, seasonally

flooded systems that support an abundance of rare plants and animals. Approximately 30 ha (73

ac) of Delmarva Bays remain in the Christina River watershed, most notably in the towns of

Brookside and Bear, Delaware. Lacustrine fringe wetlands (not pictured) are uncommon in the

Christina River watershed and are found exclusively along Beck’s Pond and Sunset Lake in

Bear, Delaware. These emergent systems differ from other non-tidal wetlands in the watershed

in that they are influenced by water levels in the lakes and can be subject to wind-driven wave

energy.


SECTION 1: CONDITION OF WETLANDS IN THE CHRISTINA RIVER

WATERSHED

METHODS

We documented the distribution of wetlands within the Christina River watershed and

estimated the number of wetlands that have been lost, both recently and historically. Wetland

condition assessments were completed in tidal and non-tidal wetlands in the Christina River

watershed during the summer of 2011. We used a probabilistic survey approach to assess

wetlands on both private and public property throughout the watershed. Tidal wetlands were

assessed using the Mid-Atlantic Tidal Rapid Assessment Version 3.0 (MidTRAM; Jacobs et al.

2010), and non-tidal wetlands were evaluated with the Delaware Rapid Assessment Protocol

Version 6.0 (DERAP; Jacobs 2010).

3.1 Changes to Wetland Acreage

We used Delaware wetland maps to determine the current distribution of wetlands across

the Christina River watershed, as well as where wetland loss has occurred in recent decades and

since colonization. Historic wetland acreage was estimated using a combination of current U.S.

Department of Agriculture soil maps and historic soil survey maps from 1915. These maps are

based on soil indicators such as drainage class, landform, and water flow. Hydric soils occurring

in areas that are currently not classified as wetlands due to significant human impacts, either

through urbanization, land clearing, or hydrologic alterations, are assumed to be historic

wetlands that have been lost. Recent losses are classified as wetlands converted during the 15-

year period of 1992 and 2007. Current acreage represents wetlands that were mapped in 2007

during Delaware’s most recent wetland mapping effort (State of Delaware 2007). Recent trends

in wetland acreage are classified as wetlands lost, created, or otherwise changed since 1992

(State of Delaware 1994).

3.2 Field Site Selection

Statistical survey methods developed by the U.S. Environmental Protection Agency’s

Ecological Monitoring and Assessment Program (EMAP) are used to extrapolate results from

random wetland sites to the condition of wetlands throughout the watershed. EMAP in

Corvallis, Oregon assisted with selecting 250 potential sample sites in estuarine intertidal

emergent wetlands and 500 potential sample sites in non-tidal wetlands using a generalized

random tessellation stratified design (Stevens and Olsen 1999, 2000). A target population was

created from all vegetated wetlands from the 2007 state wetland maps. Study sites were

randomly chosen points within mapped wetlands, with each point having an equal probability of

being selected. Sites were selected and sampled in numeric order as dictated by the EMAP

design - lowest to highest. Sites were only excluded from sampling if permission for access was

denied, the site was inaccessible, the site was of the wrong wetland classification, or if the site


was upland. Our goal was to sample 30 tidal sites and 30 non-tidal sites in each

hydrogeomorphic class (riverine, flats, and depression).

3.3 Data Collection

3.3.1 Landowner Contact and Site Access

We obtained landowner permission prior to assessing and sampling all sites. We

identified landowners using county tax records and mailed each landowner a post card providing

a brief description of our study goals, sampling techniques, and contact information. If a contact

number was available we followed the mailings with a phone call to discuss the site visit and

secure permission. If permission was denied the site was dropped and not visited. Sites were

deemed inaccessible if a landowner could not be identified or if the site was unsafe to visit.

3.3.2 Assessing Tidal Wetlands

We evaluated the condition of tidal wetlands using the MidTRAM protocol. MidTRAM

was designed and calibrated to assess polyhaline and mesohaline estuarine tidal wetlands and

developed with pilot data from Delaware, Maryland, and Virginia. MidTRAM was created by

adapting the New England Rapid Assessment Method (NERAM; Carullo et al. 2007) and the

California Rapid Assessment Method (CRAM; Collins et al. 2008) and consists of 14 scored

metrics that represent the condition of the wetland buffer, hydrology, and habitat characteristics

(Table 3). MidTRAM uses a

combination of qualitative evaluation

and quantitative sampling to record the

presence and severity of stressors in

the field or in the office using maps

and digital orthophotos.

MidTRAM was completed at

the first 30 random points that we

could access, and which met our

criteria of being of an estuarine

intertidal emergent wetland. Prior to

field assessments we produced site

maps and calculated buffer metrics

using ArcMap GIS software (ESRI,

Redlands, CA, USA). The attributes

measured included buffer width,

surrounding development, percent of

assessment area with a 5 m buffer, 250

m landscape condition, and barriers to

landward migration (Table 3). All

metrics measured in the office were

field verified to confirm accuracy.

Figure 5. Standard assessment area, subplot locations, and

buffer used to collect data for the Mid-Atlantic Tidal Rapid

Assessment Method Version 3.0.


We navigated to the EMAP points with a handheld GPS unit and established an

assessment area (AA) as a 50 m radius circle (0.78 ha) centered on each random point (Figure 5).

If a 50 m radius circle went beyond the wetland into upland or open water, we moved the circle

the least distance necessary (up to 50 m) or changed the AA to a rectangle of equal area to have

the entire AA within the wetland. We defined the AA buffer area as a 250 m radius area around

the AA.

Once the AA was established, eight 1 m2 subplots were placed along two perpendicular

100 m transects that bisected the AA. These subplots were used to measure horizontal vegetative

obstruction and soil bearing capacity (Table 3). We oriented one transect perpendicular to the

nearest source of open water (>30 m wide) and the other was perpendicular to the first. We

placed subplots 25 m and 50 m from the center of the AA along each transect. Subplots were

numbered clockwise starting with the plot 25 m from the AA center point, followed by the 50 m

one towards open water (Figure 5). If a subplot fell in a habitat type or patch that was not

characteristic of the site (e.g. in a ditch) we moved it along the transect to the nearest site

representative of the site location.

We completed all metrics within the AA via visual inspection during the field visit, with

the exception of horizontal vegetative obstruction and soil bearing capacity. Horizontal

vegetative obstruction was quantified at subplots 1, 3, 5, and 7 with a 1 m profile board, divided

into decimeters. With the profile board held at 0.25 m, 0.5 m, and 0.75 m above the wetland

surface the observer stood 4 m away from the profile board, and directly counted the number of

decimeter segments visible through the vegetation at eye level with the profile board. We

summed the 3 profile board readings for each subplot and recorded the average over the 4

subplots. We measured soil bearing capacity using a slide hammer technique on a random spot

in each subplot. To take the measurement, we raised the slide hammer and released it 5 times to

exert a consistent force on the soil surface. We subtracted the final depth below the marsh

surface of the bottom of the slide hammer from the initial depth to get the change in depth due to

the total force. Each metric was scored a 3, 6, 9, or 12, based on the narrative or numeric criteria

in the protocol.

Table 3. Metrics measured with the Mid-Atlantic Tidal Rapid Method Version 3.0.

Attribute

Group Metric Name Description

Measured

in AA or

Buffer

Qualitative or

Quantitative

Buffer/Landscape

Percent of AA

Perimeter with 5m-

Buffer

Percent of AA perimeter

that has at least 5m of

natural or semi-natural

condition land cover

Buffer Quantitative

(Office)

Buffer/Landscape Average Buffer

Width

The average buffer width

surrounding the AA that

is in natural or semi-

natural condition

Buffer Quantitative

(Office)


Table 3, continued:

Attribute

Group Metric Name Description

Measured

in AA or

Buffer

Qualitative or

Quantitative

Buffer/Landscape Surrounding

Development

Percent of developed

land within 250m from

the edge of the AA Buffer

Quantitative

(Office/Field)

Buffer/Landscape 250m Landscape

Condition

Condition of surrounding

landscape based on

vegetation, soil

compaction, and human

visitation within 250m

Buffer Quantitative

(Office/Field)

Buffer/Landscape Barriers to

Landward Migration

Percent of landward

perimeter of marsh

within 250m with

physical barriers

preventing marsh

migration inland

Buffer Quantitative

(Office/Field)

Hydrology Fill &

Fragmentation

The presence of fill or

marsh fragmentation

from anthropogenic

sources in the AA

AA Qualitative

(Field)

Hydrology Diking/Restriction

The presence of dikes or

other restrictions altering

the natural hydrology of

the wetland

AA and

Buffer

Qualitative

(Field)

Hydrology Point Sources

The presence of

localized sources of

pollution

AA and

Buffer

Qualitative

(Field)

Habitat Bearing Capacity Soil resistance using a

slide hammer AA subplots Quantitative

Field)

Habitat

Horizontal

Vegetative

Obstruction

The amount of visual

obstruction due to

vegetation AA subplots

Qualitative

(Field)

Habitat Number of Plant

Layers

Number of plant layers

in AA based on plant

height AA

Qualitative

(Field)

Habitat

Percent Co-

dominant Invasive

Species

Percent of co-dominant

species that are invasive

in the AA AA

Qualitative

(Field)

Habitat Percent Invasive Percent cover of invasive

species in the AA AA

Qualitative

(Field)

The average field time to sample each site was 2 h, with an average of 0.5 h needed to

complete computer-based metrics. Following field assessments, sites are assigned a Qualitative

Disturbance Rating from 1 (least disturbed) to 6 (most disturbed) using best professional


judgment (category descriptions can be found in Appendix A). Detailed instructions for using

MidTRAM are provided in the protocol (Jacobs et al. 2010).

3.3.3 Assessing Non-tidal Wetland Condition

DERAP is used to assess the condition of wetlands based on the presence and intensity of

stressors related to habitat, hydrology, and buffer elements. DERAP scores are calibrated,

separately for each HGM subclass, to comprehensive wetland condition data collected using the

Delaware Comprehensive Assessment Procedure (DECAP; Jacobs et al. 2009). DERAP was

completed at 32 non-tidal flats, 40 non-tidal riverines, and 2 depressions in the Christina River

watershed.

We navigated to EMAP points

with a handheld GPS unit and established

an assessment area (AA) as a 40 m radius

circle (0.5 ha) centered on each random

point (Figure 6). If the 40 m radius circle

extended beyond the wetland edge into

upland or open water, we moved the AA

the least distance necessary (up to 40 m)

or changed to a rectangle of equal area in

order to stay within the wetland. The

entire AA was explored and evidence of

wetland stressors were documented

(Table 4). Current and historic aerial

photos were used to determine forest

activity and buffer stressors and verified

in the field. Similar to MidTRAM, field

investigators assign the wetland a

Qualitative Disturbance Rating from 1

(least disturbed) to 6 (most disturbed;

Appendix A).

Table 4. Metrics measured with the Delaware Rapid Assessment Procedure Version 6.0.

Attribute Group Metric Name Description

Measured

in AA or

Buffer

Habitat Dominant Forest

Age

Estimated age of forest cover

class AA

Habitat Forest Harvesting

within 50 Years

Presence and intensity of

selective cutting or clear cutting

within 50 years

AA

Figure 6. Standard assessment area and buffer used to

collect data for the Delaware Rapid Assessment Procedure

Version 6.0.


Table 4, continued:


Measured

in AA or

Buffer

Habitat Forest Management

Conversion to pine plantation

or evidence of chemical

defoliation

AA

Habitat Vegetation

Alteration

Mowing, farming, livestock

grazing, or lands otherwise

cleared and not recovering AA

Habitat Presence of Invasive

Species

Presence and abundance of

invasive plant cover AA

Habitat Excessive Herbivory

Evidence of herbivory or

infestation by pine bark beetle,

gypsy moth, deer, nutria, etc. AA

Habitat Increased Nutrients

Presence of dense algal mats or

the abundance of plants

indicative of increased nutrients AA

Habitat Roads

Non-elevated paths, elevated

dirt or gravel roads, or paved

roads AA

Hydrology Ditches (flats and

depressions only)

Depth and abundance of ditches

within and adjacent to the AA AA and Buffer

Hydrology Stream Alteration

(riverines only)

Evidence of stream

channelization or natural

channel incision AA

Hydrology Weir/Dam/Roads

Man-made structures impeding

the flow of water into our out of

the wetland AA and Buffer

Hydrology Stormwater Inputs

and Point Sources

Evidence of run-off from

intensive land use, point source

inputs, or sedimentation AA and Buffer

Hydrology Filling and/or

Excavation

Man-made fill material or the

excavation of material AA

Hydrology Microtopography

Alterations

Alterations to the natural soil

surface by forestry operations,

tire ruts, and soil subsidence

AA

Buffer Development Commercial or residential

development and infrastructure Buffer

Buffer Roads Dirt, gravel, or paved roads Buffer

Buffer Landfill/Waste

Disposal

Re-occurring municipal or

private waste disposal Buffer

Buffer Channelized

Streams or Ditches

Channelized streams or ditches

>0.6 m deep Buffer


Table 4, continued:


Measured

in AA or

Buffer

Buffer Row Crops, Nursery

Plants, Orchards

Agricultural land cover,

excluding forestry plantations Buffer

Buffer Poultry or Livestock

Operation

Poultry or livestock rearing

operations Buffer

Buffer Forest Harvesting in

Past 15 Years

Evidence of selective or clear

cutting within past 15 years Buffer

Buffer Golf Course Presence of a golf course Buffer

Buffer Mowed Area Any re-occurring activity that

inhibits natural succession Buffer

Buffer Sand/Gravel

Operation

Presence of sand or gravel

extraction operations Buffer

DERAP produces one overall wetland condition score based on the presence and

intensity of various stressors. The final score obtained by DERAP is supported by the intensive

DECAP Index of Wetland Condition. The DERAP model was developed using a process to

screen variables specific to each hydrogeomophic wetland class to select the most important

variables that would represent wetland condition based on over 250 wetland sites (see Sifneos et

al. 2010; Appendix B). Wetland stressors included in the DERAP model were selected using

step-wise multiple regression and Akaike’s Information Criteria (AIC) approach to develop the

best model that correlated to DECAP data without over-fitting the model to this specific dataset.

Therefore, certain wetland stressors are more important than other stressors, while some stressors

are not included in final site scores. Coefficients, or stressor weights, associated with each

stressor were assigned using multiple linear regression (Appendix C). The DERAP IWC score is

calculated by summing the stressor coefficients for each of the selected stressors that were

present and subtracting the sum from the linear regression intercept:

DERAP IWCFLATS = 95 - (∑stressor weights)

DERAP IWCRIVERINE = 91 - (∑stressor weights)

DERAP IWCDEPRESSION = 82 - (∑stressor weights)

For all wetland subclasses, 23 terms were selected to be included in the DERAP IWC

calculation: 7 habitat stressors, 6 hydrology stressors, and 10 landscape or buffer stressors

(Appendix C).

