LAND-COVER CHANGE AND WETLAND PLANT ZONATION IN

LAND-COVER CHANGE AND WETLAND PLANT ZONATION IN
LAND-COVER CHANGE AND WETLAND PLANT ZONATION
IN WEEKS BAY, ALABAMA
by
ADAM JEFFREY CONSTANTIN
JULIA A. CHERRY, COMMITTEE CHAIR
JONATHAN P. BENSTEAD
WHITNEY P. BROUSSARD III
JUST CEBRIAN
A THESIS
Submitted in partial fulfillment of the requirements
for the degree of Master of Science
in the Department of Biology
in the Graduate School of
The University of Alabama
TUSCALOOSA, ALABAMA
2015
Copyright Adam Jeffrey Constantin 2015
ALL RIGHTS RESERVED
ABSTRACT
Changes in land-use/land-cover (LULC) and predicted changes in environmental
conditions threaten the marginal occupancy of tidal marshes in Weeks Bay, Alabama. In a
combined study, I investigated the implications of historic LULC and coastal wetland plant
distributions on coastal wetland viability in the Weeks Bay area. The historically forested and
agricultural landscape of Baldwin County is being rapidly converted by urban development,
which puts increasing stress on downstream ecosystems. Further, simultaneous losses on the
landward and seaward extent of the intertidal wetland transition may be contributing to coastal
squeeze in this area. Within our study area, 17% of estuarine and marine emergent wetlands were
lost to open water between 1956 and 2001, with over 30% converted to upland forested wetland
(22.2%) and urban development (10.3%). There was also a near-complete loss (95.9%) of
unconsolidated shore to open water. Within the coastal marsh transition of Weeks Bay, there was
a heterogeneous micro-topography with a large overlap of plant distributions by elevation.
However, salinity was found to be an indicator of plant zonation for the three dominant species:
Spartina cynusuroides, Cladium jamaicense, and Juncus roemerianus. At intermediate salinity (3
– 9 psu), dominance alternated between Spartina cynosuroides in lower salinities and Juncus
roemerianus in higher salinities. Because Cladium jamaicense is the least salt-tolerant of the
three dominant wetland plants surveyed, changes in salinity due to altered upland hydrology and
SLR may have the greatest effect on its distribution. Further, since the upland barrier of a
wetland forest may prevent marsh transgression as sea levels rise, the ecotone between wetland
forest and emergent marsh should be an area of specific interest for environmental managers.
ii
Factors that contribute to shoreline erosion or barriers to upslope migration will likely dictate the
health and persistence of intertidal marsh habitat at Weeks Bay and will require special focus
from local managers.
iii
DEDICATION
Dedicated to the lasting memory of my friend and mentor, Griff Blakewood, who taught me the
consequence and reward of being a steward for the natural world.
iv
LIST OF ABBREVIATIONS AND SYMBOLS
α
alpha
%
percent
=
equal to
~
approximately
<
less than
3D
three dimensional
ALDOT
Alabama Department of Transportation
AOI
area of interest
CLJA
Cladium jamaicense
cm
centimeters
CORS
Continuously Operating Reference Station
CSV
Comma-separated file
E and M
Estuarine and Marine
FWS
Fish and Wildlife Service
GCS
Geographic Coordinate System
Ha
hectares
IDW
inverted distance weighted
JURO
Juncus roemerianus
Km
kilometer
km2
square kilometers
LULC
land-use/land-cover
v
m
meters
mL
milliliter
MS
Microsoft
MSL
mean sea level
NAD
North American Datum
UTM
Universal Transverse Mercator
NAVD88
North American Vertical Datum of 1988
NEP
National Estuarine Program
NERR
National Estuarine Research Reserve
NWI
National Wetland Inventory
ρ
Spearman‟s rho
psu
practical salinity unit
r
Pearson correlation efficient
RTK
real-time kinetic
SLAMM
sea-level affecting marsh model
SLR
sea-level rise
SPCY
Spartina cynosuroides
U.S.
United States of America
USGS
United States Geological Survey
WAMS
Wetland Analytical Mapping System
IPCC
Intergovernmental Panel on Climate Change
ESRI
Environmental Systems Research Institute
GPS
Global Positioning System
vi
ACKNOWLEDGEMENTS
For all of her guidance and patience, I sincerely thank my advisor, Dr. Julia Cherry. I also
thank Dr. Whitney Broussard for his advice, patience, and enthusiasm. Additionally, I thank Drs.
Jonathan Benstead and Just Cebrian for guidance during the development of my projects.
A special thanks to Dale Steve Nevitt and Sara Martin for help in the field, as well as
Josh Jones, Dian Schneider, Nigel Temple, and Oliver Wilmot for additional support. Thanks to
Larry Handley and Christopher Wells for GIS data, and to Larry Allain for aid in plant
identification. Thanks to Michael Kendrick for aiding in data analysis, and Scott Phipps and Eric
Brunden at the Weeks Bay NERR for support during the duration of these projects.
Funding for projects was provided by The University of Alabama as research/travel funds
and a graduate teaching assistantship, and from the National Estuarine Research Reserve Science
Collaborative as a research assistantship.
I want to also thank all of my friends, both from The University of Alabama and home in
Louisiana, for shared experiences, memories, and sanity. Lastly, for their unconditional love and
support, I thank my family.
vii
CONTENTS
ABSTRACT………………………………………………………………………………………ii
DEDICATION…………………………………………………………………………………....iv
LIST OF ABBREVIATIONS AND SYMBOLS………………………………………………....v
ACKNOWLEDGEMENTS………………………………………………………..…………….vii
LIST OF TABLES…………………………………………………………………………….......x
LIST OF FIGURES…………………………………………………………………………...….xi
1. AN INTRODUCTION...…………………………………..…………………………………..1
REFERENCES……………………………………………………………………………4
2. HISTORIC LAND-COVER CHANGE IN WEEKS BAY, AL..…………………...…...…...7
INTRODUCTION……………………………………………..……………..…………...7
METHODS……………………………………………..………..…………………..……9
RESULTS…...………………………………………………..…..……………………...12
DISCUSSION…………………………………………………..………….…………….14
REFERENCES…………………………………………………………………………..17
3. INTERTIDAL MARSH PLANT ZONATION IN WEEKS BAY, AL.………………….....32
INTRODUCTION…………………………………………………………………..…...32
METHODS………………………………………………………………..……………..34
RESULTS…………………………………………………………………..……………38
DISCUSSION……………………………………………………………...…………….39
REFERENCES…………………………………………………………………………..43
viii
4. CONCLUSIONS.………………………………………………………..…….…………….54
REFERENCES………………………………………………………………..…………55
ix
LIST OF TABLES
Table 2.1: Land Change in Weeks Bay 1956 – 1979 (hectares)…………….…………...21
Table 2.2: Land Change in Weeks Bay 1979 – 1988 (hectares)…….….…......................22
Table 2.3: Land Change in Weeks Bay 1988 – 2001 (hectares)…….………….………..23
Table 2.4: Land Change in Weeks Bay 1956 – 2001 (hectares)……………….………...24
Table 2.5: Land Change in Weeks Bay 1956 – 2001 (percent)…...…..............................25
Table 2.6: Land-Use/ Land-Cover Loss/Gain 1956 – 2001 (percent)……………...……26
Table 3.1: Plant Species List…..………………………..…….……………………….…46
Table 3.2: Correlation Analysis of Plant Species and Environment..………..……….….48
Table 3.3: Correlation Analysis between Plant Species..………..……………..….…….48
x
LIST OF FIGURES
Figure 2.1: Weeks Bay Land-Cover 1956…………..………………...………………....27
Figure 2.2: Weeks Bay Land-Cover 1979………….....………………………………....28
Figure 2.3: Weeks Bay Land-Cover 1988…………..……………………..……….…....29
Figure 2.4: Weeks Bay Land-Cover 2001…………..…………………………...……....30
Figure 2.5: Weeks Bay LULC Conversion Chart….……..……..…………...………..…31
Figure 3.1: Weeks Bay Survey Map…..….……………………...………………………47
Figure 3.2: Plant Zonation by Elevation and Distance from shoreline……..……………49
Figure 3.3: Weeks Bay Salinity Survey – Juncus roemerianus.…………………………50
Figure 3.4: Weeks Bay Salinity Survey – Spartina cynosuroides ………………………51
Figure 3.5: Weeks Bay Salinity Survey – Cladium jamaicense..……………..…………52
xi
Chapter 1
An Introduction
Coastal wetlands are among the most endangered ecosystems in the world, with sea-level
rise (SLR), eutrophication, and urban development contributing to high rates of degradation and
loss. Within the contiguous United States, it is estimated that 53% of wetlands were lost between
the 1780s and 1980s (Dahl, 1990), and that an average of 89,000 acres/year of wetlands were lost
from 2004-2009, up from 54,000 acres/year from 1998 to 2004 (Dahl, 2006; 2011). These
ecosystems provide a number of important services, including habitat for ecologically and
economically important species, water pollution filtration, carbon sequestration, and shoreline
stability (Mitch, 2007; Gedan et al., 2011; McLeod et al., 2011). Because loss of these wetlands
leads to the loss of the goods and services they provide, their preservation or restoration is a high
priority.
As an ecotone between land and sea, coastal wetlands are influenced by disturbances and
stresses of both marine and terrestrial origin. Marine influences, including SLR and storm surges,
can alter inundation frequencies, depth of flooding, and salinity, while increased storm activity
can also contribute to erosion or scouring (Scavia et al., 2002; Cahoon, 2006; Church et al.,
2013). These influences are predicted to increase in intensity and/or frequency with climate
change. Sea-level rise, which is predicted to contribute to land-loss worldwide (Nicholls, 1999),
is occurring at an accelerating rate that is expected to continue into the 21st century (Church and
White, 2006; Ablain, 2009; Church et al., 2013). In addition, storm frequency and intensity are
expected to increase with higher sea surface temperatures (Emanuel, 2005; Webster et al., 2005;
1
Church et al., 2013). Such accelerations in SLR and storm activity may promote further loss of
coastal wetland ecosystems and the services they provide (Costanza et al., 1997; 2014),
including their ability to offset hurricane impacts to coastal communities (Costanza et al., 2008).
On the landward side, changes in the delivery of freshwater, sediments, and nutrients to
tidal marshes can alter their structure and function. Changes in freshwater supply alter salinity
and flooding, which in turn can alter net ecosystem production (Neubauer, 2011), species
composition, and species richness (Baldwin & Mendelssohn, 1998; Sharpe & Baldwin, 2012).
This can negatively affect ecosystem resilience to environmental shifts associated with global
climate change. Reduced sediment delivery to tidal marshes as a result of dams and levees can
starve them of the sediment needed to maintain elevations relative to sea level (Kirwan and
Megonigal, 2013; Weston, 2013). Nutrient loading may lead to changes in community
composition, plant biomass allocation patterns, rates of production and decomposition, and the
potential for these systems to adjust vertically via root zone expansion (Deegan et al., 2012;
White et al., 2012). With 21% of the human population living within 30 miles of the coast
(Gommes et al., 1997) and increasing at twice the average rate (Bijlsma et al., 1996), subsequent
land development will likely interact with these other changing factors in ways increasingly
significant to coastal management efforts.
Tidal marshes span an elevation gradient along which salinity and tidal inundation vary.
Changes in hydro-edaphic conditions along this elevation gradient result in conspicuous plant
zonation, as species are distributed based on their physiological tolerances to flooding and
salinity (Chapman, 1974; La Peyre et al., 2001). In the southeast United States, much of the
coastal marsh is dominated by two species: Juncus roemerianus and Spartina alterniflora. The
distribution of these plants in marshes is mainly dictated by their respective salinity and
1
inundation tolerances, with the more salt-tolerant S. alterniflora occupying the lower elevation,
fringing marsh zone, and J. roemerianus dominating the higher elevation, less saline upland
marsh (Pennings et. al., 2005; Touchette et. al., 2009). As SLR or storm surges increase,
thresholds for survival for some plant species or communities may be compromised, particularly
at lower elevations, forcing migration of species upland. To predict how species will respond to
future changes in sea level and tidal influence, it is important to understand the elevation and
salinity ranges for various plant species or communities along these coastal transitions.