Example: Site D

Forested flat wetland with 25% of AA clear cut, 1-5% invasive plant cover, moderate

ditching, and commercial development in the buffer:

DERAP condition score = 95 – (19+0+10+3)

DERAP condition score = 63


3.4 Presenting Wetland Condition

We present our results at both the site- and population-level. We discuss site-level results

by summarizing the range of scores that we found in sampled sites (e.g. Habitat attribute scores

ranged from 68 to 98). Population level results are presented using weighted means and standard

deviations (e.g. Habitat for tidal wetlands averaged 87 ± 13) or weighted percentages (e.g. 20%

of riverine wetlands had channelization present). Population-level results have incorporated

weights based on the probabilistic design and correct for any bias due to sample sites that could

not be sampled and different rates of access on private and public lands to be able to extrapolate

to the total area of wetland in the watershed. The cumulative results represent the total area of

the respective wetland subclass for the entire watershed.

Table 5. Condition categories and breakpoint values for tidal and non-tidal wetlands in the Christina River

watershed as determined by wetland condition scores.

Sites in each HGM subclass were placed into 3 condition categories: Minimally stressed,

Moderately stressed, or Severely stressed (Table 5). Condition class breakpoints were

determined by applying a percentile calculation to the QDR’s and condition scores from sites in

several previously-assessed watersheds. Freshwater tidal wetland regional datasets included

combined MidTRAM data from Pennsylvania, New Jersey, and Delaware (n = 90), while non-

tidal regional datasets includes DERAP data from St. Jones, Murderkill, Inland Bays, and

Nanticoke watersheds (n = 160). Minimally stressed sites are those with a condition score in the

25th

percentile of sites assigned a QDR of 1 or 2. Severely stressed sites are those in the 75th

percentile of sites assigned a 5 or 6. Based on the three watersheds combined, the condition

breakpoints for non-tidal sites that we applied in the Christina River watershed are provided in

Table 5.

We used a cumulative distribution function (CDF) to display wetland condition on the

population level. A CDF is a visual tool to extrapolate assessment results to the entire

population and can be interpreted by drawing a horizontal line anywhere on the graph and

reading that as: ‘z’ proportion of the area of tidal wetlands in the watershed falls above (or

below) the score of ‘w’ for wetland condition. The advantage of these types of graphs is that

they can be interpreted based on individual user goals, and break points can be placed anywhere

on the graph to determine the percent of the population that is within the selected conditions. For

example, in Figure 7 roughly 40% of the wetland area scored above an 80 for wetland condition.

A CDF also highlights clumps or platueas where either a large or small portion of wetlands are in

Wetland Type Method Minimally or

Not Stressed

Moderately

Stressed

Severely

Stressed

Estuarine MIDTRAM ≥ 83 < 83 and ≥ 61 < 61

Non-tidal Riverine DERAP ≥ 85 < 85 and ≥ 47 < 47

Non-tidal Flats DERAP ≥ 88 < 88 and ≥ 65 < 65

Non-tidal Depression DERAP ≥ 73 < 73 and ≥ 53 < 53


similar condition. In the example, there is a condition plateau from 50 to approximately 75,

illustrating that only a small portion of the population had condition scores in this range.

Figure 7. An example CDF showing wetland condition. The red line is the population estimate. The orange

and green dashed lines show the breakpoints between condition categories.


RESULTS

4.1 Landscape Analysis of Changes in Wetland Acreage

Based on hydric soil mapping and evidence of historic wetland loss, wetlands formerly

covered an estimated 9,163 acres (3,708 hectares) of the Christina River watershed. Compared

to most recent wetland maps, this indicates a 46% loss of wetland acreage between the time of

settlement and 2007 (Figure 8). A majority of these losses have occurred in the headwaters in

southern Newark and along the Interstate 95 corridor.

Figure 8. Estimated historic and present wetland coverage in the Christina River watershed.


Despite strict zoning codes and open space requirements in New Castle County,

approximately 81 acres (33 hectares) of wetlands were lost in the Christina River watershed

between 1992 and 2007. Due to past land-use decisions and overdeveloped portions of New

Castle County, the County ratified the Unified Development Code (UDC) in 1997. The UDC

created stringent zoning specifications to guide development and protect the remaining natural

resources in the County, including preserving 100% of wetlands (Section 40.10.320). However,

wetland protection “may be reduced when a permit from the United States Army Corps of

Engineers is issued for filling or disturbance” (Section 40.10.320). A majority of the wetlands

lost during the 15-year period were non-tidal forested systems related to the construction of new

housing developments, widening roadways, and the realignment of Route 273 through Ogletown,

DE. The single largest wetland loss (33 acres) occurred along the Delaware Bay at a disposal

site for dredged material which was covered with hydrophytic vegetation, most likely common

reed (Phragmites australis). Comparisons between 1992 and 2007 wetland maps also revealed

156 acres (63 hectares) of mapped wetlands were created in the Christina River watershed.

However, this small increase in wetland acreage was due to construction stormwater retention

ponds or excavated basins which do not function as natural wetlands.

As a result of recent changes in wetland acreage, the wetland functions potentially

provided in the Christina River watershed have further been altered. A recent landscape-level

analysis of wetland function predicted that, as a result of wetland losses between 1992 and 2007,

the potential for existing wetlands to perform nutrient transformation, sediment retention, surface

water detention, and serve as wildlife habitat were reduced (Tiner 2011). The direct replacement

of natural wetlands with stormwater retention ponds can also negatively affect wildlife that

utilize these habitats for breeding, nesting, or foraging. In developed landscapes, unnatural

hydroperiods and the accumulation of contaminants in stormwater ponds can create ecological

traps for birds, reptiles, and amphibians (Brand et al. 2010).

4.2 Landowner Contact and Site Access

Figure 9. Ownership of sampled wetland sites in the Christina River watershed (left) and success rates for

sampling private wetland sites (right).


The majority of our sampled

sites were located on private property

(Figure 9). Almost every assessed

wetland was located in Delaware, with

only one wetland assessment

performed in Maryland and zero in

Pennsylvania (Figure 10). We were

granted permission to 32 of the 34

non-tidal flat wetlands we attempted to

access, of which 66% of the wetlands

were on private property and 34%

were on public property. We were

denied permission to one non-tidal

riverine site, while two other riverine

sites proved to be inaccessible. Of the

40 riverine wetlands that were

sampled, 58% were privately owned

and 42% were on public property. We

only visited two depression wetlands

in the Christina River watershed, with

both sites found on private property.

Tidal wetland assessments were

conducted in 30 estuarine wetlands,

though access was attempted at 40 sites. Six sites were dropped because landowners could not

be contacted. Records are not available to determine why the other four sites were dropped, so it

is unknown if these sites were upland, non-tidal, or if permission was denied. Of the 30

estuarine sites that were assessed, 30% were on public property and 70% were privately owned.

4.3 Condition of Tidal Wetlands

Tidal estuarine wetlands comprise 19% of the total wetland acreage in the Christina River

watershed and provide more ecosystems services than any other wetland type. These systems

are crucial for buffering storm surges and storing floodwaters, controlling erosion, and

improving water quality by sequestering sediments and other pollutants. Within Delaware, a

majority of tidal wetlands are fringing salt marshes with salinities between 5 and 30 ppt. Salt

marshes are extremely productive systems that contain few species capable of surviving in these

environments. Uncommon in Delaware are freshwater tidal wetlands which occur along the

uppermost reaches of tidal rivers and streams that are still influenced by lunar tides. In these

areas, salt water from the Atlantic Ocean is diluted by substantial upstream freshwater inputs and

maintains salt concentrations below 0.5 ppt. These wetlands can be dominated by trees, shrubs,

or herbs and are generally more diverse than typical salt marsh communities. Within the

Christina River watershed, all of the 30 MidTRAM assessment sites were freshwater tidal

marshes.

The average score for the Buffer attribute group was 49 ± 24, with attribute scores

ranging from 93 to 20 (Figure 11). Intensive land uses were found around a majority of the tidal

Figure 10. Location of wetland assessments performed in the

Christina River watershed in 2011.


Buffer

Hydrology

Habitat

0 20 40 60 80 100

Value

wetlands in the Christina River

watershed, with 97% of wetlands having

some degree of disturbance in the

surrounding buffer. Development covers

approximately 19% of the 250 m buffers

surrounding tidal wetlands in the

watershed, due largely to the density of

roadways and commercial properties

surrounding marshes. Furthermore,

barriers to landward migration occurred

along 64% of the wetland/upland

boundaries in the watershed. In

undisturbed landscapes, tidal wetlands

respond to rising sea levels by migrating

inland and converting uplands to wetland

ecosystems. In the Christina River

watershed, the ability for marsh

migration is obstructed by these

hardened structures.

Hydrology scores were marginally better than buffer scores, ranging from 100 to 17 and

averaging 73 ± 29 (Figure 11). Thirty percent of the tidal wetlands in the watershed received a

perfect 100 for the Hydrology attribute group. Evidence of historic diking, as well as recent road

construction, was found throughout the watershed and affected the hydrology of 60% of the tidal

wetlands. Pollution entering tidal wetlands was also pervasive in the Christina River watershed

due to the close proximity of residential and commercial development. Point-source discharges

into wetlands were typically pipes, culverts, or ditches originating from anthropogenic land uses

and were found in 40% of marshes. Ditching freshwater marshes is a less common practice than

ditching salt marshes, with only 7% of the tidal wetlands in the watershed containing low

ditching.

Marshes in the Christina watershed generally had a deep peat layer with an average depth

of the organic later at 28.0 ±12.6 cm. Although the marshes had a thick organic layer, the

composition was not firm as evidenced by low bearing capacity. Bearing capacity is inversely

associated with the distance traveled by the slide hammer and this distance was relatively high in

Christina marshes (mean = 4.9±2.0 cm).

Invasive plant species were abundant in the watershed and found in 63% of freshwater

marshes. An estimated 34% of the tidal wetland acreage in the watershed was covered by

invasive species. Common reed was the most common invasive in tidal wetlands, though purple

loosestrife (Lythrum salicaria), narrowleaf cattail (Typha angustifolia) and mile-a-minute weed

(Persicaria perfoliata) also contributed to the invasive plant cover. Soil bearing capacity depth,

which measures marsh stability and reflects bulk density and below-ground biomass, averaged

4.88 ± 2.03 cm, which is deeper than values found in salt marshes.

Figure 11. Box and whisker plot of buffer, hydrology, and

habitat attribute group scores from tidal wetlands in the

Christina River watershed.


The integrated final score of Christina marshes averaged 58.9 ± 16.6. The cumulative

distribution function graph for tidal wetlands in the Christina watershed (Fig. 12) represents the

condition of the entire population of tidal wetlands. Top percentiles to determining comparative

break points among all freshwater tidal marsh condition scores were: <60 severely stressed; 61-

82 moderately stressed; >83 minimally stressed. Based on this, 57% of the tidal wetlands in the

Christina watershed were highly stressed, approximately 30% were moderately stressed, and

17% were minimally or not stressed. This implies that more than 80% of the freshwater tidal

wetlands in Christina were in comparatively poor condition.

Figure 12. The cumulative distribution function for tidal wetlands in the Christina watershed. The orange

and green dashed lines signify the condition category breakpoints dividing severely stressed from moderately

and minimally stressed portions of the tidal wetland population.

MidTRAM data from the 30 tidal wetland assessment sites can be found in Appendix D.

4.4 Condition of Non-tidal Wetlands

4.4.1 Non-tidal Flat Wetland Condition

Flat wetlands total approximately 40% of the of the wetland acreage in the Christina

River Watershed, primarily in low-lying forested areas. A majority of the non-tidal flat wetlands

are found in the western half of the watershed within the Coastal Plain province. Sizable non-

tidal flat wetlands can be found in Newport, south of the Interstates 95 and 295 interchange in an

area that was historically covered with tidal wetlands.

0

10

20

30

40

50

60

70

80

90

100

20 30 40 50 60 70 80 90 100

% o

f Ti

dal

We

tlan

d P

op

ula

tio

n

Wetland Condition Score

Minimum Maximum

Moderate


Table 6. Composition of wetland condition classes (left) and the occurrence of common wetland stressors

(right) of non-tidal flat wetlands in the Christina River watershed.

The highest possible score for non-tidal flats using DERAP is 95, and condition scores in

the Christina River watershed ranged from 91 to 43, with an average of 70±16. Nearly all (85%)

of flat wetlands in the watershed at least moderately disturbed, while only 16% of the flats are in

minimally stressed condition (Table 6). Invasive plant species were found in 85% of the flat

wetlands, with 13% of flats dominated by invasives. Recent forest harvesting was documented

in half of the non-tidal flat wetlands in the watershed, with clear cutting occurring in 31% of

wetlands and selective thinning found in 19% of wetlands. Wetland draining was also pervasive

in the watershed, with ditches found in 44% of flat wetlands. As expected within a heavily

urbanized landscape, intensive land-uses were found in most of the buffers surrounding flat

wetlands. Impervious surface cover, such as paved roads and development, occurred in 91% of

the wetland buffers in the watershed. Regularly mowed and cleared areas were found in the

buffers of 56% of flat wetlands in the watershed, which were typically maintained right-of-ways

associated with high-voltage powerlines that traverse Bear and Newark.

Though intensive land-use was found in the most wetland buffers, non-stormwater point

source inputs were not observed in any flats in the watershed. Notable stormwater inputs were

found in 9% of flat wetlands, evident by wrack lines or stormwater drainage structures directly

emptying into the wetland. Unlike Delaware’s Kent and Sussex counties, forest conversion to

pine plantations is rare in New Castle County and was not found in any flat wetlands in the

watershed. Excessive herbivory was documented in one flat wetland (3% of the population) due

to heavy browsing by white-tailed deer (Odocoileus virginianus).

Proportion of Sites with Stressor Present:

Wetland Stressor

Minimally

Stressed

(n = 5)

Moderately

Stressed

(n = 14)

Severely

Stressed

(n = 13)

Forest clear cutting 0% 0% 77%

Invasive plants present 40% 86% 100%

Ditching in wetland 0% 43% 62%

Fill material in wetland 20% 43% 85%

Development in buffer 80% 79% 69%


The cumulative distribution function for flat wetlands in the Christina River watershed

shows a wetland population skewed towards lower condition classes (Figure 13). Approximately

900 acres of flat wetlands in the watershed are functioning in a severely stressed condition.

Generally, these wetlands have been extensively cleared or thinned and plant communities with

moderate to extensive coverage of invasive plant species. A majority of these sites have also

been ditched and partially filled, and have multiple stressors in the surrounding landscape. Only

200 acres of flat wetlands in the watershed are estimated to be in a minimally stressed state.