Given that tidal marshes are increasingly vulnerable to loss, managers need to identify
susceptible areas for targeted management practices and prioritize areas for restoration in ways
that consider the predictions for, and potential responses to, future environmental changes. By
focusing on preserving and fortifying existing marshes in particularly vulnerable locations,
managers may be able to reduce the severity of impacts and minimize losses. Further, restoration
projects that account for anticipated changes in environmental conditions may promote recovery
of lost services while also enhancing coastal resilience to sea-level rise. Therefore, a working
knowledge of the potential causes of wetland loss for a given area, both past and projected, is an
important tool for managers to develop effective coastal management projects in which
ecosystem resilience to climate change is embedded.
In this study, I explored historic land-cover change in Weeks Bay, AL using National
Wetlands Inventory data from 1956, 1979, 1988, and 2001 (Chapter 2). In Chapter 3, I
investigated the distribution of dominant plant species along gradients of elevation, salinity, and
distance from shoreline, for a fine-resolution examination of intertidal wetland plant zonation.
The overall goal of these two studies was to gain insight into the persistence of intertidal
wetlands in the face of increased urban development and SLR.
2
References
Ablain, M., Cazenave, A., Valladeau, G., & Guinehut, S. (2009). A new assessment of the error
budget of global mean sea level rate estimated by satellite altimetry over 1993-2008. Ocean
Science, 5(2).
Baldwin, A. H., & Mendelssohn, I. a. (1998). Effects of salinity and water level on coastal
marshes: an experimental test of disturbance as a catalyst for vegetation change. Aquatic
Botany, 61(4), 255–268.
Bijlsma, L., Ehler, C. N., Klein, R. J. T., Kulshrestha, S. M., McLean, R. F., Mimura, N., ... &
Warrick, R. A. (1996). Coastal zones and small islands.Climate Change 1995: Impacts,
Adaptations, and Mitigation of Climate Change: Scientific-Technical Analyses.
Contribution of Working Group II to the Second Assessment Report of the
Intergovernmental Panel on Climate Change, 289-324.
Cahoon DR (2006) A review of major storm impacts on coastal wetland elevations. Estuaries
and Coasts, 29, 889–898.
Chapman, V.J. (1974) Salt marshes and salt deserts of the world. Ecology of Halophytes (ed.
W.H. Queen), pp. 3–19. Academic Press, New York.
Church, J. a., & White, N. J. (2006). A 20th century acceleration in global sea-level rise.
Geophysical Research Letters, 33(1), L01602.
Costanza, R., and C. Folke. (1997). Valuing ecosystem services with efficiency, fairness and
sustainability as goals. Pages 49-68 in G. Daily, editor. Nature's services: societal
dependence on natural ecosystems. Island Press, Washington, D.C.
Costanza, R., Pérez-Maqueo, O., Martinez, M. L., Sutton, P., Anderson, S. J., & Mulder, K.
(2008). The value of coastal wetlands for hurricane protection. Ambio, 37(4), 241–8.
Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S.J., Kubiszewski, I., Farber,
S. & Turner, R.K. (2014). Changes in the global value of ecosystem services. Global
Environmental Change, 26, 152–158.
Dahl T.E. (1990). Wetlands Losses in the United States 1780‟s to 1980‟s. U.S. Department of the
Interior, Fish and Wildlife Service, Washington, D.C. 112.
Dahl, T.E. (2006). Status and trends of wetlands in the conterminous United States 1998 to 2004.
U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C. 112.
Dahl, T.E. (2011). Status and trends of wetlands in the conterminous United States 2004 to 2009.
U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C. 108.
3
Gedan, K. B., & Bertness, M. D. (2010). How will warming affect the salt marsh foundation
species Spartina patens and its ecological role?. Oecologia, 164(2), 479-487.
Gommes, R., du Guerny, J., Nachtergaele, F., & Brinkman, R. (1997). Potential Impacts of Sealevel Rise on Populations and Agriculture. Food and Agricultural Organization of the
United Nations, Rome.
La Peyre, M. K. G., Grace, J. B., Hahn, E., Mendelssohn, I. A., Ecology, S., & Jan, N. (2001).
The Importance of Competition in Regulating Plant Species Abundance along a Salinity
Gradient. Ecology, 82(1), 62–69.
Mcleod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., & Silliman, B. R.
(2011). A blueprint for blue carbon: toward an improved understanding of the role of
vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the
Environment, 9(10), 552-560.
Mitsch, W.J, & Gosselink, J.G. (2007). Wetlands (4th ed.). Hoboken, NJ: John Wiley & Sons,
Inc.
National Estuarine Research Reserve System, Weeks Bay, AL. http://nerrs.noaa.gov/. Retrieved
February 1, 2014, from http://nerrs.noaa.gov/Reserve.aspx?ResID=WKB
Neubauer, S. C. (2011). Ecosystem Responses of a Tidal Freshwater Marsh Experiencing
Saltwater Intrusion and Altered Hydrology. Estuaries and Coasts, 36(3), 491–507.
Nicholls, R. J., Hoozemans, F. M., & Marchand, M. (1999). Increasing flood risk and wetland
losses due to global sea-level rise: regional and global analyses. Global Environmental
Change, 9, S69-S87.
Pennings, S. C., Grant, M.-B., & Bertness, M. D. (2005). Plant zonation in low-latitude salt
marshes: disentangling the roles of flooding, salinity and competition. Journal of Ecology,
93(1), 159–167.
Sharpe, P. J., & Baldwin, A. H. (2012). Tidal marsh plant community response to sea-level rise:
A mesocosm study. Aquatic Botany, 101, 34–40.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., & Miller, H. L.
(2007). IPCC, 2007: Climate change 2007: The physical science basis. Contribution of
Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate
Change. SD Solomon (Ed.).
Stagg, C. L., & Mendelssohn, I. A. (2011). Controls on resilience and stability in a sedimentsubsidized salt marsh. Ecological Applications, 21(5), 1731-1744.
4
Touchette, B. W., Smith, G. a., Rhodes, K. L., & Poole, M. (2009). Tolerance and avoidance:
Two contrasting physiological responses to salt stress in mature marsh halophytes Juncus
roemerianus Scheele and Spartina alterniflora Loisel. Journal of Experimental Marine
Biology and Ecology, 380(1-2), 106–112.
Weston, N.B. (2013). Declining sediments and rising seas: an unfortunate convergence for tidal
wetlands. Estuaries and Coasts, 37, 1–23.
5
Chapter 2
HISTORIC LAND-COVER CHANGE IN WEEKS BAY, AL
Introduction
In a time of rapidly changing landscapes and climate, measuring land-use/land-cover
(LULC) trends, and understanding their effects on natural ecosystems, is critical for developing
sound environmental conservation and management strategies. It has been estimated that
between one-third and one-half of the Earth‟s land surfaces has been altered by anthropogenic
action (Vitousek, 1997). The impacts of these changes are of particular concern in wetlands,
which represent approximately ~0.3% of the earth‟s surface yet provide approximately 20% of
the estimated total value of global ecosystem services (Costanza et al., 2014). As ecotones
between land and sea, coastal wetlands are exposed to stresses of both terrestrial and marine
origin. In some regions, these land- and sea-ward forces encroach upon coastal wetlands,
shortening the coastal transition in a phenomenon termed “coastal squeeze” (Pethick, 1993;
English Nature, 1994; Kirwan and Megonigal, 2013). On the seaward extreme, rising sea levels,
and wind- and wave-induced erosion can cause losses of wetland habitat to open water. On the
landward extreme, the presence of fixed barriers, whether natural or human-made, limit the
ability of these coastal ecosystems to migrate upland to other habitable area.
Coastal squeeze can be exacerbated by LULC changes that convert natural habitats into
areas for human use. In fact, the cultivation of wetlands for human use has resulted in worldwide
losses of these valuable ecosystems, especially in areas of rapid growth in agriculture and
6
development. Approximately 53% of wetlands were lost in the conterminous United States
between 1780 and 1980 (Dahl, 1990), up to 95% of which were initially converted for
agricultural purposes (Williams, 1990; Meyer, 1992). More recently, these agricultural lands
have largely been converted to urban development (Turner, 1994; Lopez, 2001), rather than
being restored to wetlands or floodplain ecosystems. While riparian and freshwater wetland loss
is largely due to agriculture, much of the land-use conversion of intertidal emergent wetlands is
attributed to urban-industrial-port expansion (Pinder, 1990; Meyer, 1992). Given that
approximately 60% of the human population lives within 100 km of the coast (Vitousek, 1997),
the impacts of human use on coastal wetlands are likely to persist.
In addition to direct conversion, there are indirect effects of LULC changes on the health
of downstream wetland ecosystems. Changes in hydrology, sediment transport, and nutrient
loading to coastal wetlands are often linked to upland LULC. A high percentage of agriculture in
a watershed has been shown to increase sediment and nutrient loading downstream (Osborne and
Wiley, 1988; Crosbie and Chow-Fraser, 1999; Foley, 2005), while a high percentage of
impermeable surfaces associated with urban development has been shown to increase the timing,
magnitude, and temperature of runoff (Booth et al., 2002). In addition, hydrologic modifications,
such as dams and levees, have reduced the supply of sediments to many coastal watersheds along
the Gulf and Atlantic Coasts of the U.S., which when combined with high rates of relative sealevel rise, has contributed to coastal wetland losses (Weston, 2013). Such changes in freshwater
delivery, sediment transport, and nutrient loading to coastal wetlands can alter ecosystem
structure and function by affecting plant species richness, community composition, and/or
primary production, as well as mechanisms of surface elevation maintenance (Kirwan and
7
Megonigal, 2013; McKee, 2011; Nyman et al., 2006). Consequently, these direct and indirect
effects of LULC change alter the quantity and quality of coastal wetlands in the landscape.
In the face of projected increases in development and SLR, it is increasingly important
for coastal managers to measure and understand the dynamics of LULC change when creating
and implementing environmental management strategies. Local information on historic LULC
changes provides valuable information to inform these strategies. To examine the impacts of
these changes on coastal wetlands, I explored the history of LULC change from 1956 - 2001 in
Weeks Bay in Baldwin County, Alabama, U.S.A. Baldwin County, one of the fastest growing
metropolitan areas in the United States, is subject to rapid urban development. This poses a
challenge for local managers tasked with not only supporting economic growth, but also
facilitating a sustainable, healthy environment. This project will provide local government,
environmental managers, and scientists with the background of LULC shifts in Weeks Bay to
inform best management practices and target areas for further monitoring.
Methods
Study Site Description
Weeks Bay is a part of the Bon Secour sub-estuary on the southeast corner of Mobile Bay
in Baldwin County, Alabama. It covers a 26.41-km2 area and is fed primarily by two sources, the
Magnolia River (east) and Fish River (north), with nearly three quarters of the freshwater inflow
coming from the Fish River (NERR, 2008). The Weeks Bay Estuary experiences diurnal,
microtides with a tidal range of 0.3 - 0.5 m (NERR, 2008). Reserve property includes diverse
habitat types distributed along an elevation gradient, spanning tidal salt and brackish marshes on
the seaward extreme, freshwater marsh, upland forest, pitcher plant bogs, and forested wetlands
on the landward extreme.
8
Land-Cover Change
National Wetland Inventory (NWI) datasets from 4 years (1956, 1979, 1988, and 2001)
were used to quantify LULC change, specifically wetland habitat conversion, in the Weeks Bay
area. These years represent the oldest and most recent complete datasets available for this area.