These wetlands have a low occurrence of invasive plants and selective forest thinning, no

ditches, little to no fill material, and relatively intact buffers. The DERAP stressor checklist

from the 32 flat wetland assessment sites in the Christina River watershed are provided in

Appendix E.

Figure 13. Cumulative distribution function for non-tidal flat wetlands in the Christina River watershed.

Condition scores for the wetland population are represented as the red line with 95% confidence intervals

(gray dashed lines). The orange and green dashed lines designate condition category breakpoints dividing

severely stressed, moderately stressed, and minimally stressed wetlands.


4.4.2 Non-tidal Riverine Wetland Condition

Riverine wetlands in the Christina River watershed are associated with floodplains of the

Christina River and its tributaries. These wetlands cover approximately 481 hectares (1,191

acres) which is 23% of the total wetlands acreage in the watershed. Riverine wetlands act as

buffers between streams and adjacent land use and are valued for water quality maintenance

through sediment retention and nutrient uptake. They are also vital for flood abatement by

allowing for overbank flood water storage during storm events.

The maximum score possible for riverine wetlands using DERAP is 91, and riverine

wetland scores in the Christina River watershed ranged from 88 to 6, with an average of 53±22.

Only 8% of the riverine wetlands in the watershed were functioning in a minimally stressed

condition, while 40% of the wetlands were severely stressed (Table 7). A majority of the buffers

surrounding riverine wetlands in the watershed were significantly impacted, with 95% of

wetlands containing impervious surfaces in the buffer. Similar to flat wetlands in the watershed,

regularly mowed areas were also found in a majority (83%) of riverine wetland buffers. Ten

percent of the riverine wetlands also had reoccurring mowing within the wetland itself. As

development and land clearing occurs adjacent to wetlands, edge effects, such as increased

sunlight and wind penetration, have profound impacts on the remaining wetland habitat and

increase the likelihood of colonization by invasive species. As expected, 95% of the riverine

wetlands in this watershed contained invasive plant species, with 48% of riverine wetlands

dominated by invasive plant cover. Stream channelization or natural channel incision was the

most common impact to wetland hydrology, occurring in 60% of riverine systems. Fill material,

such as spoil piles and yard waste, was also found in 53% of riverine wetlands. Forestry activity

was less common in riverine wetlands than flats in the Christina River watershed, though 25% of

riverine sites were selectively thinned or clear-cut.

Table 7. Composition of wetland condition classes (left) and the occurrence of common wetland stressors

(right) of non-tidal riverine wetlands in the Christina River watershed.

Proportion of Sites with Stressor Present:

Wetland Stressor

Minimally

Stressed

(n = 3)

Moderately

Stressed

(n = 21)

Severely

Stressed

(n = 16)

Forestry activity 0% 10% 50%

Dominated by invasive spp. 0% 38% 69%

Stream channelized/incised 0% 38% 100%

Fill material in wetland 0% 38% 81%

Development in buffer 67% 57% 88%

Evidence of stormwater inputs were more common in riverine wetlands than flats in the

watershed. Excessive sedimentation was found in 8% of riverine wetlands, and stormwater

inputs in another 10% of wetlands. Generally, elevated roads and all-terrain vehicle trails are less

common in riverine wetlands due to the saturated soil conditions, though these features were


nearly as common in riverine wetlands (28%) as they were in flats (31%) within the Christina

River watershed. Excessive herbivory was slightly more common in riverine wetlands (5%) due

to forest clearing and significant impounding by North American beavers (Castor canadensis).

Fewer than 50 acres of minimally stressed riverine wetlands remain in the Christina River

watershed based on cumulative distribution function estimates (Figure 14). These wetlands were

absent of forest cutting, stream alterations, and fill material. However, these wetlands vary in the

amount of invasive plant species present and the intensity of impacts to the surrounding buffer.

Conversely, approximately 500 acres of riverine wetlands in the Christina River watershed are

severely stressed. Invasive plant species were found in each of the severely stressed riverines,

with invasives as the dominant plant cover in 69% of these wetlands. Stream morphology and

wetland hydrology is also significantly altered, including channelization or incision (100% of

riverine wetlands), fill material (81%), and constricted or impounded streamflow (63%).

Impervious landcover was also found in the buffers around each of the severely stressed riverine

wetlands. The DERAP stressor checklist from the 40 riverine wetland assessment sites in the

Christina River watershed are provided in Appendix F.

Figure 14. Cumulative distribution function for non-tidal riverine wetlands in the Christina River watershed.

Condition scores for the wetland population are represented as the red line with 95% confidence intervals

(gray dashed lines). The orange and green dashed lines designate condition category breakpoints dividing

severely stressed, moderately stressed, and minimally stressed wetlands.


4.4.3 Non-tidal Depression Wetland Condition

Approximately 330 hectares (817 acres) of depression wetlands are found in the Christina

River watershed. These wetlands naturally form in low-lying areas and topographical

depressions within the landscape. Most of the natural depressions in the Christina River

watershed occur in forested areas south of Newark, though they can be found throughout the

watershed. The acreage of depression wetlands in the watershed is inflated by a number of large

man-made impoundments within cloverleaf interchanges on Interstate 95 and a dredge disposal

area south of the Cherry Island landfill in Wilmington. Natural depressions in the watershed are

otherwise rare and only two depression wetlands were assessed, so conclusions on the condition

of depressions at the watershed-scale cannot accurately be drawn. The DERAP stressor checklist

from the two depression assessments can be found in Appendix G.

4.5 Overall Condition and Watershed Comparison

For an overall view of non-tidal wetland condition in the Christina River watershed, and

to compare to five recently assessed watersheds in southern Delaware, we created an overall

condition score weighted by the acreage of flat and riverine wetlands in each watershed.

Non-tidal wetlands in the Christina River watershed were in considerably worse

condition than wetlands in southern Delaware (Figure 15). The Christina River watershed

contained more severely stressed wetlands than any other watershed, as well as the fewest

proportion of minimally stressed wetlands.

Figure 15. Combined condition of non-tidal flat and riverine wetlands in the Christina River watershed,

compared to wetland condition in the St. Jones, Murderkill, Mispillion, Broadkill, and Inland Bays

watersheds.


MANAGEMENT RECOMMENDATIONS

Based on our study, we offer the following seven recommendations to improve ecosystems

provided by wetlands, guide wetland restoration and management efforts, identify additional

management needs, and encourage informed decisions concerning the future of wetland

resources in the Christina River watershed.

1. Preserve remaining Delmarva Bays. Coastal Plain Seasonal Ponds, also known as

Delmarva Bays, have been identified as a regionally-unique wetland type and are

considered irreplaceable and a significant component of Delaware’s natural heritage

(McAvoy and Clancy 1994). These wetlands contain unique hydrological and biological

characteristics that are imperative for the survival of many plants and animals in

Delaware. Many Delmarva Bays throughout the state have traditionally been ditched,

filled, or excavated and are exceedingly rare in Delaware, with only an estimated 73 acres

of Delmarva Bays remaining in the Christina River watershed. New Castle County’s

Unified Development Code preserves 100% of wetlands (Section 40.10.32) unless a

permit from the United States Army Corps of Engineers is issued for filling or

disturbance (Section 40.10.320), leaving Delmarva Bays vulnerable to impacts.

Protecting Delmarva Bays, and biologically-significant buffers, through easements and

planning will preserve these irreplaceable wetlands.

2. Incorporate wetland creation and restoration into urban planning. Many

neighborhoods in Wilmington are marred by chronic flooding and inadequate drainage

which stifles local economies. An example of utilizing the natural ecosystem services

provided by wetlands are found in the management plan developed for South Wilmington

and the neighborhood of Southbridge. This large-scale community revitalization plan is

held as a national model for incorporating wetland restoration to alleviate flooding,

improve water quality, and provide habitat for wildlife. The 27-acre restored wetland

will also serve as greenspace for the community and an educational resource for urban

school students. Many opportunities for wetland restoration and outreach can be found

along the Christina River and should be considered in land-use decisions.

3. Utilize clean dredged material for wetland creation. The mouth of the Christina River

and the navigational channel in the Delaware Bay is frequently dredged to accommodate

cargo ships reaching the Port of Wilmington. Traditionally, dredged materials are

disposed of in confined disposal facilities found along the Delaware Bay. Re-using

uncontaminated dredged material for wetland restoration and creation has been used

elsewhere in the United States and can be explored in the Christina River watershed.

Considerations must be made on the source of sediments used for restoration because a

number of areas in the Delaware River and Bay have elevated concentrations of

contaminants that would limit the feasibility of habitat restoration. In 2011 DNREC and

its conservation partners began a project applying a thin layer of dredged material to a

fragmenting salt marsh in Dagsboro to increase surface elevation and promote Spartina

altiniflora growth. Dredged sediment has also been beneficially re-used throughout the

country to create wetlands in areas that were previously open water. Opportunities for


wetland creation should be investigated in open water areas of Churchman’s Marsh and

around the confluence of the Brandywine River. The Delaware Estuary Regional

Sediment Management Plan Workgroup has developed documents outlining potential

uses of dredged sediments and special considerations.

4. Encourage alternative shoreline protection designs. Shorelines are dynamic systems

that respond to sediment supply and wave energy through erosion and accretion.

Shorelines along the Christina River are significantly altered by bulkheads, gabions,

revetments, and rip-rap which lack the capacity to respond to these natural processes. In

these areas lacking estuarine wetlands, shorelines have reduced capacity to buffer storm

surges, trap sediments, and provide habitat for wildlife due to the hardened shorelines.

Along shorelines with lower wave energy, alternative shoreline stabilization approaches

should be considered if erosion threatens public infrastructure. Living shorelines are a

natural alternative which utilizes coconut fiber logs and natural vegetation to anchor the

shoreline and prevent erosion. Shellfish or low-profile rock sills can also be used with

living shorelines to dissipate wave energy. In situations with greater energy or steeper

banks, timber cribbing and log revetments may be employed. The Delaware Estuary

Living Shoreline Initiative was developed to showcase natural alternative to protect

shorelines (for more information, see http://delawareestuary.org/Living_Shorelines).

5. Develop incentives and encourage maintaining natural buffers along riverine and

tidal wetlands. As sea levels rise and extreme storm events bring more flooding, the

importance of wetland buffers between water and upland is taking center stage. The need

exists to inform Delawareans on the importance of allowing tidal wetlands to migrate

inland unobstructed by roads, rip-rap and bulkheads. Sufficient buffers along streams

and rivers stabilize shorelines and improve water quality by trapping sediments and

pollutants before they reach surface waters. Landowners along riverine and estuarine

wetlands, as well as those directly abutting surface waters, should be educated about the

ecological and societal benefits that can be attained by preserving natural buffers. In

addition to awareness, an incentive program could attract an interest in maintaining larger

natural buffers between wetlands and development.

6. Control the extent and spread of the non-native, invasive common reed (Phragmites

australis). Invasive plants such as Phragmites are capable of spreading rapidly,

outcompeting native species, reducing plant diversity in undisturbed areas, and reducing

the success of other organisms by changing habitat structure and food availability. The

DNREC Phragmites Control Program in the Division of Fish and Wildlife has treated

more than 20,000 acres on private and public property since 1986. Without continued

support from state funds and federal State Wildlife Grant funds Phragmites will degrade

more wetlands. Phragmites was the most abundant invasive species in estuarine

wetlands in the Christina River watershed and a significant stressor in non-tidal flat and

riverine wetlands.

7. Update tidal wetland regulatory maps. In addition to improving the protection of

nontidal wetlands, it is prudent to maximize the authority that already exists within

DNREC. Tidal wetland impacts are regulated by the State of Delaware and permit

http://delawareestuary.org/Living_Shorelines

http://www.dnrec.delaware.gov/fw/dplap/services/Pages/DelawarePhragmitesControl.aspx


reviewers need accurate and recent wetland maps to guide wetland permitting. Currently

1988 wetland maps are used, which must be verified in person and are difficult to read.

Evidence of recent coastal development and inundation of coastal wetlands due to sea

level rise creates a greater need to adopt updated wetland maps as regulatory maps.


SECTION 2: ESTABLISHMENT OF A PERMANENT INTENSIVE

MONITORING STATION IN THE CHRISTINA RIVER WATERSHED

SUMMARY

In September of 2010, a permanent Site Specific Intensive Monitoring (SSIM) station

was established at Christina River, in South Wilmington, Delaware. This SSIM location is a part

of a larger monitoring scheme entitled the Mid-Atlantic Coastal Wetlands Assessment

(MACWA). The major goal of MACWA SSIM is to provide long term monitoring data to aid

land managers in the conservation and protection of wetlands, especially from the threats of sea

level rise due to climate change. Several permanent fixtures were installed at the Christina River

SSIM station including surface elevation tables (SETs) and permanent vegetation plots. These

fixtures were monitored from 2011 to 2013, along with a suite of other metrics including water

quality and plant biomass. Three years of data, however, is inadequate to make conclusions

about long-term trends. Yet, data reported here suggest that between 2011 and 2013, the marsh

platform had increased in elevation at SET benchmarks. These elevation changes are most likely

the result of subsurface swelling. Some disparate results were observed between SET benchmark

and plant community data; overall, however, these data suggested an increasing trend, but at

least 5 years of data is needed to determine the inherent natural variation. The next two sampling

years will be very important to confirming these trend, as well as making conclusions about other

cause and effect relationships that will be monitored at Christina River.

INTRODUCTION

To provide insight into cause and effect relationships in accordance to overall wetland

health, Site Specific Intensive Monitoring (SSIM) seeks to track changes in wetland composition

at fixed locations over time. As part of this effort, field methodologies consist of the installation

of permanent fixtures that aid in our ability to return to exact locations, and to monitor

characteristics of the marsh that are

usually difficult to study. These fixtures

include surface elevation tables (SET),

designed to track elevation changes due to

shrink and swell of the marsh platform;

nine permanent vegetation plots, designed

to observe changes in plant communities at

precise locations; and vegetation real time

kinetic (RTK) GPS transects (accurate ±2

cm), designed to track vegetation

distribution changes at specific elevations

(Figure 16). Along with monitoring these

fixed features, other metrics are monitored

which consist of additional physical,

chemical and biological variable, such as

Figure 16. Example of Site Specific Intensive Monitoring

sampling lay-out along the main waterway.


water quality and biomass measurements. These variables in association with permanent fixtures

are collectively referred to as a SSIM “station.” Understanding changing relationships among

key ecoregion features aids in our ability to decipher stressor-response relationships in wetlands

across SSIM stations in the estuary, and offer insights into how landscape and climatic changes

are affecting wetland health and condition.

METHODS

5.1 Components of Site Specific Intensive Monitoring Stations

5.1.1 Surface Elevation Tables (SETs) and Marker Horizons (MH)

On September

17, 2010, three deep

rod surface elevation

table benchmarks were

installed at the

Christina River study

site, which also marks

the establishment of

this SSIM station.

Surface elevation tables

(SETs) and feldspar

marker horizons (MHs;

3 per SET) were

installed in accordance

with protocol

established by Cahoon

(2002; Figure 17).

Marker horizons allow

us to discern surface

accretion, and are used

in conjunction with

SET heights to

ascertain causes of

marsh elevation

change. SET heights are the cumulative height change of 9 pins on a portable SET arm, which is

positioned on the benchmark. Accretion is measured as the distance from the top of the layer of

feldspar to the surface of marsh as seen from a core taken from a marker horizon plot. More

detailed methodologies can be found online at USGS Patuxent Wildlife Research Center. SET

readings were taken March 15, 2011 (baseline); September 1, 2011; June 13, 2012; September

12, 2012; and June 5, 2013. Accretion rates at the Christina River SSIM station were obtained

Figure 17. Site Specific Intensive Monitoring (SSIM) point locations at Christina

River, Wilmington, DE: permanent vegetation plots (PVs; groups of three) and

random edge plots (REs) distributed along shoreline of main water bodies.


on March 15, 2011 (baseline); June 13, 2012; September 12, 2012; June 5, 2013 and August 14,

2013. Our partners, the Academy of Natural Science of Drexel University, collected these data.

5.1.2 Soil Quality and Biomass

Three plots were established for biomass sample collection in both low marsh or nearest

distance to the main water body and high marsh or farthest distance from the main water body

~10 m landward of two of the three SETs at each monitoring station. Three plots were

established no less than 10 m from any SET-MH. For appropriate consistency and replication,

the three plots were placed in the same or similar plant communities to the SET-MH.

Aboveground biomass was harvested in 0.5 m2 quadrats for salt marshes or 1.0 m

2

quadrats for tidal fresh marshes by clipping all standing vegetation at the marsh surface.

Aboveground biomass was placed into labeled plastic bags and brought to the lab for processing.

Belowground biomass was collected in the center of this plot as a 15-cm diameter x 30-cm depth

soil core by pounding a PVC core barrel. The belowground biomass was washed over a 5 mm

mesh sieve then separated into live and dead material. All material was washed, dried, ground,

and measured for loss on ignition. Samples were stored for biochemical analysis if needed.

5.1.3 Vegetation Transects

Three transects were established that represent the gradient from the marsh edge to its

interior in the proximity of each of the SET and MH fixtures (Figure 2). A total of nine transects

were assessed by storing point data using Real Time Kinetic GPS, which collects precise

latitude, longitude, and elevation data. Dominant vegetation was recorded in association to these

data so that elevation changes can be correlated with changes in plant communities. Data were

collected July 13, 2011 and September 27, 2013. The Academy of Natural Science of Drexel

University also collected these data.

5.1.4 Vegetation Data in Fixed Plots

Nine 1 m2 permanent vegetation plots (PVs) and six ¼ m

2 random edge (REs) plots

(Figures 16 and 17) were assessed on August 8, 2011, September 20, 2012, and July 22, 2013.

The nine PVs are grouped transversely across the three transects, so that PVs are triplicates

within their proximity (“near”: PV numbers 1,2, 3, closest to mouth or water edge; “mid”: 4,5,6

and “far”: 7,8,9 furthest upstream or from water). Therefore, calculations represent the average

PV value for its group (near, mid or far). All plots were relocated using handheld GPS units, but

only PVs were marked with permanent PVC markers. Percent plant species cover, species

dominance, light obstruction, and average blade heights were recorded for all plots (MACWA

SSIM QAPP 2010). From these measurements, canopy closure (light in kfc/blade height in cm),

alpha diversity (Shannon-Weiner), and vegetation zone dominance scores (VZD; a novel method

for estimating flood frequency using plant community structure, see Appendix B) were

calculated.

A principal component analysis (PCA) was run on these metrics, as well as SET and

accretion data, which were introduced into the PCA as environmental covariants. PCA is a

multivariate comparison that allows us to discern differences between years among the suite of


SSIM variables since temporal data are currently too limited to perform robust linear regressions.

Each year, therefore, is compared to the others as an independent sample. From the PCA, an

ordination plot is generated. An ordination plot allows us to visualize correlations between

variables, and the strength of these relationships.

5.1.5 Water Quality

Five points were established along the nearest main channel or tidal creek for spot

measurements using an YSI and water collection. Tidal creek surface water samples were

collected, filtered, and analyzed for dissolved and particulate nutrients. Sampling and data

collection occurred approximately two hours after high tide (i.e., during ebb tide). One gallon

cubitainers were rinsed with site water and then filled at each of the five locations along the main

water body or tidal creek. Cubitainers were stored on ice in the dark while in the field. Water

samples were analyzed for total suspended solids, suspended Chlorophyll (fluorometric;

acidification method), dissolved ammonium + ammonia, dissolved nitrate + nitrite, soluble

reactive phosphorus, dissolved organic carbon, total nitrogen (TKN+dissolved nitrate+nitrite)

and total phosphorus. Our partners at the Academy of Natural Sciences of Drexel University

also collected these water quality data.

RESULTS

5.2.1 Surface Elevation Tables (SETs) and Marker Horizons (MH)

Average SET heights were converted into height change per SET (Table 8). Values per

SET were then averaged to give cumulative height change (Figure 5). A linear regressions of

these data per SET produces trend lines with equations of y(SET1 height) =0.0432x-1741.5,

y(SET2 height) = 0.0245x-988.25, y(SET3 height) =0.0227x-909.62 (Figure18). These produce

a yearly (slope * 365) increase of 15.6, 12.5, 8.2 mm/yr or an average of 12.1 mm/yr. Linear

models predict no significant differences from slopes of zero. Accretion rates (MHs) are

measured as sediment heights above feldspar marker, and then are converted to cumulative

changes in height (Table 9). Daily accretion rate is given by a linear regression (slope NS from

zero) of height change, where y(accretion height) =0.0035x-136.38, or an accretion rate of 1.27

mm/year (Figure 19). Shallow subsidence (MH minus SET) is -10.83, which is a cumulative

increase in elevation at SET benchmarks.

Table 8. Surface Elevation Table heights for Christina River. Data

are given as the difference in mean pin height between sampling

dates, error values are standard error.

Date SET 1 SET 2 SET 3

3/15/2011 0 (baseline) 0 (baseline) 0 (baseline)

9/1/2011 18.96 ± 5.15 36.26 ± 6.43 25.4 ± 6.42

6/13/2012 -9.00 ± 4.38 -9.08 ± 4.23 -1.97 ± 6.14

9/12/2012 15.33 ± 6.07 7.61 ± 2.72 16.1 ± 4.24

6/5/2013 -1.58 ± 15.52 10.67 ± 14.51 -21.0 ± 6.60


Table 9. Marker Horizon Heights for Christina River. Data are given as the mean

accretion height at sampling dates. Error values are standard error.

Date SET 1 SET 2 SET 3

3/15/2011 0 (baseline) 0 (baseline) 0 (baseline)

6/13/2012 24.5 ± 5.37 46.3 ± 0.440 23.6 ± 1.63

9/12/2012 20.6 ± 0.816 25.5 ± 1.5 27.3 ± 0.421

6/5/2013 27.7 ± 6.64 NA 29.1 ± 2.19

8/14/2013 41.1 ± 3.94 33.8 ± 3.92 39.11 ± 2.87

Figure 18. Cumulative accretion height change for Christina River. Heights are given as the difference in

mean accretion height of all SETs between sampling dates, error bars are standard error. Arrow marks the

approximate date of the landfall of Hurricane Sandy in New Jersey (October 29, 2012). Yearly SET height

changes (mm) are 15.6, 12.5, 8.2 for SET 1, 2, and 3, respectively, averaging 12.1 mm/yr. Linear models

predict NS from slope =0.

Figure 19. Cumulative accretion height change for Christina River. Heights are given as the difference in

mean accretion height of all SETs between sampling dates, error bars are standard error. Arrow marks the

approximate date of the landfall of Hurricane Sandy in New Jersey (October 29, 2012). Yearly accretion rate

is 1.27 mm/yr. Slope NS from zero

y = 0.0245x - 988.25

y = 0.0432x - 1741.5 y = 0.0227x - 909.62

0

10

20

30

40

50

60

70

Cu

mu

lati

ve H

eig

ht

Ch

ange

(m

m)

Date

SET 1 SET 2 SET 3 Linear (SET 1) Linear (SET 2) Linear (SET 3)

y = 0.0035x - 136.38 R² = 0.007

-20

-10

0

10

20

30

40

Cu

mu

lati

ve H

eig

ht

Ch

ange

(m

m)

Date

Δ Accretion (mm) Linear (Δ Accretion (mm))


9%

4%

11%

2%

2%

2% 70%

Impatiens capensis

Nuphar lutea

Peltandra virginica

Pontederia cordata

Sagittaria latifolia

Scirpus fluviatilis

Typha angustifolia

2%

8% 2% 2%

6% 2%

2% 2%

2% 6%

2% 2%

2% 2%

53%

2%

4%

Amaranthus cannabinusImpatiens capensismix Acorus calamus/Scirpus fluviatilismix Amaranthus cannabinus/Typha angustifolia/mix Impatiens capensis/Typha angustifoliamix Peltandra virginica/Polygonum punctatummix Polygonum arifolium/Polygonum punctatummix Polygonum punctatum /Zizania aquaticamix Sagittaria latifolia/Peltandra virginicamix Typha angustifolia/Impatiens capensisPeltandra virginicaPolygonum arifoliumPolygonum punctatumSagittaria latifoliaTypha angustifoliaUnidentified vine/ Gleditsia tricanthosZizania aquatica

5.2.2 Soil Quality and Biomass

Aboveground biomass collected > 10m landward of SETs 1 and 3 averaged 761.2 gm-2

in

2011; the corresponding belowground biomass averaged 900.73 gm-2

. Soil cores were collected

>10m landward of SETs 1and 3 on November 8, 2010, March 15 and September 1, 2011.

Averaged across time periods, surface (5cm depth) organic matter (OM) content averaged 23%.

Averaged between the two time periods, Chl concentrations at Christina were 14.7 µg g-1

.

5.2.3 Vegetation Transects

The average

orthoheight derived from

RTK GPS of all transects

at Christina River was

0.6530.015 m in 2011

and 0.5190.028 m in

2012 (t-test p<0.0001).

Vegetation transects

were designed to create

GIS contour elevation

maps which can be used

to perform spatial

analyses in GIS for plant

community distribution.

These maps and analyses

are not yet available as

models are currently

being constructed.

Narrow leaf cattail Typha angustifolia) was the most abundant species observed along these

transects. Percentages of species dominance along the transects are in Figure 20 and 21.

Figure 21. Percentages of dominant vegetation type over all three RTK GPS

transects in 2011.

Figure 20. Percentages of dominant vegetation type over all three RTK GPS transects in 2012.


5.2.4 Vegetation in Fixed Plots

The following tables (Tables 10-12) contain mean values for raw or calculated values

(i.e. percentages) for measurements taken at fixed vegetation plots, including faunal data.

Although three years of data are currently available for vegetation in fixed plots, more years of

collection are necessary to make robust conclusions about vegetation community trends at the

Christina River. Future analyses will include linear models of year to year changes in community

structures.

Table 10. Mean blade heights (cm) of first and second dominant species within near, mid, and far PVs.

Plot Area

Year All Species First Dominant Species Second Dominant Species

Mean (n=3) SE Species Mean SE Species Mean SE

near PVs

2011 212.60 7.41 Typha angustifolia 246.59 4.54 Peltandra virginica 121.73 4.88

2012 177.93 7.08 Typha angustifolia 181.09 7.09 Impatiens capensis 115.00 6.43


mid PVs


2012 103.09 7.99 Peltandra virginica 77.11 4.66 Impatiens capensis 80.12 4.38

2013 199.80 8.40 Typha angustifolia 261.55 4.71 Nuphar lutea 59.64 5.24

far PVs




all REs

2011 127.77 3.45 Nupar lutea 106.19 3.78 Pontedaria cordata 140.68 3.57

2012 77.81 2.62 Nuphar lutea 72.27 2.78 Polygonum sp. 77.46 3.44

2013 119.72 2.17 Nuphar lutea 119.75 2.27 Pontedaria cordata 131.47 5.84


0.00

0.50

1.00

1.50

2.00

2.50

2011 2012 2013

Near

Mid

Far

RE

Table 11. Percent of light penetration is given by light

intensity at the bottom of the canopy divided by the

light intensity above the canopy.

Table 12. Percentages of the first two most dominant species found in each plot, followed by the diversity

index and mean VZD score. Percentages may not equal 100% as species coverage in plot may be highly

overlapped or sparse.

Plot Area

Year

Dominant Species, Diversity and VZD

Dominant Species % Subdominant Species % Diversity VZD

near PVs

2011 Typha angustifolia 58.3% Lonicera sp. 40.0% 0.633 2.02

2012 Typha angustifolia 76.7% none 0.66 2.1

2013 Typha angustifolia 52.7% Impatiens capensis 31.5% 0.56 1.88

mid PVs

2011 Peltandra virginica 37.5% Typha angustifolia 35.0% 0.32 1.96

2012 Impatiens capensis 80.0% Nuphar lutea 20.0% 0.523 1.81

2013 Typha angustifolia 65.0% Nuphar lutea 40.0% 0.483 2.19

far PVs

2011 Typha angustifolia 63.3% Peltandra virginica 57.5% 0.69 2.08

2012 Typha angustifolia 47.5% Impatiens capensis 30.0% 0.44 1.98

2013 Typha angustifolia 52.5% Peltandra virginica 50.0% 0.55 2.14

all REs

2011 Nuphar lutea 61.3% Pontedaria cordata 40.0% 0.93 1.42

2012 Nuphar lutea 24.2% Pontedaria cordata 15.0% 0.87 0.71

2013 Nuphar lutea 55.2% none 0.89 0.99

A principal component analysis (PCA) was then run on the vegetation data, which

included group wise VZD score, canopy closure, blade height, canopy density and alpha

diversity constrained (i.e. treated as independent co-variables) by the environmental variables:

cumulative SET height and accretion rates. The red variable labels indicate the approximate

direction of each metric's increasing values. The blue arrows indicate the strength and direction

of the covariant environmental variables' with the main variables (red labels). Each polygon is

one sampling year, with near (A), mid (B), and far (C) PVs. Differences between years are

highlighted by the shape of each polygon, and the distance between them. The vectors indicate

Plot Area

Year Percent Light Penetration

All Species

Mean (n=3) SE

near PVs

2011 3.57 0.96

2012 8.85 1.91

2013 5.03 0.82

mid PVs

2011 7.29 2.78

2012 17.52 12.25

2013 8.69 2.25

far PVs

2011 4.20 0.60

2012 7.17 0.52

2013 8.46 1.36

all REs

2011 15.00 7.20

2012 35.78 7.59

2013 11.37 3.44

Figure 22. Three year trend of Vegetation Zone

Dominance (VZD) scores at PVs (near, mid, and far) and

REs. Lower VZD scores are communities whose

structure suggest more tidal flooding.


which sampling years (polygons) exhibit larger metrics with greater lengths. Notable

relationships may be observed when arrows point in the same direction (positive correlation) or

when they are opposing (negative correlation). It should also be noted that arrows that are

orthogonal to one another have no correlation.