The NWI is a digital data set that describes wetland habitat extent in the United States and is
comprised of digitized 1:24,000 scale maps using the Wetland Analytical Mapping System
(WAMS). Wetlands are classified using the Cowardin classification system (Cowardin et
al., 1979), the official classification system of US Fish and Wildlife Service (FWS) and the
standard for wetland classification. The Cowardin system uses alphanumeric, hierarchical
classification codes that begin with system followed by subsystem, class, sub-class, and
modifier. Upland characteristics were classified using the Anderson classification system
(Anderson et al., 1976) modified by Handley et al. (2007). NWI datasets were acquired from the
USGS National Wetlands Research Center, Lafayette, LA, USA for use in this study.
All data were processed in the North American Datum of 1983, the Universal Transverse
Mercator 16N and GCS North America 1983 geographic coordinate system using ESRI ArcGIS
software (ArcGIS 10.3.1, ESRI, California, USA). Because the original data were separated into
four geographic regions (Bon Secour, Little Point Clear, Magnolia Spring, and Point Clear), the
shapefiles were merged into one file. Datasets for the four years were clipped to their smallest
mutual perimeters to create a consistent spatial extent among datasets, and were then converted
from a vector to a raster to allow for land area comparison. Cell size was set to 1 m to allow for a
high-resolution transcription of data and to minimize discrepancy of conversion. This process
yielded a raster dataset for each year with a habitat classification attribute field.
9
From the Cowardin and Anderson classifications, similar habitats were grouped into
more succinct, manageable categories to quantify LULC change. Cowardin codes were identified
and classified using the “Wetland Code Interpreter” on the FWS website
(http://www.fws.gov/wetlands/NWI/Index.html), the “Wetland Classification Chart,” and the
“Wetland and Deepwater Habitat Mapping Code Table”. The upland codes were crossreferenced with Handley et al. (2007). Codes were then associated with one of 13 categories,
seven for wetland/marine: estuarine and marine (E and M) deepwater; E and M emergent
wetland; E and M unconsolidated shore; fresh emergent wetland; fresh forest/shrub wetland;
freshwater pond; riverine) and six for uplands/other (upland urban/developed; agriculture; upland
forest/shrub; upland barren; and other). When necessary, wetland data were cross-referenced
with the FWS online mapper using a reverse-lookup to determine which classification to use.
Once the attribute fields for each dataset were exported from the raster shapefile to a commaseparated values file (CSV), all classification codes were placed into one column and duplicates
removed. Codes were then classified into one of the 13 categories. Before these data could be
imported into ESRI ArcMap and joined to the NWI datasets, the “/” from classification codes
(e.g., PFO4/1C) had to be removed in the CSV file and in ArcMap. A new field was created
(“HABCLASS”), and using the Field Calculator (“HABCLASS=REPLACE([DATA],”/”,””)), a
new field with the “/” removed was created. Then the CSV was joined to the HABCLASS field.
After these steps, all four of the NWI raster datasets had distinct wetland classification attribute
fields.
The subsequent NWI raster files were then cross-referenced to quantify LULC/habitat
shifts between time periods using the ArcMap Tabulate Area tool in the Spatial Analyst toolset.
Datasets were compared sequentially (1956 – 1979, 1979 – 1988, and 1988 – 2001), as well for
10
total change (1956 – 2001). Feature Zones were entered as the earlier dataset year and Feature
Class as the later dataset year, thus quantifying LULC classification changes from the earlier to
the later dataset for each pixel. This resulted in a cross-tabulated comparison of habitat
classifications between dataset years. The .txt files for each of the comparisons were then opened
in MS Excel and compiled into one workbook. This workbook permitted quantification of habitat
changes from which percentage changes could also be calculated. To create a more concise
illustration of LULC change, a flowchart of total land conversion (Figure 2.5) was created for
eight classifications of interest: E and M deepwater, E and M emergent wetland, E and M
unconsolidated shore, fresh emergent wetland, fresh forest/shrub wetland, upland
urban/developed, agriculture, upland forest/shrub. A table of land classification change (Table
2.6) was also created, with 100% indicating no net change. The unconsolidated shore
classification was only present in the 1956 and 2001 datasets, so loss was only calculated over
the total time period. Maps of the full-calculated area of interest were created for each sequential
year and the total time span of the datasets (Figures 2.1, 2.2, 2.3, and 2.4).
Results
Both freshwater wetlands and E and M emergent wetlands declined in the Weeks Bay
area from 1951 to 2001. Freshwater emergent wetland habitat area declined by 674.95 hectares
(ha) between 1956 and 1979 (Table 2.1; Figures 2.1, 2.2); increased 19.30 ha between 1979 and
1988 (Table 2.2; Figures 2.2, 2.3); and declined by 41.75 ha between 1988 and 2001 (Table 2.3;
Figures 2.3, 2.4), with a total loss of 697.4 ha, or -81.15% (859.433 ha to 162.04 ha), over the
46-yr time span (Tables 2.4, 2.5; Figure 2.5). The Weeks Bay study area lost 51.97 ha of E and
M emergent wetland habitat between 1956 and 1979, 15.65 ha between 1979 and 1988, and
11
39.20 ha between 1988 and 2001 (Tables 2.1, 2.2, 2.3). Total area lost was 106.82 ha, or 44.94 % (237.7 ha to 130.88 ha), between 1956 and 2001 (Table 2.4-2.5).
Upland forest/shrub lost the largest total area during the study period due primarily to
agriculture conversion (1748.49 ha; 30.3%) and upland urban/development (1013.04 ha;
17.6%)(Tables 2.4, 2.5; Figure 2.5). The highest net growth in agriculture was recorded between
1956 and 1979 (2301.05 ha; +22.49%) (Table 2.1). This expansion of agricultural land was
followed by a substantial loss to upland urban/development, with 1800.76 ha of agricultural land
converted to development from 1979 – 2001(Tables 2.2, 2.3). Increases in upland
urban/development were fairly consistent for the 1956 – 1979 (556.56 ha) (Table 2.1) and 1979 –
1988 (491.48 ha) (Table 2.2) time intervals; however, growth in development for 1988 – 2001
was much greater, adding 1614.93 ha of developed land in the study area (Table 2.3). Total
upland urban/developed land increased by 2662.96 ha (+1034.11%) over the entire study period
(Tables 2.4, 2.5, 2.6; Figure 2.4).
Fresh Emergent Wetlands had the lowest retention of any of the land classifications,
excluding unconsolidated shore (Figure 2.4). Freshwater emergent wetland habitat converted to
fresh forest/shrub wetland (342 ha; 39.81%) and to agriculture (284.08 ha; 33.05%) (Tables 2.4,
2.5; Figure 2.5). In total, only 162.04 ha (18.85%) of freshwater emergent wetlands were
retained over the study period (Tables 2.5, 2.6).
Of the E and M emergent wetland habitat lost, 24.59 ha (10.34%) were converted to
upland urban/development and 40.31 ha (16.96%) were converted to E and M deepwater (Tables
2.4, 2.5; Figure 2.5). As with fresh emergent wetlands, a large amount (52.79 ha or 22.21%) was
converted to fresh forest/shrub wetland, but unlike Fresh emergent wetlands, none converted to
agriculture (Tables 2.4, 2.5; Figure 2.5). Also, 3.5 ha (1.5%) of E and M emergent wetlands
12
converted to unconsolidated shore, a habitat that otherwise experienced the greatest proportional
loss of any habitat type, with 228.4 ha, or 95.9%, converted to E and M deepwater (Tables 2.4,
2.5; Figure 2.5). In total, a little over half of E and M emergent wetland habitat (130.88 ha;
55.06%) was retained over the study period, while only 4.1% (1.45 ha) of the unconsolidated
shore was retained (Tables 2.4, 2.6; Figure 2.5).
Discussion
During the 1956 – 2001 period, wetlands were primarily lost to agriculture,
urban/developed, conversion to fresh forest/shrub wetlands on the terrestrial end, and conversion
to open water on the marine end. Of the E and M emergent wetlands, these losses were
consistent with the phenomenon of coastal squeeze (Pethick, 1993; English Nature, 1994;
Kirwan and Megonigal, 2013), which threatens coastal wetlands as sea level rises and upslope
areas are converted into agriculture or developed areas. Such losses in Weeks Bay underscore the
need to protect the remaining wetland habitat from further erosional losses on the seaward
extreme and to restore or assist upslope migration of wetlands on the landward extreme.
Watersheds with a high percentage of agriculture and urban development are associated
with reduced water quality and altered hydrology, specifically decreased landscape permeability
(Booth et al., 2002). Baldwin County, a traditionally agricultural area, is the fastest growing
county in the state and one of the fastest growing counties in the nation, with a 75% population
increase from 1990 to 2007 and an 89% increase in housing units (Handley et al., 2002 ; Barfoot
et al., 2012). This growth is apparent from the boom in agriculture from 1956 -1978and the
steady increase of urban development, specifically between 1988 and 2001, within the Weeks
Bay watershed. A GIS study done by Basnyat et al. (1999), which modeled the effect of LULC
13
in the Fish River Watershed, found that urban development and agriculture were the highest
contributors of nitrate pollution to coastal areas, which is not surprising considering the
prevalence of these LULCs in the area. With the impacts that development and agriculture have
on the environment, and as population increases in the Weeks Bay area, it is important that local
managers strive to accommodate continued growth while maintaining a sustainable landscape.
Although agricultural/urban watersheds are characterized by negative effects on adjacent
or downstream ecosystems (Perry and Vanderklein, 1996; Roth et al., 1996; Groffman et al.,
2003; Wang et al., 2003; Raymond et al., 2007; Broussard and Turner, 2009; Johnston et al.,
2009), there are many management practices that have successfully mitigated their effects in
other watersheds. Management strategies that alter agricultural management practices, such as
reduced fertilizer input, effective irrigation/hydrology, and optimized cultivation, have been
shown to reduce the effects of LULC on downstream ecosystems (Zhang et al., 1996; Ju et al.,
2009). However, these efforts alone do not offer reductions sufficient to ameliorate all of the
negative effects. In many urban/industrial areas, the ability of wetlands to remove nutrients and
other pollutants provides environmental managers with a relatively cheap and efficient water
filtration system (Fetter et al., 1978; Nichols, 1983; Reed and Bastian, 1985; Brix, 1994;
Oostrom and Russeul, 1994; Rousseau et al., 2008). In fact, Day et al. (2004) found that the use
of wetlands for wastewater treatment across the Mississippi Delta has not only reduced effluent
nutrient levels, but also stimulated primary productivity, which may be especially important in
marshes that rely principally on biological mechanisms to maintain surface elevation relative to
sea level (Nyman, 2006; McKee, 2011).
Strategic placement of vegetated riparian buffer zones has been shown to effectively
mitigate storm water runoff events, both in water volume and pollution (Osborne and Wiley,
14
1988; Lowrance et al., 1997; Goetz et al., 2003; Zedler, 2003; Jantz, 2005). An ongoing study at
the Weeks Bay National Estuarine Research Reserve (NERR) measured the costs and benefits of
wetland construction along a tidal stream in terms of vegetative cover and nutrient abatement
under conditions of projected sea-level rise. The preliminary findings of this study suggest that
marsh restoration can achieve desired outcomes with a moderate effort level (50% planting) and
that these efforts may be resilient to SLR expected over the next 25 - 35 years (Cherry and
Cebrian, personal communication). Further funding and implementation of such research into
local management planning would prove to be a valuable contribution to environmental efforts.
Though wetlands losses via conversion to agriculture and development were substantial
at Weeks Bay, the most notable of losses of emergent wetlands resulted from conversion to other
natural habitats. E and M emergent wetland conversion to fresh forest/shrub wetland was the
highest reported for that category, followed by conversion to open water. Because these marshes
occupy such a thin transition between open water and forested wetland, these losses may
contribute to coastal squeeze in Weeks Bay.