Figure 23. Principal component analysis for Christina River. Red labels are main analysis variables; blue

arrows are covariant environmental variables. Vertices of polygons are labeled A, B, and C, for near, mid,

and far, respectively, followed by the sample year

For the Christina River SSIM station, the ordination plot shows a large degree of polygon

overlap, which suggests that year to year variation among all SSIM metrics analyzed in the PCA

shows no specific trend. Individually, however, VZD scores and accretion appear to be

negatively correlated, which implies that higher accretion rates have lower VZD scores. SET

heights and diversity show a similar negative correlation. SET heights appear to have a stronger

influence over the differences between sampling locations (near, mid, far) than accretion rates.

This coincides with our findings that SET height change is much larger, on average, than surface

accretion rates (~12 mm/yr versus ~1 mm/yr), which is also reflected in the ordination plot as the

blue accretion arrow is shorter than the SET height arrow. The remaining metrics appear to be

relatively orthogonal to one another, and of similar lengths.


5.2.5 Water Quality

Average water quality values are described in the following tables:

Table 13. Average total alkalinity, turbidity, chlorophyll α concentration, total suspended

solids respectively, measured from water samples taken from the main water body of Christina

River in 2013. Error values are standard error, n = 5.

Date Total Alk

(mg/L) Turbidity

(NTU) Chloro a

(ug/L) TSSn(mg/L)

6/2/13 58.9 ± 1.57 12.9 ± 1.22 46.1 ± 3.41 20.3 ± 1.13

22/7/13 58.1 ± 0.451 12.1 ± 0.552 65.9 ± 5.49 23.9 ± 2.62

14/8/13 24.5 ± 0.889 45.3 ± 1.19 6.1 ± 0.554 51.3 ± 5.29

Table 14. Average temperature, salinity (uS/cm and ppt), dissolved oxygen (percent and concentration), and pH

respectively, measured from YSI meter from the main water body of Christina River. Error values are standard error. N =

5, except on 1/9/2011, only one measurement was taken for values with no error due to an equipment malfunction, otherwise

n=2.

Date TEMP SAL (uS/cm) SAL DO (%) DO (mg/L) pH

3/9/10 26.8 ± 0.0195 2883 ± 120 1.48 ± 0.0643 83.6 ± 0.378 NA NA

1/11/10 11.06 ± 0.743 145.6 ± 53.4 0.0680 ± 0.0281 79.1 ± 1.34 8.78 ± 0.147 NA

16/2/11 3.06 ± 0.0437 786.8 ± 27.3 0.384 ± 0.0146 38.1 ± 1.19 5.09 ± 0.140 NA

1/9/11 21.4 ± 0.0250 234 NA 48.6 4.31 6.94 ± 0.0400

11/6/12 23.7 ± 0.0198 313.4 ± 1.43 0.150 ± 0 95.1 ± 0.630 8.01 ± 0.0535 7.35 ± 0.0497

12/9/12 24.1 ± 0.117 1331 ± 212 0.726 ± 0.112 82.1 ± 1.61 6.84 ± 0.146 7.29 ± 0.0363

5/6/13 24.3 ± 0.0663 413.7 ± 13.1 0.198 ± 0.00583 98.9 ± 1.41 8.30 ± 0.102 7.41 ± 0.0459

INTENSIVE MONITORING CONCLUSIONS

Site specific intensive monitoring requires several years of data to elucidate and make

conclusions about trends in the data collected. Five years of data is the least recommended for

these purposes, but only three years of data have been collected at Christina River. It is

extremely important to continue to collect data for the next two years to fulfill the minimum

requirement to discern relevant trends. It is equally important that monitoring be continued even

further, as more data substantiates these trends, and more cause and effect relationships can be

teased from larger datasets. This is especially important for tracing the effects of increasing

storm frequencies due to climate change, as more frequent storms with greater intensities have

observable effects on sediment loads, and therefore accretion on the marsh platform.

Between 2011 and 2013, there are some observable patterns, but given that there are only

three years of data, these patterns could change within another sampling year. This report will

discuss these important patterns in brief, especially in light of the goals of SSIM for the

MACWA, but it should be noted that more data are needed to make definitive conclusions.

Because data produced no significant linear trends, a principal component analysis (PCA)

was performed. PCA is useful because it does not require many years of data to highlight key


differences between sample periods. This is why a PCA was chosen to aid in the analysis of the

data collected from the Christina River SSIM station. Permanent plot data and SET-MH data

were utilized for the PCA, as water quality, soil quality, biomass, and line transect data are too

sparse as of yet. Key PCA findings suggest that differences between years at Christina River are

minimal, but SET heights, i.e. subsurface processes, drive elevation changes. Furthermore, a

negative correlation between accretion rates and VZD scoring it apparent, which alludes to

differential sediment deposition across the marsh platform as higher accretion rates are found

where plant communities are closer to tidal influence.

From the SET height data the marsh platform at Christina River appears to be increasing.

Because SET heights are not equal to accretion, the primary source of elevation change is likely

subsurface processes—specifically swelling. The more direct cause of platform swelling,

however, requires more in depth studies of belowground biomass and water flux. Although SET

data suggest that the platform is increasing in elevation, vegetation transects suggested that

between 2011 and 2012, the platform had subsided. These disparate results could be due to a lack

of data, or could be contributed to natural variation in the sink-swell patterns of the marsh

platform, such as those that arise from varying distances to the main water channel. Plant

community structures through VZD scores appear to have increased marginally in mid and far

plots, but decreased in near plots and random edges. More data is needed to test correlations

between marsh elevation and plant communities, especially as there appears to be differential

elevation changes.

Many marshes along the Mid-Atlantic suffer from subsidence, or landmass sinking; this

characteristic, coupled with decreasing sediment loads from anthropogenic or natural causes,

reduces the marsh’s ability to maintain its platform with increasing sea levels. At the time of this

report, NOAA predicts that at Reedy Point, at the C&D Canal, DE sea level is rising

approximately 3.46±0.66 mm/yr. Given this estimate of sea level rise, from 2011-2013 the

Christina River benchmarks have actually increased in elevation at an excess of 7.4 mm/yr. On

the other hand, vegetation transects suggest that elevation between 2011-2012 in the proximity of

the SET-MH has decreased by 0.133m. Coupling these findings with community structure will

be extremely important to elucidating what processes drive elevation change at Christina River

and how these processes are affected by the overall condition of the watershed. This may be

especially important in terms of sediment loads, which control accretion rates, as well as nutrient

patterns that fuel plant community robustness, which affects belowground structure through root

biomass and/or decomposition.


LITERATURE CITED

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Watershed prepared for The Christina Basin Clean Water Partnership. University of

Delaware Institute for Public Administration-Water Resources Agency.

Brand, A.B, J. W. Snodgrass, M.T. Gallagher, R. E. Casey, R. Van Meter. 2010. Lethal and

sublethal effects of embryonic and larval exposure of Hyla versicolor to stormwater pond

sediments. Archives of Environmental Contamination and Toxicology 58:325-331.

Cahoon, D. R. and R. E. Turner. 1989. Accretion and canal impacts in a rapidly subsiding

wetland II. Feldspar marker horizon technique. Estuaries 12: 260-268.

Cahoon, D. R., J. C. Lynch, P. Hensel , R. Boumans, B. C. Perez, B. Segura, and J. W. Day, Jr.

2002. A device for high precision measurement of wetland sediment elevation: I. Recent

improvements to the sedimentation-erosion table. Journal of Sedimentary Research 72:

730-733.

Cahoon, DR, JC Lynch, BC Perez, B Segura, R Holland, C Stelly, G Stephenson, and PF Hensel.

2002. High precision measurement of wetland sediment elevation: II. The rod surface

elevation table. Journal of Sedimentary Research 72(5):734-739.

Carullo, M., B.K. Carlisle, and J.P. Smith. 2007. A New England Rapid Assessment Method for

Assessing Condition of Estuarine Marshes; A Boston Harbor, Cape Cod and Islands Pilot

Study. Massachusetts Office of Coastal Zone Management, Boston, USA.

Collins, N. N., E. D. Stein, M. Sutula, R. Clark, A. E. Fetscher, L. Grenier, C. Grosso, and A.

Wiskind. 2008. California Rapid Assessment Method (CRAM) for Wetlands, Version

5.0.2.

DNREC. 2008. Delaware Wetlands Conservation Strategy. Delaware Department of

Natural Resources and Environmental Control, Dover, USA.

Fretwell, J. D., J. S. Williams, and P. J. Redman. 1996. National water summary on wetland

resources. United State Geological Survey Water Supply Paper: 2425. U.S. Government

Printing Office, Washington, D.C.

Hodges, A. L. Jr. 1984. Delaware in National Water Summary 1984: Hydrologic events--

Selected water-quality trends, and Groundwater resources, p. 243-248.

Jacobs, A.D. 2010. Delaware Rapid Assessment Procedure Version 6.0. Delaware Department

of Natural Resources and Environmental Control, Dover, DE, USA.

Jacobs, A.D., A.M. Howard, and A.B. Rogerson. 2010. Mid-Atlantic Tidal Wetland Rapid

Assessment Method Version 3.0. Delaware Department of Natural Resources and

Environmental Control, Dover, DE, USA. 47pp.


Jacobs, A.D., D. Whigham, D. Fillis, E. Rehm, and A. Howard. 2009. Delaware Comprehensive

Assessment Procedure Version 5.2. Delaware Department of Natural Resources and

Environmental Control. Dover, Delaware, USA. 72pp.

Jacobs, A.D., M.E. Kentula, and A.T. Herlihy. 2009. Developing an index of wetland condition

from ecological data: an example using HGM functional variables from the Nanticoke

watershed, USA. Ecological Indicators 10: 703-712.

MACWA SSIM QAPP. 2010. Intensive Monitoring and Assessment Program for Tidal Wetlands

of Delaware, New Jersey & Pennsylvania, Version 1.0. Partnership for the Delaware

Estuary: Danielle Kreeger, Angela Padeletti; Barnegat Bay Partnership: Martha Maxwell-

Doyle.

McAvoy, W. and K. Clancy. 1994. Community Classification and Mapping Criteria for

Interdunal Swales and Coastal Plain Pond Wetlands in Delaware. Final Report

Submitted to Watershed Assessment Branch, DNREC Division of Water Resources,

Dover, DE.

NOAA Tides and Currents. 2014. Mean Sea Level Trend, 8551910 Reedy Point, Delaware.

Accessed 19 June 2014

http://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?stnid=8551910

Partnership for the Delaware Estuary. 2012. Technical Report For the Delaware Estuary and

Basin. PDE Report No. 12-01 255 pages.

www.delawareestuary,=.org/science_programs_state_ of_the_estuary.asp.

Partnership for the Delaware Estuary. May 2012. Development and Implementation of an

Integrated Monitoring and Assessment Program for Tidal Wetlands. PDE Report No. 12-

03. 77 pp.

Phillipp, K. R. 1995. Tidal wetlands characterization – then and now. Delaware Estuary

Program Final Report to Delaware River Basin Commission. 165pp.

Plank, M. O. and W. S. Schenck. 1998. Delaware Piedmont Geology: Including a guide to the

rocks of Red Clay Valley. Delaware Geological Survey, University of Delaware.

Special Publication No. 20.

USEPA. 2006. Nutrient and Dissolved Oxygen Total Maximum Daily Loads Under High-Flow

Conditions in the Christina River Basin, Pennsylvania-Delaware-Maryland. U.S.

Environmental Protection Agency, Region III, Philadelphia, PA.

USGS Patuxent Wildlife Research Center. 2014. Surface Elevation Table (SET). Accessed 7

June 2014. http://www.pwrc.usgs.gov/set/

Sebold, K. R. 1992. From marsh to farm: The landscape transformation of coastal New Jersey.

National Park Service, U.S. Department of the Interior, Washington, D.C.

http://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?stnid=8551910

http://www.delawareestuary,=.org/science_programs_state_%20of_the_estuary.asp

http://www.pwrc.usgs.gov/set/


Sifneos, J. C., A. T. Herlihy, A. D. Jacobs and M. E. Kentula. 2010. Calibration of the

Delaware rapid assessment protocol to a comprehensive measure of wetland condition.

Wetlands 30:1011-1022.

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of Delaware’s Department of Natural Resources and Environmental Control (DNREC)

and for the Department of Transportation (DELDOT), Dover, DE. USA.

State of Delaware. 2007. Statewide Wetland Mapping Project (SWMP). Prepared for the State

of Delaware’s Department of Natural Resources and Environmental Control (DNREC).

Dover, DE, USA.

Stevens, D.L. Jr., and A.R. Olsen. 1999. Spatially restricted surveys over time for aquatic

resources. Journal of Agricultural, Biological, and Environmental Statistics 4:415-428.

Stevens, D.L. Jr., and A.R. Olsen. 2000. Spatially-restricted random sampling designs for

design-based and model-based estimation. Pages 609-616 in Accuracy 2000: Proceedings

of the 4th International symposium on spatial accuracy assessment in natural resources

and environmental sciences. Delft University Press, Delft, The Netherlands.


APPENDIX A: Qualitative Disturbance Rating (QDR) Category Descriptions

Qualitative Disturbance Rating: Assessors determine the level of disturbance in a

wetland through observation of stressors and alterations to the vegetation, soils,

hydrology in the wetland site, and the land use surrounding the site. Assessors should

use best professional judgment (BPJ) to assign the site a numerical Qualitative

Disturbance Rating (QDR) from least disturbed (1) to highly disturbed (6) based on the

narrative criteria below. General description of the minimal disturbance, moderate

disturbance and high disturbance categories are provided below.

Minimal Disturbance Category (QDR 1 or 2): Natural structure and biotic

community maintained with only minimal alterations. Minimal disturbance sites

have a characteristic native vegetative community unmodified water flow into and

out of the site, undisturbed microtopographic relief, and are located in a landscape of

natural vegetation (100 or 250 m buffer). Examples of minimal alterations include a

small ditch that is not conveying water, low occurrence of invasive species, individual

tree harvesting, and small areas of altered habitat in the surrounding landscape,

which does not include hardened surfaces along the wetland/upland interface. Use

BPJ to assign a QDR of 1 or 2.

Moderate Disturbance Category (QDR 3 or 4): Moderate changes in structure

and/or the biotic community. Moderate disturbance sites maintain some components

of minimal disturbance sites such as unaltered hydrology, undisturbed soils and

microtopography, intact landscape, or characteristic native biotic community despite

some structural or biotic alterations. Alterations in moderate disturbance sites may

include one or two of the following: a large ditch or a dam either increasing or

decreasing flooding, mowing, grazing, moderate stream channelization, moderate

presence of invasive plants, forest harvesting, high impact land uses in the buffer,

and hardened surfaces along the wetland/upland interface for less than half of the

site. Use BPJ to assign a QDR of 3 or 4.

High Disturbance Category (QDR 5 or 6): Severe changes in structure and/or

the biotic community. High disturbance sites have severely disturbed vegetative

community, hydrology and/or soils as a result of ≥1 severe alterations or >2 moderate

alterations. These disturbances lead to a decline in the wetland’s ability to effectively

function in the landscape. Examples of severe alterations include extensive ditching

or stream channelization, recent clear cutting or conversion to an invasive vegetative

community, hardened surfaces along the wetland/upland interfaces for most of the

site, and roads, excessive fill, excavation or farming in the wetland. Use PBJ to

assign a QDR of 5 or 6.


APPENDIX B: DERAP Stressor Codes and Definitions

Habitat Category (within 40m radius of sample point)

Hfor50 Forest age 31-50 years



Hfor2 Forest age ≤2 years

Hcc10 <10% of AA clear cut within 50 years

Hcc50 11-50% of AA clear cut within 50 years

Hcc100 >50% of AA clear cut within 50 years

Hforsc Selective cutting forestry

Hpine Forest managed or converted to pine

Hchem Forest chemical defoliation

Hmow Mowing in AA

Hfarm Farming activity in AA

Hgraz Grazing in AA

Hnorecov Cleared land not recovering

Hinv1

Invasive plants cover <1% of AA

Hinv5 Invasive plants cover 1-5% of AA

Hinv50 Invasive plants cover 6-50% of AA

Hinv100 Invasive plants cover >50% of AA

Hherb Excessive Herbivory/Pinebark Beetle/Gypsy Moth

Halgae Nutrients dense algal mats

Hnis50 Nutrient indicator plant species cover <50% of AA

Hnis100 Nutrient indicator plant species cover >50% of AA

Htrail Non-elevated road

Hroad Dirt or gravel elevated road in AA

Hpave Paved road in AA

Hydrology Category (within 40m radius of sample point)

Wditchs Slight Ditching; 1-3 shallow ditches (<.3m deep) in AA

Wditchm Moderate Ditching; 3 shallow ditches in AA or 1 ditch >.3m

within 25m of edge Wditchx Severe Ditching; >1 ditch .3-.6 m deep or 1 ditch > .6m deep

within AA Wchannm Channelized stream not maintained

Wchan1 Spoil bank only one side of stream

Wchan2 Spoil bank both sides of stream

Wincision Natural stream channel incision

Wdamdec Weir/Dam/Road decreasing site flooding

Wimp10 Weir/Dam/Road impounding water on <10% of AA

Wimp75 Weir/Dam/Road impounding water on 10-75% of AA

Wimp100 Weir/Dam/Road impounding water on >75% of AA

Wstorm Stormwater inputs

Wpoint Point source (non-stormwater)

Wsed Excessive sedimentation on wetland surface


Hydrology Category (continued)

Wfill10 Filling or excavation on <10% of AA

Wfill75 Filling or excavation on 10-75% of AA

Wfill100 Filling or excavation on >75% of AA

Wmic10 Microtopographic alterations on <10% of AA

Wmic75 Microtopographic alterations on 10-75% of AA

Wmic100 Microtopographic alterations on >75% of AA

Wsubsid Soil subsidence or root exposure

Landscape/Buffer Category (within 100m radius outside site/AA)

Ldevcom Commercial or industrial development

Ldevres3 Residential development of >2 houses/acre

Ldevres2 Residential development of ≤2 houses/acre

Ldevres1 Residential development of <1 house/acre

Lrdgrav Dirt or gravel road

Lrd2pav 2-lane paved road

Lrd4pav ≥4-lane paved road

Llndfil Landfill or waste disposal

Lchan Channelized streams or ditches >0.6m deep

Lag Row crops, nursery plants, or orchards

Lagpoul Poultry or livestock operation

Lfor Forest harvesting within past 15 Years

Lgolf Golf course

Lmow Mowed area

Lmine Sand or gravel mining operation


APPENDIX C: DERAP IWC Stressors and Weights

** Stressors with weights in boxes were combined during calibration analysis and are counted only once, even if more than one

stressor is present.

Category/Stressor Name* Code Stressor Weights**

*DERAP stressors excluded from this table are not in

the rapid IWC calculation. Flats Riverine Depression

Habitat Category (within 40m radius site)

Mowing in AA Hmow

15 3 24 Farming activity in AA Hfarm

Grazing in AA Hgraz

Cleared land not recovering in AA Hnorecov

Forest age 16-30 years Hfor16 5 4 2

≤10% of AA clear cut within 50 years Hcc10

Forest age 3-15 years Hfor3

19 7 12 Forest age ≤2 years Hfor2

11-50% of AA clear cut within 50 years Hcc50

>50% of AA clear cut within 50 years Hcc100

Excessive Herbivory Hherb 4 2 2

Invasive plants dominating Hinvdom 2 20 7

Invasive plants not dominating Hinvless 0 5 7

Chemical Defoliation Hchem 5 9 1

Managed or Converted to Pine Hpine

Non-elevated road in AA Htrail

2 2 2 Dirt or gravel elevated road in AA Hroad

Paved road in AA Hpave

Nutrient indicator species dominating AA Hnutapp 10 12 10

Nutrients dense algal mats Halgae

Hydrology Category (within 40m radius site)

Slight Ditching Wditchs 10

0

5 Moderate Ditching Wditchm 0

Severe Ditching Wditchx 17 0

Channelized stream not maintained Wchannm 0 13 0

Spoil bank only one side of stream Wchan1 0 31

0

Spoil bank both sides of stream Wchan2 0 0

Stream channel incision Wincision 0 21 0

WeirDamRoad decreasing site flooding Wdamdec

2 2 2 WeirDamRoad/Impounding <10% Wimp10

WeirDamRoad/Impounding 10-75% Wimp75

WeirDamRoad/Impounding >75% Wimp100

Stormwater Inputs Wstorm

2 2 2 Point Source (non-stormwater) Wpoint

Excessive Sedimentation Wsed


APPENDIX C continued

** Stressors with weights in boxes were combined during calibration analysis and are counted

only once, even if more than one stressor is present.

Hydrology Category (continued)

Filling, excavation on <10% of AA Wfill10 2 0 8

Filling, excavation on 10-75% of AA Wfill75 16 11 2

Filling, excavation on >75% of AA Wfill100

Soil Subsidence/Root Exposure Wsubsid 7 0 0

Microtopo alterations on <10% of AA Wmic10

Microtopo alteations on 10-75% of AA Wmic75 16 11 2

Microtopo alterations on >75% of AA Wmic100

Buffer Category (100m radius around site)

Development- commercial or industrial Ldevcom

1 buffer

stressor = 3

2 buffer

stressors = 6

≥ 3 buffer

stressors = 9

1 buffer

stressor = 1

2 buffer

stressors = 2

≥ 3 buffer

stressors = 3

1 buffer

stressor = 4

2 buffer

stressors = 8

≥ 3 buffer

stressors = 12

Residential >2 houses/acre Ldevres3

Residential ≤2 houses/acre Ldevres2

Residential <1 house/acre Ldevres1

Roads (buffer) mostly dirt or gravel Lrdgrav

Roads (buffer) mostly 2- lane paved Lrd2pav

Roads (buffer) mostly 4-lane paved Lrd4pav

Landfill/Waste Disposal Llndfil

Channelized Streams/ditches >0.6m deep Lchan

Row crops, nursery plants, orchards Lag

Poultry or Livestock operation Lagpoul

Forest Harvesting Within Last 15 Years Lfor

Golf Course Lgolf

Mowed Area Lmow

Sand/Gravel Operation Lmine

Intercept/Base Value 95 91 82

Flats IWCrapid= 95 -(∑weights(Habitat+Hydro+Buffer))

Riverine IWCrapid= 91 -(∑weights(Habitat+Hydro+Buffer))

Depression IWCrapid= 82 -(∑weights(Habitat+Hydro+Buffer))


APPENDIX D: MidTRAM Raw Data and Metric Scores from Estuarine Sites in

the Christina River Watershed

Blue columns indicate raw variable values; orange columns indicate corresponding metric scores

Buffer Metrics:

Site Number* QDR

Mid-

TRAM

Score

B1:

% of AA

with 5m-

buffer

B2:

Average

Buffer

Width

B3:

Percent

Develop-

ment

B5:

% of

Landward

Edge

Obstructed

B1

Score

B2

Score

B3

Score

B4

Score

B5

Score

RMDTCH11X005 3 86.7 100 242 0 0 12 12 12 6 12

RMDTCH11X011 2 84.4 100 154 1 0 12 9 9 9 12

RMDTCH11X018 2 83.3 100 189 0 0 12 9 12 12 12

RMDTCH11X022 2 80.0 100 250 10 100 12 12 6 9 3

RMDTCH11X008 2 77.8 100 250 0 0 12 12 12 9 12

RMDTCH11X000 3 77.8 100 235 10 10 12 12 6 6 6

RMDTCH11X027 2 75.6 100 119 5 100 12 6 9 9 3

RMDTCH11X004 4 75.6 100 177 25 10 12 9 3 6 6

RMDTCH11X001 2 73.3 100 216 18 100 12 12 3 6 3

RMDTCH11X010 6 72.8 100 142 40 100 12 9 3 3 3

RMDTCH11X024 1 71.1 100 250 0 0 12 12 12 9 12

RMDTCH11X020 3 62.2 100 248 15 0 12 12 6 3 12

RMDTCH11X016 5 61.1 100 75 20 100 12 6 3 3 3

RMDTCH11X025 6 60.0 100 117 25 100 12 6 3 3 3

RMDTCH11X007 5 58.3 100 187 22 100 12 9 3 3 3

RMDTCH11X033 4 55.6 100 233 1 0 12 12 9 6 12

RMDTCH11X037 5 53.9 100 93 20 100 12 6 3 3 3

RMDTCH11X012 5 53.3 100 123 20 60 12 6 3 3 3

RMDTCH11X030 5 47.8 100 151 40 30 12 9 3 3 3

RMDTCH11X002 6 45.6 100 205 5 100 12 12 9 3 3

RMDTCH11X032 5 45.6 85 159 12 100 9 9 6 3 3

RMDTCH11X019 6 45.0 80 166 30 50 9 9 3 3 3

RMDTCH11X009 6 43.3 100 168 15 100 12 9 6 3 3

RMDTCH11X029 6 43.3 100 141 25 100 12 9 3 3 3

RMDTCH11X036 6 42.8 100 195 17 50 12 12 3 3 3

RMDTCH11X023 5 42.2 100 172 35 100 12 9 3 3 3

RMDTCH11X003 6 41.7 100 147 40 100 12 9 3 3 3

RMDTCH11X038 6 40.6 100 218 12 100 12 12 6 3 3

RMDTCH11X015 6 37.2 80 132 45 100 9 9 3 3 3

RMDTCH11X039 6 30.0 75 83 50 100 9 6 3 3 3

* Site numbers are coded by condition categories (green are minimally stressed, yellow are moderately stressed,

red are severely stressed)


APPENDIX D continued

Orange columns indicate corresponding metric scores

Hydrology Metrics:

Site Number* QDR

Mid-

TRAM

Score

H1

Score

H2

Score

H3

Score

H4

Score

RMDTCH11X005 3 86.7 12 12 12 12

RMDTCH11X011 2 84.4 12 12 12 12

RMDTCH11X018 2 83.3 12 12 6 12

RMDTCH11X022 2 80.0 12 12 12 12

RMDTCH11X008 2 77.8 12 12 3 12

RMDTCH11X000 3 77.8 12 12 12 12

RMDTCH11X027 2 75.6 12 12 12 12

RMDTCH11X004 4 75.6 12 12 12 12

RMDTCH11X001 2 73.3 12 12 12 12

RMDTCH11X010 6 72.8 12 9 12 12

RMDTCH11X024 1 71.1 12 12 3 12

RMDTCH11X020 3 62.2 12 12 3 12

RMDTCH11X016 5 61.1 12 6 12 12

RMDTCH11X025 6 60.0 12 12 12 12

RMDTCH11X007 5 58.3 12 12 3 12

RMDTCH11X033 4 55.6 12 12 3 12

RMDTCH11X037 5 53.9 12 12 9 6

RMDTCH11X012 5 53.3 12 12 12 12

RMDTCH11X030 5 47.8 12 12 3 3

RMDTCH11X002 6 45.6 12 12 3 3

RMDTCH11X032 5 45.6 12 3 3 12

RMDTCH11X019 6 45.0 12 6 6 3

RMDTCH11X009 6 43.3 12 12 3 3

RMDTCH11X029 6 43.3 12 3 12 3

RMDTCH11X036 6 42.8 12 3 3 9

RMDTCH11X023 5 42.2 12 3 3 6

RMDTCH11X003 6 41.7 12 3 3 3

RMDTCH11X038 6 40.6 9 12 3 3

RMDTCH11X015 6 37.2 12 3 3 3

RMDTCH11X039 6 30.0 9 3 3 3

* Site numbers are coded by condition categories (green are minimally stressed, yellow are moderately

stressed, red are severely stressed)


APPENDIX D continued

Blue columns indicate raw variable values; orange columns indicate corresponding metric scores

Habitat Metrics:

Site Number* QDR

Mid-

TRAM

Score

HAB1:

Bearing

Capacity

HAB2:

Veg

Obstruc-

tion

HAB3:

# of

Plant

Layers

HAB4:

Percent

Co-dom

Invasive

spp.