Further support for the occurrence of coastal squeeze in Weeks Bay is evident from the
near total (95.9%) conversion of unconsolidated shoreline to open water, which underscores the
importance of vegetation for shoreline stability and may elucidate a history of unsustainable
environmental management in the area. Douglas and Pickel (1999a) measured the
implementation of bulkhead shorelines in Mobile Bay, Alabama and found that by 1997, the
shoreline between Fairhope, AL and Weeks Bay was almost completely dominated by
bulkheads, and that in all of Mobile Bay, 15% to 30% of intertidal habitat in front of bulkheads
was lost. In our study, most of the unconsolidated shoreline loss that occurred fell within this
area. Thus, the use of hardened shorelines, such as bulkheads, may have contributed to losses of
15
unconsolidated shoreline in the Weeks Bay area since 1956. Beach nourishment has proven to be
a relatively effective solution to shoreline stabilization where bulkheads prevent inland migration
of vegetation (Douglas and Pickel, 1999b), and represents one possible management strategy to
restore these areas. However, the vulnerability of E and M wetlands in Weeks Bay warrants
further investigation, including examinations of management impacts on land loss and highresolution ecosystem dynamics along the coastal transition.
Over the time period of our study, intertidal wetlands in Weeks Bay were exposed to
impacts and losses from both marine and terrestrial origins. Direct conversion to anthropogenic
land-uses, combined with encroachment by forested wetlands and erosional loss to open water,
contributed to significant loss to intertidal wetland habitat in the area. Continued monitoring of
upland LULC characteristics and trends, along with the implementation of best management
practices and mitigation projects, would allow local managers to develop strategies that support
the persistence of coastal wetland habitat in Weeks Bay.
References
Anderson, J. R. (1976). A land-use and land-cover classification system for use with remote
sensor data (Vol. 964). US Government Printing Office.
Barfoot, K., Foster, D., Herder, T., Lowther, B., Miller, C., O'Keefe, K., & Swann, R. (2012).
The Comprehensive Conservation Management Plan Baldwin and Mobile Counties,
Alabama 2013-2018. Retrieved January 6, 2015, from
http://www.mobilebaynep.com/images/uploads/library/CCMP_Draft_Complete.pdf
Basnyat, P., Teeter, L. D., Flynn, K. M., & Lockaby, B. G. (1999). Relationships between
landscape characteristics and nonpoint source pollution inputs to coastal estuaries.
Environmental Management, 23(4), 539-549.
Booth, D. B., Hartley, D., & Jackson, R. (2002). FOREST COVER, IMPERVIOUS‐SURFACE
AREA, AND THE MITIGATION OF STORMWATER IMPACTS. JAWRA Journal of the
American Water Resources Association, 38(3), 835-845.
Brix, H. (1994). Use of constructed wetlands in water pollution control: historical development,
present status, and future perspectives. Water Science and Technology, 30(8), 209-224.
16
Broussard, W., & Turner, R. E. (2009). A century of changing land-use and water-quality
relationships in the continental US. Frontiers in Ecology and the Environment, 7(6), 302307.
Church JA, Clark PU, Cazenave A, et al. (2013) Sea level change. In: Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.
Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,, pp. 1137–1216. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA.
Costanza, R., Pérez-Maqueo, O., Martinez, M. L., Sutton, P., Anderson, S. J., & Mulder, K.
(2008). The value of coastal wetlands for hurricane protection. AMBIO: A Journal of the
Human Environment, 37(4), 241-248.
Crosbie, B., & Chow-Fraser, P. (1999). Percentage land-use in the watershed determines the
water and sediment quality of 22 marshes in the Great Lakes basin. Canadian Journal of
Fisheries and Aquatic Sciences, 56(10), 1781-1791.
Dahl, T. E. (1990). Wetlands loss since the revolution. National Wetlands Newsletter, 12, 16-17.
Day, J. W., Ko, J. Y., Rybczyk, J., Sabins, D., Bean, R., Berthelot, G., & Twilley, R. (2004). The
use of wetlands in the Mississippi Delta for wastewater assimilation: a review. Ocean &
Coastal Management, 47(11), 671-691.
Douglass, S. L., & Pickel, B. H. (1999a). The Tide Doesn't Go Out Anymore- The Effect of
Bulkheads on Urban Bay Shorelines. Shore & Beach, 67(2), 19-25.
Douglass, S. L., & Pickel, B. H. (1999b). Headland Beach Construction on Bay Shorelines. Proc.
12th Annual National Beach Preservation Technology, St. Petersburg, FL.
English Nature (1992) Coastal Zone Conservation. English Nature's Rationale, Objectives and
Practical Recommendations. English Nature, Peterborough, UK.
Fetter Jr, C. W., Sloey, W. E., & Spangler, F. L. (1978). Use of a natural marsh for wastewater
polishing. Water Pollution Control Federation, 290-307.Turner 1994,
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., & Snyder, P. K.
(2005). Global consequences of land-use. Science, 309(5734), 570-574.
Goetz, S. J., Wright, R. K., Smith, A. J., Zinecker, E., & Schaub, E. (2003). IKONOS imagery
for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in
the mid-Atlantic region. Remote sensing of environment, 88(1), 195-208.
Groffman, P. M., Bain, D. J., Band, L. E., Belt, K. T., Brush, G. S., Grove, J. M., & Zipperer, W.
C. (2003). Down by the riverside: urban riparian ecology. Frontiers in Ecology and the
Environment, 1(6), 315-321.
17
Handley, L., Altsman, D., & DeMay, R. (2007). Statewide Summary for Alabama. Seagrass
Status and Trends in the Northern Gulf of Mexico: 1940-2002: US Geological Survey
Scientific Investigations Report 2006-5287 and US Environmental Protection Agency 855-R04-003.
Jantz, P., Goetz, S., & Jantz, C. (2005). Urbanization and the loss of resource lands in the
Chesapeake Bay watershed. Environmental Management, 36(6), 808-825.
Johnston, C. A., Zedler, J. B., Tulbure, M. G., Frieswyk, C. B., Bedford, B. L., & Vaccaro, L.
(2009). A unifying approach for evaluating the condition of wetland plant communities and
identifying related stressors. Ecological Applications, 19(7), 1739-1757.
Ju, X. T., Xing, G. X., Chen, X. P., Zhang, S. L., Zhang, L. J., Liu, X. J., & Zhang, F. S. (2009).
Reducing environmental risk by improving N management in intensive Chinese agricultural
systems. Proceedings of the National Academy of Sciences, 106(9), 3041-3046.
Kirwan, M. L., & Megonigal, J. P. (2013). Tidal wetland stability in the face of human impacts
and sea-level rise. Nature, 504(7478), 53-60.
Lowrance, R., Altier, L. S., Newbold, J. D., Schnabel, R. R., Groffman, P. M., Denver, J. M., &
Todd, A. H. (1997). Water quality functions of riparian forest buffers in Chesapeake Bay
watersheds. Environmental Management, 21(5), 687-712.
McKee, K. L. (2011). Biophysical controls on accretion and elevation change in Caribbean
mangrove ecosystems. Estuarine, Coastal and Shelf Science, 91(4), 475-483.
Meyer, W. B., & Turner, B. L. (1992). Human population growth and global land-use/cover
change. Annual Review of Ecology and Systematics, 39-61.
National Estuarine Research Reserve System, Weeks Bay, AL. http://nerrs.noaa.gov/. Retrieved
February 1, 2014, from http://nerrs.noaa.gov/Reserve.aspx?ResID=WKB
Nichols, D. S. (1983). Capacity of natural wetlands to remove nutrients from wastewater. Water
Pollution Control Federation, 495-505.
Nyman, J. A., Walters, R. J., Delaune, R. D., & Patrick, W. H. (2006). Marsh vertical accretion
via vegetative growth. Estuarine, Coastal and Shelf Science, 69(3), 370-380.
Osborne, L. L., & Wiley, M. J. (1988). Empirical relationships between land-use/cover and
stream water quality in an agricultural watershed. Journal of Environmental Management,
26(1), 9-27.
Perry, J., & Vanderklein, E. L. (2009). Water quality: management of a natural resource. John
Wiley & Sons.
18
Pethick, J. (1993). Shoreline adjustments and coastal management: physical and biological
processes under accelerated sea-level rise. Geographical Journal, 162-168.
Pinder, D. A., & Witherick, M. E. (1990). Port industrialization, urbanization and wetland loss.
Wetlands: a threatened landscape, 235-66.
Raymond, P. A., Oh, N., Turner, G., & Broussard, W. (2007, December). Anthropogenic
alteration of riverine water and carbonate fluxes. In AGU Fall Meeting Abstracts (Vol. 1, p.
1264).
Reed, S. C., & Bastian, R. K. (1985). Wetlands for wastewater treatment: an engineering
perspective. Ecological Considerations in Wetlands Treatment of Municipal Wastewaters.
Van Nostrand Reinhold Company New York. 1985. p 444-450, 3 ref.
Roth, N. E., Allan, J. D., & Erickson, D. L. (1996). Landscape influences on stream biotic
integrity assessed at multiple spatial scales. Landscape Ecology, 11(3), 141-156.
Rousseau, D. P. L., Lesage, E., Story, A., Vanrolleghem, P. A., & De Pauw, N. (2008).
Constructed wetlands for water reclamation. Desalination, 218(1), 181-189.
Scavia D, Field JC, Boesch DF et al. (2002) Climate change impacts on U. S. coastal and marine
ecosystems. Estuaries, 25, 149–164.
Van Oostrom, A. J., & Russell, J. M. (1994). Denitrification in constructed wastewater wetlands
receiving high concentrations of nitrate. Water Science & Technology, 29(4), 7-14.
Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human domination of
Earth's ecosystems. Science, 277(5325), 494-499.
Wang, L., Lyons, J., & Kanehl, P. (2003). Impacts of urban land-cover on trout streams in
Wisconsin and Minnesota. Transactions of the American Fisheries Society, 132(5), 825-839.
Williams, J. R., Llewelyn, R. V., & Barnaby, G. A. (1990). Risk analysis of tillage alternatives
with government programs. American Journal of Agricultural Economics, 72(1), 172-181.
Zedler, J. B. (2003). Wetlands at your service: reducing impacts of agriculture at the watershed
scale. Frontiers in Ecology and the Environment, 1(2), 65-72.
Zhang, W. L., Tian, Z. X., Zhang, N., & Li, X. Q. (1996). Nitrate pollution of groundwater in
northern China. Agriculture, Ecosystems & Environment, 59(3), 223-231.
19
Table 2.1: LULC classification matrix of changes from 1956 - 1979 in the Weeks Bay area in Baldwin County, Alabama. Rows show
the 1956 values of land-cover quantity in hectares for different LULC categories and columns show the 1979 values.
Land Change in Weeks Bay 1956-1979 (hectares)
To
Agriculture
E and M
Deepwater
E and M
Emergent
Wetland
Fresh Emergent
Wetland
Fresh
Forest/Shrub
Wetland
Freshwater
Pond
Other
Riverine
Upland
Upland Barren
Upland
Forest/Shrub
Upland Range
Upland Urban/
Development
Total
8918.1681
0.558
0
0.0108
347.6007
189.5197
4.033
0.0169
1.479
3.7605
2534.6029
385.0156
36.392
0.0247
2959.7854
218.6544
20.4684
1.0081
14.5271
0
0
0.0723
0
2.6653
0
0.8558
0.0919
5.4843
2.7973
144.2221
11.8671
17.1824
0
0
0
0
1.9753
0
2.1116
35.3215
0.0558
0
2.528
68.2957
54.5256
2.0359
0
0
0.0141
17.7114
1.673
2.3266
137.6705
13.7013
1.931
32.5406
330.0321
3198.1776
1.5686
0.3664
12.9204
1.0407
440.4661
57.7296
12.591
13.2611
0
0
0.177
16.2934
8.5787
1.8216
0
0
1.2751
11.9855
0.6225
0.0638
1.0956
0.3123
7.5319
1.4749
5.5836
3.767
0.4551
0
0
0
0.9275
0.0726
0
0
16.3828
0
0.3187
0.3934
8.2958
0.5929
0
54.2052
0
3.8047
0
3.9215
0
0.008
0
0.6556
0
0.2075
0
0
0
0
0
0
0
0.0067
0.0029
0.7616
0.398
0
1.1364
0
0
0
0
12.2765
0
0.1223
397.1825
2.7441
1.2248
5.4138
40.8789
276.6981
0.3995
0
2.36
2.4458
2399.9438
100.4477
45.7299
32.6689
0
0
0
14.3657
6.1743
0
0
0
0
49.8489
8.1253
0.0304
119.0261
5.0707
5.2492
29.4977
23.1154
156.9212
0
0
1.4945
1.9413
295.2641
23.1202
153.3688
9654.5176
3004.1056
238.1502
237.7056
859.4341
3935.7114
10.9066
0.3833
72.5314
10.4775
5771.472
576.8065
257.5137
1956/1979
Agriculture
E and M Deepwater
E and M Unconsolidated Shore
E and M Emergent Wetland
From
Fresh Emergent Wetland
Fresh Forest/Shrub Wetland
Freshwater Pond
Other
Riverine
Upland Barren
Upland Forest/Shrub
Upland Range
Upland Urban/Development
20
Table 2.2: LULC classification matrix of changes from 1979 - 1988 in the Weeks Bay area in Baldwin County, Alabama. Rows show
the 1979 values of land-cover quantity in hectares for different LULC categories and columns show the 1988 values.