HAB5:

Percent

Invasive

Cover

HAB1

Score

HAB2

Score

HAB3

Score

HAB4

Score

HAB5

Score

RMDTCH11X005 3 86.7 5.19 0.75 3 14 35 6 12 9 12 6

RMDTCH11X011 2 84.4 9.28 0.5 3 0 0 3 12 9 12 12

RMDTCH11X018 2 83.3 7.47 0 3 0 0 3 12 9 12 12

RMDTCH11X022 2 80.0 6.19 0.25 3 0 0 6 12 9 12 12

RMDTCH11X008 2 77.8 5.78 2.25 4 0 0 6 12 12 12 12

RMDTCH11X000 3 77.8 9.41 3.25 2 0 0 3 12 9 12 12

RMDTCH11X027 2 75.6 7.06 1 3 0 0 3 12 9 12 12

RMDTCH11X004 4 75.6 5.5 6.75 3 0 0 6 12 9 12 12

RMDTCH11X001 2 73.3 5.94 8.75 3 0 0 6 9 9 12 12

RMDTCH11X010 6 72.8 1.5 4 3 0 0 12 12 9 12 12

RMDTCH11X024 1 71.1 4.31 13.5 2 0 0 6 6 9 12 12

RMDTCH11X020 3 62.2 5.75 15 3 0 0 6 6 9 12 12

RMDTCH11X016 5 61.1 6.19 4.25 3 12.5 12.5 6 12 9 12 9

RMDTCH11X025 6 60.0 4.66 5.5 3 17 55 6 12 9 9 3

RMDTCH11X007 5 58.3 2.91 10.75 3 28.6 15 9 9 9 9 9

RMDTCH11X033 4 55.6 6.94 9.75 3 33 60 3 9 9 6 3

RMDTCH11X037 5 53.9 5.16 3.5 3 25 45 6 12 9 9 6

RMDTCH11X012 5 53.3 5.09 6.75 1 100 96 6 12 6 3 3

RMDTCH11X030 5 47.8 3.13 2.5 4 40 60 9 12 12 6 3

RMDTCH11X002 6 45.6 3.28 11.75 1 100 94 9 9 6 3 3

RMDTCH11X032 5 45.6 4.22 7.25 3 0.33 24 6 9 9 12 9

RMDTCH11X019 6 45.0 3.88 8.5 4 20 45 9 9 12 9 6

RMDTCH11X009 6 43.3 2.13 6 1 100 95 9 12 6 3 3

RMDTCH11X029 6 43.3 3.03 3.75 4 50 80 9 12 12 3 3

RMDTCH11X036 6 42.8 3.41 8.25 3 50 30 9 9 9 3 6

RMDTCH11X023 5 42.2 6.47 7 3 14 20 3 9 9 12 9

RMDTCH11X003 6 41.7 1.84 0 3 29 45 9 12 9 9 6

RMDTCH11X038 6 40.6 2.69 7.25 1 100 95 9 9 6 3 3

RMDTCH11X015 6 37.2 2.78 11 3 33 20 9 9 9 6 9

RMDTCH11X039 6 30.0 5.06 5.5 1 100 85 6 12 6 3 3

* Site numbers are coded by condition categories (green are minimally stressed, yellow are moderately stressed,

red are severely stressed)


APPENDIX E: DERAP Wetland Assessment Stressor Checklist for Non-tidal Flat

Wetlands in the Christina River Watershed

Stressor descriptions are listed in Appendix B. ‘1’ indicates stressor presence; ‘0’ indicates

stressor absence.

Habitat and Plant Community Stressors

Site

Number* QDR

DERAP

Score

Hfo

r31

Hfo

r16

Hfo

r3

Hfo

r2

Hcc1

0

Hcc5

0

Hcc1

00

Hfo

rsc

Hp

ine

Hch

em

Hm

ow

Hfarm

Hg

raz

Hn

oreco

v

Hin

v1

Hin

v5

Hin

v5

0

Hin

v1

00

Hh

erb

Halg

ae

Hn

is50

Hn

is10

0

Htrail

Hro

ad

Hp

ave

CH0124 2 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

CH0013 2 89 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0030 2 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0060 3 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0068 2 88 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

CH0021 3 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0015 3 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0019 3 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0029 3 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0063 3 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0071 3 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0046 4 80 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0009 4 79 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0027 3 79 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0107 2 78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0001 4 76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0

CH0079 5 75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0075 4 71 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0004 5 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0003 4 61 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0091 4 61 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0081 4 58 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0

CH0131 5 58 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0022 5 56 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0038 4 55 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0100 5 55 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0065 5 52 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0007 6 51 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0076 3 51 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0121 6 46 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0055 6 45 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

CH0017 5 43 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0




APPENDIX E continued


stressor absence.

Hydrology Stressors

Site

Number* QDR

DERAP

Score

Wd

itchs

Wd

itchm

Wd

itchx

Wch

ann

m

Wch

an1

Wch

an2

Win

cision

Wd

amd

ec

Wim

p1

0

Wim

p7

5

Wim

p1

00

Wsto

rm

Wp

oin

t

Wsed

Wfill1

0

Wfill7

5

Wfill1

00

Wm

ic10

Wm

ic75

Wm

ic10

0

Wsu

bsid

CH0124 2 91 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0013 2 89 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0030 2 89 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0060 3 89 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0068 2 88 0 0 0 - - - - 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0021 3 86 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0015 3 85 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 1

CH0019 3 84 0 0 0 - - - - 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0029 3 84 0 0 0 - - - - 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0063 3 82 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 1 0 0 0

CH0071 3 82 1 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0046 4 80 0 0 0 - - - - 0 0 0 0 1 0 0 0 0 0 1 0 0 0

CH0009 4 79 0 1 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0027 3 79 0 1 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0107 2 78 1 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0001 4 76 0 0 0 - - - - 0 0 0 0 0 0 0 0 1 0 1 0 0 0

CH0079 5 75 0 1 0 - - - - 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0075 4 71 0 0 0 - - - - 0 0 0 0 1 0 0 1 0 0 1 0 0 0

CH0004 5 65 0 0 1 - - - - 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0003 4 61 1 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0091 4 61 0 0 0 - - - - 0 0 0 0 0 0 0 1 0 0 1 0 0 0

CH0081 4 58 0 0 0 - - - - 0 0 0 0 1 0 0 1 0 0 0 0 0 0

CH0131 5 58 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 1 1 0 0 0

CH0022 5 56 1 0 0 - - - - 0 0 0 0 0 0 0 1 0 0 0 1 0 0

CH0038 4 55 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 0 0 1 0 0

CH0100 5 55 1 0 0 - - - - 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0065 5 52 0 0 1 - - - - 0 0 0 0 0 0 0 0 1 0 0 1 0 0

CH0007 6 51 0 0 0 - - - - 0 0 0 0 0 0 0 0 0 1 0 0 0 0

CH0076 3 51 0 1 0 - - - - 0 0 0 0 0 0 0 1 0 0 1 0 0 0

CH0121 6 46 0 0 1 - - - - 0 0 0 0 0 0 0 0 0 1 0 0 0 0

CH0055 6 45 0 1 0 - - - - 1 0 0 0 0 0 0 0 1 0 0 1 0 0

CH0017 5 43 1 0 0 - - - - 0 0 0 0 0 0 0 1 0 0 1 0 0 0




APPENDIX E continued


stressor absence.

Buffer Stressors

Site

Number* QDR

DERAP

Score

Ld

evco

m

Ld

evres3

Ld

evres2

Ld

evred

1

Lrd

grav

Lrd

2p

av

Lrd

4p

av

Lln

dfil

Lch

an

Lag

Lag

po

ul

Lfo

r

Lg

olf

Lm

ow

Lm

ine

CH0124 2 91 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0013 2 89 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0

CH0030 2 89 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0060 3 89 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0

CH0068 2 88 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0021 3 86 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0

CH0015 3 85 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0019 3 84 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0

CH0029 3 84 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0

CH0063 3 82 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0

CH0071 3 82 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0046 4 80 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0009 4 79 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0027 3 79 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0107 2 78 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0001 4 76 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0

CH0079 5 75 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0

CH0075 4 71 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0

CH0004 5 65 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0

CH0003 4 61 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0

CH0091 4 61 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0081 4 58 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0

CH0131 5 58 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0

CH0022 5 56 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0

CH0038 4 55 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0100 5 55 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0

CH0065 5 52 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0

CH0007 6 51 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0076 3 51 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0

CH0121 6 46 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0

CH0055 6 45 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0

CH0017 5 43 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0

* Site numbers are coded by condition categories (green are minimally stressed, yellow

are moderately stressed, red are severely stressed)


APPENDIX F: DERAP Wetland Assessment Stressor Checklist for Non-tidal

Riverine Wetlands in the Christina River Watershed


stressor absence.


Site

Number* QDR

DERAP

Score

Hfo

r31

Hfo

r16

Hfo

r3

Hfo

r2

Hcc1

0

Hcc5

0

Hcc1

00

Hfo

rsc

Hp

ine

Hch

em

Hm

ow

Hfarm

Hg

raz

Hn

oreco

v

Hin

v1

Hin

v5

Hin

v50

Hin

v10

0

Hh

erb

Alg

ae

Hn

is50

Hn

is100

Htrail

Hro

ad

Hp

ave

CH0028 3 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0092 3 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

CH0002 2 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0047 3 84 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0016 4 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0125 3 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0104 3 80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0

CH0066 5 71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0050 3 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0072 3 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0032 4 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0036 5 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0054 4 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0037 3 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0044 4 62 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0051 3 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0114 4 62 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0062 3 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0010 4 57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0058 6 55 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0136 4 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0064 4 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0098 5 49 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0101 5 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0005 4 46 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0113 5 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0134 4 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0

CH0096 6 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

CH0126 4 38 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0

CH0014 4 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0025 5 37 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0052 5 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0082 5 36 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

CH0049 4 31 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0006 5 24 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0

CH0031 5 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

CH0020 5 20 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0

CH0069 5 16 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1

CH0033 5 13 1 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1

CH0067 6 6 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0




APPENDIX F continued


stressor absence.

Hydrology Stressors

Site

Number* QDR

DERAP

Score

Wd

itchs

Wd

itchm

Wd

itchx

Wch

ann

m

Wch

an1

Wch

an2

Win

cision

Wd

amd

ec

Wim

p1

0

Wim

p7

5

Wim

p1

00

Wsto

rm

Wp

oin

t

Wsed

Wfill1

0

Wfill7

5

Wfill1

00

Wm

ic10

Wm

ic75

Wm

ic10

0

Wsu

bsid

CH0028 3 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0092 3 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

CH0002 2 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0047 3 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

CH0016 4 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0125 3 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0104 3 80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0066 5 71 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0050 3 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0072 3 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0032 4 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0036 5 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0054 4 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0037 3 63 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1

CH0044 4 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0051 3 62 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0114 4 62 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0062 3 59 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0

CH0010 4 57 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0058 6 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

CH0136 4 51 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0064 4 49 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0098 5 49 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0

CH0101 5 48 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0

CH0005 4 46 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0113 5 45 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0134 4 45 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0096 6 39 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0

CH0126 4 38 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0

CH0014 4 37 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

CH0025 5 37 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0

CH0052 5 36 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0

CH0082 5 36 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0049 4 31 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0

CH0006 5 24 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0

CH0031 5 21 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0020 5 20 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0

CH0069 5 16 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0

CH0033 5 13 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

CH0067 6 6 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0

* Site numbers are coded by condition categories (green are minimally stressed, yellow are

moderately stressed, red are severely stressed)


APPENDIX F continued


stressor absence.

Buffer Stressors

Site

Number QDR

DERAP

Score

Ld

evco

m

Ld

evres3

Ld

evres2

Ld

evred

1

Lrd

grav

Lrd

2p

av

Lrd

4p

av

Lln

dfil

Lch

an

Lag

Lag

po

ul

Lfo

r

Lg

olf

Lm

ow

Lm

ine

CH0028 3 88 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0092 3 87 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0002 2 85 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0047 3 84 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0016 4 83 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0

CH0125 3 83 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0

CH0104 3 80 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0

CH0066 5 71 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0

CH0050 3 70 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

CH0072 3 69 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0032 4 68 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0

CH0036 5 68 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0

CH0054 4 68 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0

CH0037 3 63 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0

CH0044 4 62 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0051 3 62 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0

CH0114 4 62 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0062 3 59 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0

CH0010 4 57 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0

CH0058 6 55 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0136 4 51 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0

CH0064 4 49 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0098 5 49 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0101 5 48 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0

CH0005 4 46 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

CH0113 5 45 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0

CH0134 4 45 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0096 6 39 0 0 0 1 0 0 1 0 1 1 0 0 0 1 0

CH0126 4 38 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

CH0014 4 37 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0

CH0025 5 37 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0

CH0052 5 36 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0

CH0082 5 36 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0

CH0049 4 31 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0

CH0006 5 24 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0

CH0031 5 21 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0

CH0020 5 20 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0

CH0069 5 16 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0033 5 13 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0

CH0067 6 6 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0

* Site numbers are coded by condition categories (green are minimally stressed,

yellow are moderately stressed, red are severely stressed)


APPENDIX G: DERAP Wetland Assessment Stressor Checklist for Non-tidal

Depression Wetlands in the Christina River Watershed


stressor absence.