Land Change in Weeks Bay 1979 - 1988 (hectares)
To
Agriculture
E and M
Deepwater
E and M
Emergent
Wetland
Fresh Emergent
Wetland
Fresh
Forest/Shrub
Wetland
Freshwater
Pond
Other
Riverine
Upland Barren
Upland
Forest/Shrub
Upland Range
Upland Urban/
Development
Total
10800.5303
0.2262
0
66.7591
185.1375
21.6378
1.5236
1.1341
0
0.4977
370.6391
39.4379
63.0374
0.6584
3175.1172
15.1274
0.0015
29.3034
0.2095
11.6678
14.3222
0.2155
0.4455
4.4719
0
24.2588
0
9.9232
125.9568
0
21.9571
0
0.8054
0
0.062
0
3.0195
0
8.3607
40.6081
0
14.3571
70.9136
65.0749
4.2298
2.1199
0
0
0
4.7236
0.6332
1.1251
311.4417
13.9482
22.2015
27.9114
3554.3181
5.2462
3.4669
8.1548
0.1428
0.4591
363.1394
25.5726
47.0848
17.8744
0
0.0235
5.8044
7.6928
12.5829
0.1613
3.3248
0
0.2563
4.6692
1.9195
1.9808
0.9489
0
0
0.0347
0
0
0.6783
0
0
0
0
0
0
0.4644
0.0703
0
0
11.0478
0
0
45.2194
0
0
1.9038
0
3.166
41.1321
0.0439
0.0465
0.02
1.0077
0.5
0
0.1268
0
8.1158
16.0955
0
1.7127
588.1008
4.4953
3.2666
7.0796
300.6784
4.7207
0.1476
2.0357
0
4.0907
2210.0757
28.3705
162.6756
119.747
0.2464
0.2986
1.4604
5.5863
2.8048
0
0.0701
0
0
33.3785
15.2799
57.6167
499.6543
13.9907
4.454
4.5027
58.9322
2.147
0.6497
13.5271
0.4508
0.8393
263.3531
0
443.0506
12421.1604
3218.0614
185.732
184.4874
4240.7362
54.0787
21.2205
87.915
0.8711
14.7044
3275.4693
111.2136
814.0692
1979/1988
Agriculture
E and M Deepwater
E and M Emergent Wetland
Fresh Emergent Wetland
From
Fresh Forest/Shrub Wetland
Freshwater Pond
Other
Riverine
Upland
Upland Barren
Upland Forest/Shrub
Upland Range
Upland Urban/Development
21
Table 2.3: LULC classification matrix of changes from 1988 - 2001 in the Weeks Bay area in Baldwin County, Alabama. Rows show
the 1988 values of land-cover quantity in hectares for different LULC categories and columns show the 2001 values.
Land Change in Weeks Bay 1988-2001 (hectares)
To
Agriculture
E and M
Deepwater
E and M
Unconsolidated
Shore
E and M
Emergent
Wetland
Fresh Emergent
Wetland
Fresh
Forest/Shrub
Wetland
Freshwater
Pond
Other
Riverine
8076.6101
0
0
32.7833
64.2929
11.1107
0.3251
0
0.7381
66.5292
27.1253
56.3344
0.257
3234.7719
16.2222
2.3984
23.404
0
0
2.7996
0.0344
3.9598
0.0545
28.2378
0
4.9926
0.9145
0.0335
1.7329
0
0
0.0699
0
0.6631
0
2.9669
0
7.1931
110.3757
0.0409
10.9781
0
0
0
0.0085
0.71
0
1.5768
24.0704
0.1765
2.2689
78.6155
44.8824
1.9588
0
0
0.5989
7.6903
0.1517
1.6224
165.8026
13.9409
31.6151
52.2561
3244.6247
4.4612
0.4913
4.1213
0.0729
160.9776
4.6476
31.691
26.4895
2.8242
0.1608
7.346
20.3016
21.2105
0.4757
0
0.4269
7.2286
3.4748
3.5602
0.7693
0
0
0.0109
0.1075
0.5704
0.3183
0
0
0.0863
0
0.236
0.6927
0
0
0.2427
10.9007
4.8612
0
50.9577
0.0221
1.0273
0
4.1381
1988/2001
Agriculture
E and M Deepwater
E and M Emergent Wetland
Fresh Emergent Wetland
From
Fresh Forest/Shrub Wetland
Freshwater Pond
Other
Riverine
Upland Barren
Upland Forest/Shrub
Upland Range
Upland Urban/Development
22
Transportation Upland Barren
4.633
0
0
0
0
0
0
0
0
0
0
0
30.6599
0.2578
0
2.6725
7.0096
0.2367
0
0
0.7272
5.265
5.3995
3.0199
Upland
Forest/Shrub
Upland Range
Upland Urban/
Development
Total
1399.5534
1.5058
2.1447
18.0583
796.213
5.3292
0
1.0969
7.5454
2459.6214
50.8878
120.5559
524.5441
0.8447
0.0884
3.0582
46.8674
1.6701
0.0515
0
52.3546
189.2667
116.2388
21.0682
1296.4748
9.2915
6.2944
6.2691
111.771
4.881
0
2.8263
6.272
412.7114
28.5087
1030.5438
11550.5568
3275.799
170.0847
203.7854
4383.0858
56.2898
1.6619
61.8717
68.801
3315.7367
236.4887
1305.5514
Table 2.4: LULC classification matrix of changes from 1956 - 2001 in the Weeks Bay area in Baldwin County, Alabama. Rows show
the 1956 values of land-cover quantity in hectares for different LULC categories and columns show the 2001 values.
Land Change in Weeks Bay 1956-2001 (hectares)
To
Agriculture
E and M
Deepwater
E and M
Unconsolidated
Shore
E and M
Emergent
Wetland
Fresh Emergent
Wetland
Fresh
Forest/Shrub
Wetland
Freshwater
Pond
Other
Riverine
6442.4571
0
0
0
259.2775
99.8154
2.8559
0
0
0.0067
1333.1501
190.4641
7.8199
0.1564
2979.0609
228.3952
40.3142
7.1098
41.7794
0
0
2.5162
0
7.4711
0.1145
5.3365
0
0.8925
1.446
3.5296
0
1.9621
0
0
0.0154
0
3.5278
0
0
0
4.2843
1.0748
110.2542
0.15
12.0902
0
0
0
0
1.5172
0
1.5124
22.483
0.0988
0
2.9408
48.2822
73.6032
0.8104
0
0
0.0977
12.8282
1.2184
0.8915
183.0234
9.9457
3.5046
52.7946
342.183
2817.6634
2.6883
0.1973
6.3604
0.4522
284.9127
30.3452
10.9764
21.9526
0
0
0.1834
20.1553
25.6182
2.1695
0
0.0333
1.3077
20.7355
1.5621
1.3435
0.6284
0
0
0
0.3875
0.4641
0.1524
0
0
0
0.4663
0
0
0.5142
0
0
0
0.1555
12.394
0.0851
0
55.9312
0
2.7077
0.2029
1.0548
1956/2001
Agriculture
E and M Deepwater
E and M Unconsolidated
E and M Emergent Wetland
From
Fresh Emergent Wetland
Fresh Forest/Shrub Wetland
Freshwater Pond
Other
Riverine
Upland Barren
Upland Forest/Shrub
Upland Range
Upland Urban/Development
23
Transportation Upland Barren
4.4641
0
0
0
0
0.035
0
0
0
0
0.1339
0
0
26.8253
0
0.041
0
4.6852
6.239
0.0466
0
0
0.482
15.0435
3.1407
1.8855
Upland
Forest/Shrub
Upland Range
Upland Urban/
Development
Total
1449.0886
2.5833
0.2592
3.1002
113.2189
594.6184
0.9279
0.0539
2.2952
5.1154
2660.7264
178.2748
30.5237
345.6132
0
0
0
24.7975
39.5499
0.1614
0.1321
0.0455
0.3771
415.3424
111.2759
18.7576
1372.1662
7.24
3.4294
24.5886
39.0306
209.8785
1.0091
0
5.3342
2.6387
1012.9086
60.2077
177.4119
9869.3725
3004.1055
238.1502
237.7056
859.433
3935.7108
10.9066
0.3833
72.5314
10.4775
5771.4714
576.8063
257.5137
Table 2.5: LULC classification matrix of percent changes from 1956 - 2001 in the Weeks Bay area in Baldwin County, Alabama.
Rows show the 1956 values of land-cover percent for different LULC categories and columns show the 2001 values.
Land Change in Weeks Bay 1956-2001 (percent)
To
Agriculture
E and M
Deepwater
E and M
Unconsolidated
Shore
E and M
Emergent
Wetland
Fresh Emergent
Wetland
Fresh
Forest/Shrub
Wetland
Freshwater
Pond
Other
Riverine
65.3%
0.0%
0.0%
0.0%
30.2%
2.5%
26.2%
0.0%
0.0%
0.1%
23.1%
33.0%
3.0%
0.0%
99.2%
95.9%
17.0%
0.8%
1.1%
0.0%
0.0%
3.5%
0.0%
0.1%
0.0%
2.1%
0.0%
0.0%
0.6%
1.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.1%
0.5%
46.4%
0.0%
0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.6%
0.2%
0.0%
0.0%
1.2%
5.6%
1.9%
7.4%
0.0%
0.0%
0.9%
0.2%
0.2%
0.3%
1.9%
0.3%
1.5%
22.2%
39.8%
71.6%
24.6%
51.5%
8.8%
4.3%
4.9%
5.3%
4.3%
0.2%
0.0%
0.0%
0.1%
2.3%
0.7%
19.9%
0.0%
0.0%
12.5%
0.4%
0.3%
0.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.4%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
0.8%
0.0%
77.1%
0.0%
0.0%
0.0%
0.4%
1956/2001
Agriculture
E and M Deepwater
E and M Unconsolidated
E and M Emergent Wetland
From
Fresh Emergent Wetland
Fresh Forest/Shrub Wetland
Freshwater Pond
Other
Riverine
Upland Barren
Upland Forest/Shrub
Upland Range
Upland Urban/Development
24
Transportation Upland Barren
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
0.0%
0.0%
0.0%
0.5%
0.2%
0.4%
0.0%
0.0%
4.6%
0.3%
0.5%
0.7%
Upland
Forest/Shrub
Upland Range
Upland Urban/
Development
14.7%
0.1%
0.1%
1.3%
13.2%
15.1%
8.5%
14.1%
3.2%
48.8%
46.1%
30.9%
11.9%
3.5%
0.0%
0.0%
0.0%
2.9%
1.0%
1.5%
34.5%
0.1%
3.6%
7.2%
19.3%
7.3%
13.9%
0.2%
1.4%
10.3%
4.5%
5.3%
9.3%
0.0%
7.4%
25.2%
17.6%
10.4%
68.9%
Table 2.6: Total LULC classification loss/gain from 1956 – 2001 for 13 classifications identified
in the Weeks Bay, AL watershed. Numbers greater than 100% indicate a net gain.
25
Figure 2.1: Map of 1956 LULC categories by color in Weeks Bay, AL. AOI chosen based on extent of NWI dataset and proximity to
the bay.
26
Figure 2.2: Map of 1979 LULC categories by color in Weeks Bay, AL. AOI chosen based on extent of NWI dataset and proximity to
the bay.
27
Figure 2.3: Map of 1988 LULC categories by color in Weeks Bay, AL. AOI chosen based on extent of NWI dataset and proximity to
the bay.
28
Figure 2.4 Map of 1979 LULC categories by color in Weeks Bay, AL. AOI chosen based on extent of NWI dataset and proximity to
the bay.
29
Figure 2.5 Conversion of eight key LULC classifications in Area of Interest surrounding Weeks Bay, AL from 1956 – 2001. Black
circles represent extent of cover in 1956 and colored circles represent the extent in 2001, such that a colored circle inside a black circle
represents a nets loss and vice versa. Because increases were too great to fit within the figure, the developed colored circle was
reduced for visibility. Arrows are weighted proportionally to indicate percent change.
30
Chapter 3
INTERTIDAL MARSH PLANT ZONATION IN WEEKS BAY, AL
Introduction
Coastal wetlands provide a wealth of valuable ecosystem services, including crucial
habitat, water filtration, carbon sequestration, and shoreline stability (Mitch 2007, Gedan et al.
2011, McLeod et al. 2011). Therefore, wetland loss also means loss of these goods and services.
Because these ecosystems are among the most endangered in the world (Dahl, 1990; 2011), their
preservation and restoration is a high priority for environmental managers. Because they are
located at or near sea level, accelerating rates of global mean sea-level rise (SLR) are among the
greatest threats to these intertidal ecosystems (Church et al., 2013). By increasing inundation
frequencies and depth, and facilitating ecosystem-wide shifts in salinity, eustatic SLR threatens
to radically alter coastal wetlands worldwide.
Coastal wetland plant communities have many characteristics that make them resilient to
the negative effects of SLR. Flooding stress can alter species composition to favor more floodtolerant species, and whole plant communities can migrate landward into new habitat. In fact,
some estimates show a potential increase in areas inhabitable by coastal wetlands as sea levels
rise (Kirwan and Megonigal, 2013). However, in the presence of a fixed upland barrier (e.g. a
hard bulkhead shoreline or upland ridge), landward migration can be restricted, and a
phenomenon known as “coastal squeeze” can occur (Pethick, 1993; English Nature, 1994). With
31
SLR and pervasive anthropogenic development on or near the coast (Vitousek, 1997), this
coastal squeeze phenomenon is being observed worldwide, and as such, is a topic of particular
interest to environmental managers in highly developed/developing coastal regions (Kirwan and
Megonigal, 2013).
As coastal squeeze occurs and the area of the inhabitable marsh zone is reduced,
gradients of environmental stress along the coastal transition can be exaggerated. Coastal
wetlands often exhibit conspicuous plant zonation patterns (Chapman, 1976; Nixon, 1982) that
are driven by changes in hydro-edaphic conditions along the elevation gradient from the sea to
upland environments (Zedler, 1977; Nixon, 1982; Earle and Kershaw, 1989). Ultimately,
species‟ distributions are determined by their physiological tolerances to changes in flooding and
salinity occurring within the coastal transition (Chapman, 1974; Cooper, 1982; Earle and
Kershaw, 1988; Odum, 1988; Brewer and Grace, 1990; La Peyre et al., 2001). Plant zonation
along environmental gradients also arises in response to trade-offs between competitive ability
and stress tolerance; i.e., as stress declines, interspecific competition increases and has a greater
influence on community structure (Grime, 1977; 1979). Therefore, an increase in environmental
stress along a coastal transition may favor more stress-tolerant species, potentially limiting
species richness or plant diversity and reducing ecosystem resilience (Peterson et al., 1998; Diaz
and Cabido, 2001; Elmqvist et al., 2003, Stagg and Mendelssohn, 2011). In the face of rapidly
changing environmental conditions associated with SLR, changes in species‟ distributions and
subsequent impairment of wetland ecosystem resilience is an increasingly relevant topic for
researchers and environmental managers.
32
There have been numerous efforts to model and predict the response of coastal
ecosystems to SLR projections. Among the most widely implemented of these efforts is the Sea
Level Affecting Marsh Model (SLAMM). The latest version of SLAMM (SLAMM 6.2) includes
six input parameters: inundation, erosion, overwash, saturation, accretion, and salinity. This
model is used to inform management projects of more than 100 National Wildlife Refuges in the
United States. Although SLAMM is a useful tool for environmental managers, calculations of
marsh loss/conversion are based on general plant community categories based on broad stress
tolerance ranges, which overlook overlapping ranges or species-specific responses to change,
and consequently, can overestimate total wetland loss (Kirwan and Guntenspergen, 2009).
Therefore, site-specific surveys of plant species‟ distributions along environmental stress
gradients can inform managers of potential responses within a community. This knowledge
remains a valuable asset to coastal management practices in the face of rising sea levels.
To explore the environmental drivers of plant species zonation in an intertidal coastal
wetland, I surveyed marsh plant dominance along gradients of elevation and salinity in Weeks
Bay, Alabama. Coastal wetlands in this area occupy an already narrow transition, and are
therefore susceptible to changes in environmental conditions associated with SLR and coastal
squeeze. This survey will provide information on local plant zonation in the Mobile Bay
complex, which can inform future environmental management efforts along the Gulf Coast.
Methods
Study Site Description
Weeks Bay is a part of the Bon Secour sub-estuary on the southeast corner of Mobile Bay
in Baldwin County, Alabama, USA. It covers a 26.41-km2 area and is fed primarily by two
33
sources, the Magnolia River to the east and Fish River to the north, with nearly three quarters of
the freshwater inflow coming from the Fish River (NERR, 2008). Study areas were selected from
nearly 10.12 km2 of reserve-owned property surrounding Weeks Bay. Of this property, 6.03,
2.83, and 1.82 km2 border Mobile Bay, Weeks Bay, and the Fish River, respectively (Fig. 1). The
Weeks Bay Estuary is microtidal, with a tidal range of 0.3 to 0.5 meters (NERR, 2008). The
focus of this study was on a narrow band of intertidal marshes near the mouth of the Fish River
on the west side of the Bay. This area of intertidal marsh included three main vegetative zones
dominated by Cladium jamaicense (swamp sawgrass) in freshwater areas, Juncus roemerianus
(black needlerush) in the intermediate zone, and Spartina alterniflora (smooth cordgrass) in the
fringe/shoreline marsh. Other key species in these areas include: Spartina patens (salt meadow
cordgrass); Sagittaria lancifolia (duck potato/bull tongue); Spartina cynosuroides (big
cordgrass); Symphyotrichum subulatum (salt marsh aster); and Distichlis spicata (saltgrass)
(NERR, 2008).
Field Surveys of Species’ Distributions
Vegetation surveys were conducted along transects running perpendicular to the shoreline
in an intertidal marsh at Weeks Bay (Figure 3.1). The specific survey area was chosen based on
accessibility and the presence of contiguous marsh. The Weeks Bay NERR walkway and
observation deck allowed access to and from the survey site. A field map including approximate
transect locations was created beforehand and used for reference during surveys. To create
parallel transects that were approximately perpendicular to the shoreline, a 350-degree bearing
from the southwest piling of the observation deck was established as the initial reference transect
from which all transect lines were based. Initially, all survey points were taken at 20-m
34
frequency along nine transects, with a 40-meter interval between transects (Figure 3.1). This was
done to ensure that a representative profile of the emergent marsh within the area of interest
would be surveyed. Once the breadth of the survey area had been covered, four intermediate
transects were sampled at a 10-m frequency (Figure 3.1). This increase in sampling frequency
ensured that plant species zones were fully recorded.
Measurements of elevation and marsh plant percent cover were taken at each sampling
point along all transects. Elevation was measured using a GPS Real-time Kinetic (RTK) device
(Trimble VRS Rover), which connected to the Alabama Department of Transportation (ALDOT)
Continuously Operating Reference Stations (CORS) network. This provided real-time, autocorrected elevation readings for each survey point. The GPS RTK was tested at a knownelevation benchmark near the sampling area, from which it was determined that <3cm accuracy
could be acquired consistently within 180 epochs (RTK readings). Elevation readings were
recorded using the NAVD88 datum. Because local mean sea level (MSL) data were not available
for this site, the NAVD88 numbers were used for all analysis. The closest tide station with
available NAVD88 relative to local MSL was Dauphin Island, which showed that local MSL
was +1.8 cm NAVD88. Therefore, the NAVD88 elevation readings for Weeks Bay were
assumed to be ±1.8 cm relative to local MSL and a sufficient representation.
Vascular plant species percent cover was measured visually using a 1-m2 quadrat at each
sampling point. The quadrat was randomly placed within a meter of the RTK device while
elevation was being sampled. Percent cover by plant species was recorded for all species with
standing aboveground biomass present within the quadrat. Any other species that were not in the
quadrat, but were observed within approximately 3 m of the sampling location, were also
35
recorded. Unknown plants were pressed for subsequent identification with the help of Godfrey
and Wooten (1979, 1981), the USDA plant database (plants.usda.gov), and Larry Allain, resident
botanist at the USGS National Wetlands Research Center in Lafayette, Louisiana.
Once survey data were collected and uploaded, they were plotted by elevation and
distance from shoreline. Distance from shoreline was calculated in ESRI ArcMap (ArcGIS
10.3.1, ESRI, California, USA) using a created vector shoreline shapefile. Once data were
plotted by elevation and distance from shoreline, spatial representations of marsh plant species‟
distributions along vertical and horizontal planes were available. Elevation generally increased
from shore to inland, but with microtopographic variations that resulted in a lack of clear plant
zones by elevation.
Porewater salinity was also sampled to determine if zonation patterns corresponded to
changes in salinity. Porewater was sampled along the same transects as the elevation and percent
cover surveys. Samples of at least 40 mL of porewater were collected using a sipper tube
inserted vertically into the soil to a depth of 15cm, from which salinity (psu) was determined
using a YSI conductivity meter (YSI 3100, YSI Inc., Yellow Springs, Ohio). Previous transects
were followed as closely as possible with samples taken at a 10-meter frequency and GPS points
recorded for each new sample site. GPS points and associated salinity values were then imported
into ArcMap as a point shapefile. Because the points from the salinity survey did not match
exactly with that of the previous survey, an inverse distance weighted (IDW) raster interpolation
was performed to create a 2D surface model of salinity. Salinity data were then extracted from
the resulting raster by points from the previous survey. Each survey point then had an associated
value for species percent cover, elevation, salinity, and distance from shoreline.
36
I examined how environmental conditions (e.g., elevation, salinity, distance from
shoreline) related to each other and to species‟ percent cover using separate correlation analyses
for the three dominant species: Cladium jamaicense (CLJA), Juncus roemerianus (JURO), and
Spartina cynosuroides (SPCY). Correlations with percent cover were examined using data from
all 101 survey points across the full ranges of elevation, salinity and distance from shoreline to
detect general patterns of species‟ distribution within the coastal transition. I also examined
correlations between species using data from all 101 survey points to elucidate relationships of
cohabitation between species. Data for all correlations violated the normality assumption for
parametric tests, necessitating the use of non-parametric Spearman‟s rho correlations. All
analyses were performed in JMP v10.0 (SAS Institute, Cary, NC, USA) and tested at the α =
0.05 level.