Site

Number* QDR

DERAP

Score

Hfo

r31

Hfo

r16

Hfo

r3

Hfo

r2

Hcc1

0

Hcc5

0

Hcc1

00

Hfo

rsc

Hp

ine

Hch

em

Hm

ow

Hfarm

Hg

raz

Hn

oreco

v

Hin

v1

Hin

v5

Hin

v5

0

Hin

v1

00

Hh

erb

Alg

ae

Hn

is50

Hn

is10

0

Htrail

Hro

ad

Hp

ave

CH0011 4 15 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0

CH0080 5 48 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0

Hydrology Stressors

Site

Number* QDR

DERAP

Score

Wd

itchs

Wd

itchm

Wd

itchx

Wch

ann

m

Wch

an1

Wch

an2

Win

cision

Wd

amd

ec

Wim

p1

0

Wim

p7

5

Wim

p1

00

Wsto

rm

Wp

oin

t

Wsed

Wfill1

0

Wfill7

5

Wfill1

00

Wm

ic10

Wm

ic75

Wm

ic10

0

Wsu

bsid

CH0011 4 15 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

CH0080 5 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

Buffer Stressors

Site

Number* QDR

DERAP

Score

Ld

evco

m

Ld

evres3

Ld

evres2

Ld

evred

1

Lrd

grav

Lrd

2p

av

Lrd

4p

av

Lln

dfil

Lch

an

Lag

Lag

po

ul

Lfo

r

Lg

olf

Lm

ow

Lm

ine

CH0011 4 15 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0

CH0080 5 48 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0

* Site numbers are coded by condition categories (green are minimally stressed,

yellow are moderately stressed, red are severely stressed)


APPENDIX H: SITE SPECIFIC INTENSIVE MONITORING PLOT

LOCATIONS AND SAMPLING DATES

Permanent vegetation (PV) plot locations and sampling dates at the Christina River SSIM:

Plot Area

PV Number Latitude Longitude

Dates Sampled Photo Examples

2011 2012 2013 PV No. Date

Near

1 39.72015 -75.56193 8/8/2011 9/20/2012 7/22/2013

PV1

7/22/2013

2 39.72006 -75.56208 8/8/2011 9/20/2012 7/22/2013

3 39.71995 -75.56228 8/8/2011 9/20/2012 7/22/2013

Mid

4 39.72156 -75.56485 8/8/2011 9/20/2012 7/22/2013

PV5

7/22/2013

5

39.72139 -75.56496 8/8/2011 9/20/2012 7/22/2013

6 39.72128 -75.56510 8/8/2011 9/20/2012 7/22/2013

Far

7 39.72234 -75.56648 8/8/2011 9/20/2012 7/22/2013

PV9

7/22/2013

8 39.72220 -75.56659 8/8/2011 9/20/2012 7/22/2013

9

39.72204 -75.56675 8/8/2011 9/20/2012 7/22/2013

Random edge (RE) plot locations and sampling dates at the Christina River SSIM:

RE Number Latitude Longitude

Dates Sampled Photo Examples

2011 2012 2013 PV No. Date

1 39.71978 -75.56271 8/8/2011 9/20/2012 7/22/2013

RE1

7/22/2013

2 39.71939 -75.56369 8/8/2011 9/20/2012 7/22/2013

3 39.71911 -75.56491 8/8/2011 9/20/2012 7/22/2013

4 39.71864 -75.56716 8/8/2011 9/20/2012 7/22/2013

RE6

7/22/2013

5 39.71896 -75.56760 8/8/2011 9/20/2012 7/22/2013

6 39.72004 -75.56779 8/8/2011 9/20/2012 7/22/2013


GPS coordinates of line transect RTK points. Included data are orthoheights (plus standard deviation),

dominant and subdominant species observed, as well as sampling data and time:

Date/Time Latitude Longitude Ortho Ht Sd Height Dom Spp Subdom Spp

13/7/11 9:33 39.72213 -75.56681 0.6958 0.009 Typha angustifolia Peltandra virginica


13/7/11 9:39 39.72182 -75.56630 0.6035 0.0085 Peltandra virginica Typha angustifolia


13/7/11 9:46 39.72152 -75.56601 0.7037 0.0101 Typha angustifolia Impatiens capensis




13/7/11 10:18 39.72207 -75.56656 0.7211 0.0121


13/7/11 10:41 39.72183 -75.56568 0.6186 0.0099 Sagittaria latifolia Peltandra virginica




13/7/11 11:55 39.72141 -75.56487 0.681 0.0071 Typha angustifolia Sagittaria latifolia



13/7/11 12:08 39.72189 -75.56537 0.7085 0.0092


13/7/11 12:13 39.72202 -75.56573 0.589 0.0086 Typha angustifolia mix P. virginica/A. cannabinus


13/7/11 12:26 39.72179 -75.56501 0.5609 0.0077 Nuphar lutea Peltandra virginica

13/7/11 12:28 39.72177 -75.56498 0.5559 0.008 Typha angustifolia Pontederia cordata



13/7/11 12:46 39.72150 -75.56456 0.8289 0.0117 Nuphar lutea Sagittaria latifolia



13/7/11 12:58 39.72099 -75.56401 0.6792 0.0109 Impatiens capensis Peltandra virginica


13/7/11 13:08 39.72119 -75.56458 0.5056 0.0118 Typha angustifolia

13/7/11 13:10 39.72120 -75.56463 0.4802 0.0122 Pontederia cordata Sagittaria latifolia

13/7/11 13:11 39.72121 -75.56467 0.5277 0.018 Typha angustifolia Sagittaria latifolia








GPS coordinates of line transect RTK points continued: 13/7/11 13:50 39.71998 -75.56261 0.6359 0.0133 Impatiens capensis Sagittaria latifolia

13/7/11 13:53 39.72009 -75.56287 0.7504 0.0115 Impatiens capensis Sagittaria latifolia

13/7/11 14:05 39.72019 -75.56192 1.0291 0.0197 Scirpus fluviatilis Typha angustifolia








27/9/12 10:36 39.72152 -75.56602 0.6346 0.0116 Typha angustifolia mix Impatiens

capensis/Sagittaria latifolia

27/9/12 9:15 39.72232 -75.56682 0.2595 0.0134 Zizania aquatica Amaranthus cannabinus

27/9/12 14:01 39.72009 -75.56209 0.9185 0.0115 mix Typha angustifolia/Impatiens

capensis Aster sp./Acer negundo



27/9/12 13:47 39.72034 -75.56236 0.5398 0.0085 Impatiens capensis Typha angustifolia


27/9/12 13:35 39.72018 -75.56193 1.0091 0.0108 mix Acorus calamus/Scirpus fluviatilis

27/9/12 9:11 39.72212 -75.56684 0.1667 0.0158 Typha angustifolia mix Zizania

aquatica/Peltandra virginica



27/9/12 13:49 39.72037 -75.56236 0.2932 0.0109 Typha angustifolia Polygonum punctatum


APPENDIX I: VEGETATION ZONE DOMINANCE

Synopsis of Vegetation Zone Dominance “VZD”

Each plant species was assigned a score, based on its suggested proclivity for soil

saturation. For this, multiple sources were utilized including the USDA Plant Database's Wetland

Indicator Statuses and Pennsylvania Natural Heritage habitat description reports (both available

online). Scores vary from 1 to 4; 1 representing a location that is within regular tidal influence,

and flooded at least twice daily, and 4 representing the back levee of the wetland or its upland

border. These scores were then proportioned to the first three most dominant plant's estimated

percent cover in each permanent plot, according to a dominance scheme, where the most

dominant plants take priority and less dominant plants act to add better resolution to the scoring

process. A sum of these scores is the total score, or the VZD score for the plot.

Introduction

One of the objectives of MACWA is to identify ways we can quantify the subtle changes

within wetlands over time in a reproducible and comparable way. A major source of change is

sea level rise due to climate change, which has direct effects on tidal marshes. Vegetation Zone

Dominance (VZD) was designed to allow us to track the frequency of inundation, a direct effect

of rising sea level, at each PV based on the composition of the plant community. It is well

understood that certain plants occupy different areas along a hydrologic gradient, where

assemblages are considered uplands or wetlands. On a finer scale, however, specifically within

tidal wetlands, this gradation is also discernible—some plants occupy extremely saturated or

frequently to semi-permanently inundated habitats, whereas through competition or adaptation,

others are found at higher elevations, further from the water’s edge, in locations that may be

temporarily or seasonally flooded.

In this methodology, we seek to take advantage of flood gradients to elucidate a plant

community’s relationship to the water’s edge with expected zone affiliations in the tidal frame.

Permanent vegetation plots of one by one meter were established and reassessed over

consecutive years. By assigning numbers, or scores, to these plots that represent the plant

community, we hope to observe quantitatively any changes in the communities' proximity to the

water’s edge over time. This metric is designed to track how plant communities keep pace with

changes in sea level and land elevations, thus detecting cases where plant communities might

shift to wetter assemblages if a marsh is not keeping pace. More data collection is needed to

fully evaluate the utility of this new approach, and results included in this appendix should be

interpreted as preliminary.

Floristic quality index (FQI) was first considered, because it was designed to allow

researchers and managers to compare various plant communities quantitatively. The focus of this

index is quality, which for this sense is ultimately related to the presence of invasive species

(lower qualities) or rare natives (higher qualities). This index can also be described as a measure

of disturbance. Although this index is similar to what we sought to achieve, it does not share a

similar focus. It will, however, be used as a framework for our scoring system. In the FQI plants


are assigned a number (the conservation coefficient) that relates to the probability of its

occurrence in habitats unaltered since European settlement—larger numbers correspond to plants

with more restricted distributions. Mirroring this, in our scoring system, wetland plants were

assigned numbers that correspond with its distribution along a tidal flood gradient—larger

numbers are those further from the water’s edge. Lastly, the FQI does not include species cover.

We incorporated percent covers in our study because our plots are of a small scale, we are

investigating within larger wetland habitats, and the changes in dominant plant cover may be the

most important indicator of water level alterations.

Scoring Methodology

Plant species were assigned a score (see Figure 1A). These were based on the wetland

delineation statuses provided by the USDA and the U.S. Army Corps’ National Wetland Plant

List, along with habitat descriptions by Pennsylvania’s Natural Heritage for freshwater marshes

(“Freshwater Tidal Mixed High Marsh” and “Riverbank Freshwater Tidal Marsh”; see

http://www.naturalheritage.state.pa.us/Wetlands.aspx) and community descriptions by the

Barnegat Bay Partnership for saltwater marshes (http://bbp.ocean.edu/pages/305.asp).

A.

B.

Figure 1A. Diagram of plant zone scores for fresh water (A) and salt water (B) marshes.

Generic diagram of plant zones for freshwater (A) and salt water (B) marshes.

http://bbp.ocean.edu/pages/305.asp


Dominance Score

To calculate the dominance score, the first two most dominant plants, as recorded

proportions of observed cover (cover/total observed cover of plot) are multiplied by their

respective zone score (Score1). The two most dominant plant species must fall within 20% of

each other to be considered first dominant and second dominant. Otherwise, the second or the

third greatest plant coverage is considered the third dominant.

Also, the dominant plants must constitute at least or approximately half of the total

observed cover (see special treatments below). Frequently, co-dominance occurs at each level.

Co-dominance is defined for this study as two or more plant species with equal percent covers. If

this is the case for second or third level dominance, the proportions are added together and the

zone scores are averaged between the two plant species. Furthermore, if the third species falls

within 20% of the first two co-dominant species, the third fulfills the second level dominance

and if a fourth is present, it fulfills the third level. If this is true for first level dominance,

however, each species then fulfills the role of either first or second dominant. These levels are

weighted equally, so no other special considerations need to be implemented.

Third Level Dominance Score

In order to summarize the potential effects of a third dominant plant, a third level

dominance score is calculated (Score2). To obtain this, the first two dominant species’ zones are

averaged, and then subtracted from the third dominant plant’s zone, summarizing the zone

change. The third dominant plant cover is then multiplied by the zone change.

Score2 = zone3 - ( zone1 + zone2

) * ( observed cover3

) 2 total cover

This aims to capture how the presence of a third dominant species may be useful in

capturing where along the marsh gradient the community may lay. Third level dominance

numbers may be positive, zero, or negative. As such, a negative third level score would indicate

the presence of a species that is found typically closer to the bank, thereby giving the community

a score that is lower than just the dominance score.

Score1 = ( observed cover1

) * zone1 + ( observed cover2

) * zone2 total cover total cover


Total VZD Score

The addition of the Dominance Score (Score1) and the Third Level Dominance Score

(Score2) gives total vegetation zone dominance score, hereby referred to as simply the VZD

Score.

Total VZD = Score1 + Score2

Special treatments

Occasionally, the highest percentages listed do not account for a large enough portion of

the total observed cover. Such an error can cause spurious scores that are much lower than

expected based on the plants present. This error may be better corrected by establishing a “line”

for dominance. That is, no species can obtain first dominance unless it makes up at least or

approximately half of observed total cover. A community of dominance must be established if

this is not met. Dominant species covers should be added until this level is reached. For example,

consider a plot with a total cover of 90%; where Typha angustifolia is 25%, Acorus calamus

15%, and Impatiens capensis 8%. The total first dominance cover is then 48% (more than half of

90) and the three zones should be averaged. This plot's first dominant label would therefore be

"Typha-Acorus-Impatiens." The next dominant would constitute the 3rd

level not the second.

If the total observed cover recorded is much less than the gross or additive species covers,

scoring over estimates the proportion of each species’ cover. When this occurs, the first and

second dominant covers are added and used instead of what was recorded in the field. Third level

dominance cover is not added because although the discrepancy for first and second cover must

be altered to make mathematical sense, there was likely significant plant cover overlap within the

plot. By not including the third level at least a notion of this situation can be preserved while also

avoiding a majority of the mathematical error that these records produce. It is also important to

note that the scores of monoculture plots are simply the percent cover multiplied by the zone

score. As there are no other dominance effects in play, this is inherent in the way things are

scored, so this does not represent a special treatment.

Score Interpretation

Freshwater marshes VZD scores fall between 0 and 3.5; where 1 is the maximum extent

of the riverbank habitat. Half a meter from mean high tide theoretically delineates this limit. A

score of 3.5 represents beyond the back levee of the flood plain; plants at this point and beyond

are some degree facultative or considered upland species. Mixed marsh scores (<1.5) represent

communities that mostly exist within regular tidal influence, and higher scores (~1.5 to 3) are

those that occupy the higher marsh. It should be noted that the mixed high marsh is generally

considered to be an area of poor zonation. Poor zonation may be in part related to the multitude

of competitive interactions within freshwater marshes. The most important interpretations of

these data should focus on scores that fall from 0 to ~1.5 due to this phenomenon. Scores from 0-

1.5 may represent communities consisting largely of Nuphar lutea, which opportunistically

occupies muddy river or creek banks; Nuphar lutea can withstand large tidal fluxes and uses


asexual or rhizomatous reproduction to colonize and recolonize these areas. These unique

characteristics may make N. lutea the ideal plant to gauge changing freshwater tidal marsh

topography. Other Nuphar communities do exist outside of river or creek banks—the species is

very opportunistic. Other plants of interest that exist at the bank include: Sagittaria sp. and

Scirpus fluvialitis. Scirpus sp. are also thought to be opportunistic and can withstand tidal energy

well (which other high mixed marsh species may deal with poorly).

For saline marshes, VZD scores fall between 0 and 2.5; where scores of 1 indicate plants

that are regularly inundated by the tide. This area is largely dominated by high vigor Spartina

alterniflora, whose rhizomatous growth habits tend to create a natural levee that reduces flooding

of plant communities behind it. Scores from 1.25 to 1.75 indicate that the plant community exists

directly behind this front levee; these plants are adapted to saturated and extremely haline soils.

Scores of 2 and above represent the plant communities of slightly higher elevations, where soils

are less haline and flooding occurs less frequently. Although some zonation is apparent in

freshwater marshes, saline marshes are considered to have much more distinct zonation, despite

the dense monoculture stands of Spartina alterniflora. Zonation can be deciphered regardless of

this due to the growth form of Spartina alterniflora. The plant's phenotypic plasticity is a good

indicator for zonation, as well as its presence or absence.

Over time, scores may be seen as increasing or decreasing. Increasing scores indicate

increasing average group scores over time. This means that the communities are shifting to

consist of more facultative or higher marsh species, and likely shifting away from wetter

habitats. Likewise, decreasing scores depict communities that are shifting to more flood tolerant

species representing toward wetter habitats.


This report and other watershed condition reports, assessment methods, and scoring

protocols can be found on the Delaware Wetlands website:

Matthew A. Jennettedelawareestuary.s3.amazonaws.com/pdf/Summit15/PDE-Report...Figure 17. Site Specific Intensive Monitoring (SSIM) point locations at Christina River, Wilmington, DE:

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