Results
In the surveyed vegetated marsh, elevation ranged from -0.474 – 0.661 m (NAVD 88),
with the lowest elevation occurring along the shoreline (Table 3.1; Figure 3.2). Inland of the
subtidal area, there was a ~0.3m increase in elevation, marking a berm that ran parallel to the
shore. Inland of the berm, elevation ranged between 0.2 – 0.6 meters, generally increasing with
increasing distance from shoreline (ρ = 0.47; p < 0.0001), although there was heterogeneous
micro-topography throughout the coastal transition (Figure 3.2).
Of the dominant plant species, J. roemerianus had the largest elevation range (-0.228 –
0.586 m), with higher percent cover in lower elevation, saltier areas nearer the shore (Table 3.1,
Figures 3.2, 3.3). Generally, its distribution throughout the coastal transition declined with
37
increasing elevation (ρ = -0.45; p < 0.0001) and distance from shoreline (ρ = -0.20; p = 0.04),
but increased with increasing salinity (ρ = 0.33; p = 0.0008) (Table 3.2).
Spartina cynosuroides had the next largest elevation range (-0.066 – 0.636 m), with
higher percent cover in areas along and near the berm (Table 3.1; Figures 3.2, 3.4). Its cover
throughout the coastal transition declined with increasing distance from shoreline (ρ = -0.45; p <
0.0001), but increased with increasing salinity (ρ = 0.29, p = 0.003) (Table 3.2). Unlike J.
roemerianus, S. cynosuroides distribution was not significantly related to elevation (Table 3.2; ρ
= 0.32; p = 0.75), although it did occupy elevations up to 0.05m higher than J. roemerianus.
C. jamaicense occupied the smallest elevation range (0.330 – 0.586 m), with greater
cover at higher elevations further inland (Table 3.1; Figures 3.2, 3.5). Its percent cover increased
with increasing elevation (ρ = 0.20; p =0.04), decreased with increasing salinity (ρ = -0.41; p <
0.0001), and increased with distance from shoreline (ρ = 0.76; p < 0.0001). Furthermore, S.
cynosuroides and C. jamaicense were spatially segregated, with C. jamaicense cover increasing
as S. cynosuroides cover decreased (r = -0.59; p < 0.0001) (Table 3.3). While J. roemerianus and
S. cyosuroides occupied similar elevation and salinity ranges (Table 3.1), J. roemerianus cover
tended to increase as S. cynosuroides cover decreased, although this relationship was not
significant (ρ = -0.18; p = 0.08).
Discussion
Our findings suggest that wetland plant species zonation in Weeks Bay arises in response
to changes in inundation and salinity that vary across the coastal transition. Although elevation
ranges differed for the three dominant plant species, their ranges did overlap, especially between
J. roemerianus and S. cynosuroides, and as such, zonation could not be defined by elevation
38
alone. When salinity was also examined, clear segregation between C. jamaicense and the other
two species was apparent, with a tendency for J. roemerianus to dominate when salinity
exceeded 9 psu. Thus, salinity provided a possible explanation for relative spatial distributions of
the dominant species along the elevation gradient in this Weeks Bay marsh.
The observed influence of salinity as a driver of tidal marsh plant zonation is consistent
with the current paradigm in which salinity is viewed as a key indicator of vegetation and habitat
classification (Adams, 1963; Chapman, 1974; Odum, 1988; Visser et al., 2002; Pennings et al.,
2005). In a study of temporal changes in vegetation in a tidal freshwater marsh, Perry and
Hershner (1999) documented an increase in the abundance of S. cynosuroides with increasing
saltwater influence. J. roemerianus, which can occupy zones with dissimilar environmental
conditions (Touchette, 2006), has a more ubiquitous presence throughout Weeks Bay. It is also
common in marshes along the south Atlantic and northern Gulf of Mexico coasts where it
dominates 20.7% and 7.3% of marshes, respectively (Eleterius, 1976). Further, tradeoffs between
S. cynosuroides and J. roemerianus in response to salinity are well documented (Higinbotham et
al., 2004; Pennings et al., 2005; Touchette et al., 2009; White and Alber, 2009). Hackney et al.
(1996) found that J. roemerianus occupied higher salinity areas than S. cynosuroides (average
salinities 17 psu and 13 psu, respectively), a pattern that was observed in the Weeks Bay marsh
as well.
Given that zonation was linked to salinity, the effects of projected SLR on the Weeks Bay
marsh and other similar ecosystems may be through its effects on salinity, rather than inundation.
Under conditions of global climate change, a possible reduction in precipitation in the Weeks
Bay watershed could lower freshwater delivery and increase salinity in these marshes. Higher
39
salinities would likely favor J. roemerianus and may result in a sharper salinity gradient along
the coastal transition. However, based on the spatial salinity profile in the survey area, sea-level
rise could reduce salinity by flushing out salts that have accumulated behind the berm. The area
of highest observed salinity (and highest J. roemerianus dominance) was on the landward side of
this berm. Field observations indicated that this may be a result of evaporation and subsequent
salt accumulation, resulting in higher salinity than that of the bay water (Schroeder et al. 1992).
In the event of higher sea levels, tidal flushing could lower salinities in this area by flushing
these salts, favoring S. cynosuroides.
The already marginal tidal marshes in Weeks Bay may be under threat of coastal squeeze.
As the dominant species in the upland marsh zone, C. jamaicense is limited by salinity and
forested wetland on its seaward and landward extents, respectively. Because the salinity
tolerances of J. roemerianus and S. cynosuroides are much higher than that of C. jamaicense, the
possibility of elevated salinity from SLR may shift the habitable C. jamaicense zone upland as J.
roemerianus and S. cynosuroides migrate inland. On the landward extreme, the forested wetlands
that border the C. jamaicense zone have been expanding into coastal marsh habitat (Chapter 2),
which further reduces the inhabitable coastal transition and may limit the capacity of marsh
transgression upland. Management approaches that reduce these barriers to migration may
promote marsh persistence as sea levels continue to rise.
Prescribed burning of marshes may be one mechanism by which upland forest barriers to
marsh migration are reduced. Fire can promote a more natural forest to wetland ecotone (Poulter
et al., 2008; Knickerbocker et al., 2009), in which marsh-forest transitions are facilitated in
response to changing environmental conditions. Ecosystems exposed to fire events have been
40
shown to be more spatially fluid in response to changing environmental conditions, and also
display more distinct ecotone boundaries than those in which fire is absent (Boughton et.al.
2006; Smith et.al. 2013). Because the first prescribed burn on record for the Weeks Bay marsh
was not until 2008, it is possible that fire management strategies will reduce forest encroachment
(as documented in Chapter 2), and promote marsh migration in response to rising sea level. The
continued practice of prescribed burns, along with monitoring of changes in salinity and the
location of the marsh-forest ecotone, are key to gaining insight to the future of distribution of
species along this coastal transition.
The vulnerability of unvegetated and human-altered shorelines to erosional loss is a welldocumented occurrence worldwide (Gedan et al., 2011). Hard shoreline structures, such as
bulkheads that are intended to stabilize shoreline, actually can exacerbate erosion by focusing
tidal action on a small area of land resulting in „vertical erosion‟. They can also act as a physical
barrier preventing landward migration of plant species in response to changing environmental
conditions. These bulkhead shorelines are common in areas of urban and residential development
to protect property from the hazards of the sea. In Weeks Bay, the majority of shoreline erosion
is concentrated in areas with bulkhead structures or unvegetated sediment (Douglas and Pickel,
1999a; Jones and Tidwell, 2012; Chapter 2), making the narrow zone of intertidal marsh an
important resource for the area.
The findings of this study suggest that intertidal marsh species‟ distributions arise in
response to changes in elevation and salinity, factors that will likely change with SLR. Further,
upslope barriers to species migration, coupled with seaward stresses associated with hardened
shorelines and erosion, may reduce the ability of Weeks Bay marshes to respond to SLR in the
41
future. To facilitate sustainable coastal development practices and minimize coastal squeeze, it
may be necessary to utilize prescribed burns to manage upland barriers, limit the presence of
bulkheads or other hardened structures, and promote natural mechanisms of ecosystem resilience
to sea-level rise. In so doing, environmental managers may be able to enhance marsh
transgression and reduce erosional land loss, thereby promoting a more resilient coastal
landscape in Weeks Bay, AL.
References
Adams, D. A. (1963). Factors influencing vascular plant zonation in North Carolina salt
marshes. Ecology, 445-456.
Boughton, E.A., P.F. Quintana-Ascencio, E.S., Menges, and R.K. Boughton. (2006) Association
of ecotones with relative elevation and fire in an upland Florida landscape. Journal of
Vegetation Science. 17, 361-368.
Brewer, J.S., Grace, J. B. (1990). Plant community structure in an oligohaline tidal marsh.
Vegetatio, 96(2), 93-107.
Chapman, V.J. (1974) Salt marshes and salt deserts of the world. Ecology of Halophytes (ed.
W.H. Queen), pp. 3–19. Academic Press, New York.
Cooper, A. (1982). The effects of salinity and waterlogging on the growth and cation uptake of
salt marsh plants. New phytologist, 90(2), 263-275.
Dahl T.E. (1990). Wetlands Losses in the United States 1780‟s to 1980‟s. U.S. Department of the
Interior, Fish and Wildlife Service, Washington, D.C. 112.
Dahl, T.E. (2011). Status and trends of wetlands in the conterminous United States 2004 to 2009.
U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C. 108.
D az, S., & Cabido, M. (2001). Vive la difference: plant functional diversity matters to
ecosystem processes. Trends in Ecology & Evolution, 16(11), 646-655.
Douglass, S. L., & Pickel, B. H. (1999). The Tide Doesn't Go Out Anymore- The Effect of
Bulkheads on Urban Bay Shorelines. Shore & Beach, 67(2), 19-25.
42
Earle, J. C., & Kershaw, K. A. (1989). Vegetation patterns in James Bay coastal marshes. III.
Salinity and elevation as factors influencing plant zonations. Canadian Journal of
Botany, 67(10), 2967-2974.
Elmqvist, T., Folke, C., Nyström, M., Peterson, G., Bengtsson, J., Walker, B., & Norberg, J.
(2003). Response diversity, ecosystem change, and resilience. Frontiers in Ecology and the
Environment, 1(9), 488-494.
English Nature (1992) Coastal Zone Conservation. English Nature's Rationale, Objectives and
Practical Recommendations. English Nature, Peterborough, UK.
Gedan, K. B., & Bertness, M. D. (2010). How will warming affect the salt marsh foundation
species Spartina patens and its ecological role?, 479–487.
Godfrey, R. K. andJ. W. Wooten. 1979. Aquatic and wetland plants of southeastern United
States. Monocotyledons.
Godfrey, R. K., & Wooten, J. W. (1981). Aquatic plants of southeastern United States:
dicotyledons.
Greiner La Peyre, M.K., Grace, J.B., Hahn, E., & Mendelssohn, I. A. (2001). The importance of
competition in regulating plant species abundance along a salinity gradient. Ecology,
82(1),62-69.
Grime, J. P. (1977). Evidence for the existence of three primary strategies in plants and its
relevance to ecological and evolutionary theory. American Naturalist, 1169-1194.
Grime, J. P. (1979). Competition and the struggle for existence. In Population dynamics. 20th
Symposium of the British Ecological Society. Blackwell Scientific Publication, London (pp.
123-139).
Hackney, C. T., Brady, S., Stemmy, L., Boris, M., Dennis, C., Hancock, T., & Barbee, E. (1996).
Does intertidal vegetation indicate specific soil and hydrologic conditions. Wetlands, 16(1),
89-94.
Higinbotham, C. B., Alber, M., & Chalmers, A. G. (2004). Analysis of tidal marsh vegetation
patterns in two Georgia estuaries using aerial photography and GIS. Estuaries, 27(4), 670683.
Jones, S. C., & Tidwell, D. K. Gulf Shoreline Monitoring, 2012, Baldwin and Mobile Counties,
Alabama.
43
Kirwan, M. L., Guntenspergen, G. R., D'Alpaos, A., Morris, J. T., Mudd, S. M., & Temmerman,
S. (2010). Limits on the adaptability of coastal marshes to rising sea level. Geophysical
Research Letters, 37(23).
Kirwan, M. L., & Megonigal, J. P. (2013). Tidal wetland stability in the face of human impacts
and sea-level rise. Nature, 504(7478), 53-60.
Knickerbocker, C. M., Leitholf, S., Stephens, E. L., Keellings, D. J., Laird, H., Anderson, C. J.
R., & Quintana-Ascencio, P. F. (2009). Tree encroachment of a sawgrass (Cladium
jamaicense) marsh within an increasingly urbanized ecosystem. Natural Areas
Journal, 29(1), 15-26.
McLeod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., ... & Silliman, B.
R. (2011). A blueprint for blue carbon: toward an improved understanding of the role of
vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the
Environment, 9(10), 552-560.
Mitsch, W. J., & Gosselink, J. G. (2007). Wetlands. Hoboken.
National Estuarine Research Reserve System, Weeks Bay, AL. http://nerrs.noaa.gov/. Retrieved
February 1, 2014, from http://nerrs.noaa.gov/Reserve.aspx?ResID=WKB
Nixon, S. W. (1982). The ecology of New England high salt marshes: a community profile (No.
FWS/OBS-81/55). National Coastal Ecosystems Team, Washington, DC (USA); Rhode
Island Univ., Kingston, RI (USA). Graduate School of Oceanography.
Odum, W.E., (1988). Comparative ecology of tidal freshwater and salt marshes. Annual Review
of Ecology and Systematic, 147-176.
Pennings, S. C., Grant, M. B., & Bertness, M. D. (2005). Plant zonation in low‐latitude salt
marshes: disentangling the roles of flooding, salinity and competition. Journal of
ecology, 93(1), 159-167.
Peterson, G., Allen, C. R., & Holling, C. S. (1998). Ecological resilience, biodiversity, and
scale. Ecosystems, 1, 6-18.
Pethick, J. (1993). Shoreline adjustments and coastal management: physical and biological
processes under accelerated sea-level rise. Geographical Journal, 162-168.
Poulter, B., Qian, S. S., & Christensen, N. L. (2009). Determinants of coastal treeline and the
role of abiotic and biotic interactions. Plant Ecology, 202(1), 55-66.
44
Schroeder, W. W., Dinnel, S. P. and Wiseman, W. J. (1992). Salinity Structure of a Shallow,
Tributary Estuary, in Dynamics and Exchanges in Estuaries and the Coastal Zone (ed D.
Prandle), American Geophysical Union, Washington, D. C.. doi: 10.1029/CE040p0155
Smith III, T.J., Foster, A.M., Tiling-Range, G., Jones, J.W. (2013). Dynamics of mangrove–
marsh ecotones in subtropical coastal wetlands: fire, sea-level rise, and water levels. Fire
Ecology, 9, 66–77.
Stagg, C. L., & Mendelssohn, I. A. (2011). Controls on resilience and stability in a sedimentsubsidized salt marsh. Ecological Applications, 21, 1731-1744.
Touchette, B. W., Smith, G. A., Rhodes, K. L., & Poole, M. (2009). Tolerance and avoidance:
two contrasting physiological responses to salt stress in mature marsh halophytes Juncus
roemerianus Scheele and Spartina alterniflora Loisel. Journal of Experimental Marine
Biology and Ecology, 380(1), 106-112.
Visser, J. M., Sasser, C. E., Chabreck, R. H., & Linscombe, R. G. (2002). The impact of a severe
drought on the vegetation of a subtropical estuary. Estuaries, 25(6), 1184-1195.
Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human domination of
Earth's ecosystems. Science, 277, 494-499.
White, S. N., & Alber, M. (2009). Drought-associated shifts in Spartina alterniflora and S.
cynosuroides in the Altamaha River estuary. Wetlands,29, 215-224.
Zedler, J. B. (1977). Salt marsh community structure in the Tijuana Estuary,
California. Estuarine and Coastal Marine Science, 5, 39-53.
45
Table 3.1 Plant species identified during surveys of marsh transects listed in order of frequency of plots
(n) in which they were observed out of 101 total plots. Ranges in elevation (m), salinity (psu), and
distance from shoreline (m) are provided for each species.
(psu)
46
Figure 3.1: Area of Interest for survey of plant species percent cover and elevation measurements. Each green circle represents a
survey point.
47
Table 3.2: Spearman‟s rho correlation analysis between environmental conditions (salinity, elevation, and distance from shoreline) and
the percent covers of the dominant plant species: Spartina cynosuroides (SPCY), Cladium jamaicense (CLJA), and Juncus
roemerianus (JURO). Significance was tested at the α = 0.05 level, and is indicated in bold.
Table 3.3: Spearman‟s rho correlation analysis amongst the dominant plant species (Spartina cynosuroides (SPCY), Cladium
jamaicense (CLJA), and Juncus roemerianus (JURO)). r value signify the nature (positive or negative) and strength of correlations
and p value indicates significance at the α = 0.05 level. A positive value would indicate cohabitation and a negative would indicate a
lack of cohabitation. Significant values are indicated in bold.
48
Figure 3.2: Change in elevation with increasing distance from shoreline and the distribution of dominant marsh plant species by
elevation and distance from shoreline. Spartina cynosuroides (SPCY), Cladium jamaicense (CLJA), and Juncus roemerianus (JURO)
distributions are illustrated by the shaded boxes, with the vertical extent of each box indicating elevation and the horizontal extent of
each box indicating distance from shoreline. Each point on elevation line signifies a survey point, conveying the heterogeneous microtopography of the coastal transition in the survey area.
49
Figure 3.3. Spatial distribution of Juncus roemerianus by salinity. Larger circles indicate higher percent cover in area and green to red
indicates increased porewater salinity generated from field readings.
50
Figure 3.4: Spatial distribution of Spartina cynosuroides by salinity. Larger circles indicate higher percent cover in area and green to
red indicates increased porewater salinity generated from field readings.
51
Figure 3.5: Spatial distribution of Cladium jamaicense by salinity. Larger circles indicate higher percent cover in area and green to red
indicates increased porewater salinity generated from field readings.
52
Chapter 4
Conclusions
The implications of plant species salinity and elevation ranges are especially relevant in
intertidal wetlands given anthropogenic influences and accelerated SLR. Factors that limit the
ability of these ecosystems to migrate in the face of changing environmental conditions,
specifically those associated with SLR, reduce ecosystem resilience and contribute to the worldwide phenomenon of coastal squeeze (Pethick, 1993; English Nature, 1994; Kirwan and
Megonigal, 2013). In areas with upland barriers, especially hardened, urban shoreline structures,
such as bulkheads and upland terraces, marsh transgression is often suppressed and tidal action is
concentrated on smaller areas, potentially increasing land loss to open water via erosional action
(Douglas and Pickel, 1999). By limiting the presence of these types of structures and facilitating
marsh migration into more favorable habitats in the face of projected SLR, environmental
managers can enhance natural mechanisms of ecosystem resilience, thereby preserving the goods
and services these ecosystems provide.
Historic land-use and land-cover changes can have a cumulative effect on ecosystems‟
capacities to adapt to continued changes. Agriculture and urban development practices have
consequences downstream that may affect the ability of coastal wetlands to persist in the face of
environmental stress (Nyman et al., 2006; McKee, 2011; Stagg and Mendelssohn, 2011; Kirwan
and Megonigal, 2013). Also, the degradation or loss of natural habitats, especially wetlands,
exacerbates the negative effects of agriculture and urban development by reducing the mitigative
53
services of these ecosystems (i.e. functions of runoff abatement) in the landscape (Fetter et al.,
1978; Nichols, 1983; Brix, 1994; Oostrom and Russeul, 1994; Rousseau et al., 2008). However,
there are numerous strategies employed by environmental managers that have been shown to
ameliorate these impacts, including improved best management practices in agriculture and
strategic restoration/placement of riparian buffer wetlands (Osborne and Wiley, 1988; Lowrance
et al., 1997; Goetz et al., 2003; Zedler, 2003; Jantz, 2005).
With projected SLR and continued expansion of urban development on and near the
coast, environmental managers are tasked with preserving these at-risk ecosystems. By gaining
an understanding of the upland processes associated with LULC and local plant species zonation
patterns, environmental managers can employ site-specific strategies to support healthy
ecosystem on a landscape scale. Paramount to environmental strategies is the facilitation of
natural processes of resilience that afford these wetland species the opportunity to migrate and
follow their preferred inundation and salinity niche to mitigate the adverse effects of LULC
change and SLR.
References
Brix, H. (1994). Use of constructed wetlands in water pollution control: historical development,
present status, and future perspectives. Water Science and Technology, 30(8), 209-224.
Douglass, S. L., & Pickel, B. H. (1999). The Tide Doesn't Go Out Anymore- The Effect of
Bulkheads on Urban Bay Shorelines. Shore & Beach, 67(2), 19-25.
English Nature (1992) Coastal Zone Conservation. English Nature's Rationale, Objectives and
Practical Recommendations. English Nature, Peterborough, UK.
Goetz, S. J., Wright, R. K., Smith, A. J., Zinecker, E., & Schaub, E. (2003). IKONOS imagery
for resource management: Tree cover, impervious surfaces, and riparian buffer analyses
in the mid-Atlantic region. Remote sensing of environment, 88(1), 195-208.
Pethick, J. (1993). Shoreline adjustments and coastal management: physical and biological
processes under accelerated sea-level rise. Geographical Journal, 162-168
Jantz, P., Goetz, S., & Jantz, C. (2005). Urbanization and the loss of resource lands in the
Chesapeake Bay watershed. Environmental Management, 36(6), 808-825.
54
Kirwan, M. L., & Megonigal, J. P. (2013). Tidal wetland stability in the face of human impacts
and sea-level rise. Nature, 504(7478), 53-60.
Lowrance, R., Altier, L. S., Newbold, J. D., Schnabel, R. R., Groffman, P. M., Denver, J. M., ...
& Todd, A. H. (1997). Water quality functions of riparian forest buffers in Chesapeake Bay
watersheds. Environmental Management, 21(5), 687-712.
McKee, K. L. (2011). Biophysical controls on accretion and elevation change in Caribbean
mangrove ecosystems. Estuarine, Coastal and Shelf Science, 91(4), 475-483.
Nichols, D. S. (1983). Capacity of natural wetlands to remove nutrients from wastewater. Water
Pollution Control Federation, 495-505.
Nyman, J. A., Walters, R. J., Delaune, R. D., & Patrick, W. H. (2006). Marsh vertical accretion
via vegetative growth. Estuarine, Coastal and Shelf Science, 69(3), 370-380.
Van Oostrom, A. J., & Russell, J. M. (1994). Denitrification in constructed wastewater wetlands
receiving high concentrations of nitrate. Water Science & Technology, 29(4), 7-14.
Osborne, L. L., & Wiley, M. J. (1988). Empirical relationships between land-use/cover and
stream water quality in an agricultural watershed. Journal of Environmental Management,
26(1), 9-27.
Rousseau, D. P. L., Lesage, E., Story, A., Vanrolleghem, P. A., & De Pauw, N. (2008).
Constructed wetlands for water reclamation. Desalination, 218(1), 181-189.
Fetter Jr, C. W., Sloey, W. E., & Spangler, F. L. (1978). Use of a natural marsh for wastewater
polishing. Water Pollution Control Federation, 290-307.Turner 1994,
Stagg, C. L., & Mendelssohn, I. A. (2011). Controls on resilience and stability in a sedimentsubsidized salt marsh. Ecological Applications, 21(5), 1731-1744.
Zedler, J. B. (2003). Wetlands at your service: reducing impacts of agriculture at the watershed
scale. Frontiers in Ecology and the Environment, 1(2), 65-72.
55
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertising