Download The Impacts of Shoreline Development on Shallow-water Benthic Communities in the Patuxent River, MD

The Impacts of Shoreline Development on
Shallow-water Benthic Communities in the Patuxent River, MD
__________________
A Thesis
Presented to
The Faculty of the School of Marine Science
The College of William & Mary in Virginia
In partial fulfillment
of the Requirements for the Degree of
Master of Science
________________
by
Cassie D. Bradley
2011
APPROVAL SHEET
This thesis is submitted in partial fulfillment of
the requirements for the degree of
Master of Science
____________________________________
Cassie D. Bradley
Approved, by the Committee, May 2011
____________________________________
Rochelle D. Seitz, Ph.D.
Committee Chairman/Advisor
____________________________________
Mark. J. Brush, Ph.D.
____________________________________
Carl T. Friedrichs, Ph.D.
____________________________________
Linda C. Schaffner, Ph.D.
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DEDICATION
This thesis is dedicated to the memory of Calvin Bennett Bradley (1933-2005), who
taught me many things, including the value of honest hard work, the value of laughing at
my own mistakes, and when to “shape up or ship out.”
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ............................................................................................... v
LIST OF TABLES............................................................................................................. vi
LIST OF FIGURES.......................................................................................................... vii
ABSTRACT ........................................................................................................................ x
INTRODUCTION .............................................................................................................. 2
SALT MARSH ECOSYSTEMS AND INFAUNA ................................................................................... 2
ANTHROPOGENIC IMPACTS ............................................................................................................ 4
STUDY OBJECTIVES AND HYPOTHESES ........................................................................................ 7
MATERIALS AND METHODS ....................................................................................... 9
STUDY LOCATION & SITE SELECTION .......................................................................................... 9
FIELD & LABORATORY METHODS .............................................................................................. 10
STATISTICAL ANALYSES ................................................................................................................. 14
RESULTS .......................................................................................................................... 17
PHYSICAL PARAMETERS................................................................................................................ 17
INFAUNA & PREDATORS ............................................................................................................... 18
COMMUNITY STRUCTURE PREDICTORS ..................................................................................... 20
COMMUNITY COMPOSITION......................................................................................................... 22
SYSTEM-WIDE COMPARISONS ...................................................................................................... 23
DISCUSSION.................................................................................................................... 24
GENERAL OBSERVATIONS............................................................................................................. 24
INFAUNAL RESPONSES .................................................................................................................. 25
POST-HOC ANALYSES .................................................................................................................... 34
SYSTEM-WIDE COMPARISONS ...................................................................................................... 35
CONCLUSIONS ............................................................................................................... 37
LITERATURE CITED .................................................................................................... 40
APPENDIX ....................................................................................................................... 87
VITA .................................................................................................................................. 94
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ACKNOWLEDGEMENTS
I am sincerely grateful to my advisor, Dr. Rochelle Seitz, for her support, patience
and good humor throughout the duration of this project. I am also grateful for the
enthusiasm and advice offered by my committee members: Drs Mark Brush, Carl
Friedrichs, and Linda Schaffner. Thank you all for your positivity and for taking an
interest in my research. I must extend an extra note of thanks to Dr. Mark Brush for not
only reminding me that science is supposed to be fun, but also for providing expertly
timed Harry Potter-themed encouragement.
This project would not have been possible without the field, laboratory, and
logistical assistance of many individuals. I offer enormous thanks to the mud-slingers,
sample-sorters, taxonomy and statistics wizards, and general gurus: Stacy Aguilera, Alice
Brylawski, Bryce Brylawski, Russ Burke, Allison Colden, Theresa Davenport, Dr. Robert
Diaz, Sarah Donelan, Ryan Gill, Juliette Giordano, Dr. Rob Latour, Cat Lauck, Emily
Kimminau, Katie Knick, Dr. Rom Lipcius, Danielle McCulloch, Gina Ralph, Gabby
Saluta, Mike Seebo, Cara Simpson, Alison Smith, Seth Theuerkauf, Diane Tulipani, and
Jacques van Montfrans. Thank you to Amit Malhotra and Dr. Mark Fonseca for their
advice and assistance with WEMo modeling software, and also to Rachael Blake for
battling through the WEMo trenches with me. I also owe much gratitude to Gina Burrell
and Maxine Butler of the Biological Sciences department, and to Kevin Kiley in ITNS
for their patience and technical support.
Many thanks are owed to VIMS colleagues and friends for providing guidance,
support, laughter, and relief during the past three years. In particular, thank you to Kristy
Hill, Andrew Wozniak, Katie Knick, Colin Felts, Pat Dickhudt, Matilda, Anu FrankLawale, Andrij Horodysky, CJ Sweetman, Kristene Parsons, Kathryn Sobocinski,
Heather Wiseman, Stephanie Salisbury, Amy Then, and Mark “chum” Henderson. Also,
thanks to the entire SMS core class of 2008 for being an amazing cohort.
Finally, I could not have succeeded in the completion of this – or any other –
project without the love and support of my family. To my sisters, thank you for sharing
an appreciation for oddities and for constantly making me laugh. To Anna, Karina, Ash,
and Gable – thanks for keeping me upright. And thank you Mom and Dad, for instilling
in me both a strong (if overenthusiastic) work ethic and a genuine love of the natural
world.
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LIST OF TABLES
Page
Table 1: Value of ecosystem services of tidal marshes. (Adapted from Bromberg-Gedan
et al. 2009) ........................................................................................................................ 51
Table 2: Linear regression models used in AIC analyses. Explanatory parameters
used to predict changes in infaunal density, diversity, and biomass include: shoreline
type, sediment type, predator abundance, representative wave energy, total organic
carbon, and sediment chlorophyll concentration .............................................................. 52
Table 3: Water quality, sediment, and wave energy measurements (means per shoreline
type). ................................................................................................................................. 53
Table 4: Species list of 3-mm infauna (means per shoreline type). .................................. 54
Table 5: Species list of 500-µm infauna (means per shoreline type)................................ 55
Table 6: Species list of epibenthic predators (means per shoreline type). ........................ 57
Table 7: AIC analysis results: 3-mm infaunal density ...................................................... 58
Table 8: AIC analysis results: 3-mm infaunal Shannon diversity .................................... 59
Table 9: AIC analysis results: 3-mm infaunal biomass .................................................... 60
Table 10: AIC analysis results: 500-µm infaunal density................................................. 61
Table 11: AIC analysis results: 500-µm infaunal Shannon diversity. .............................. 62
Table 12: AIC analysis results: 500-µm infaunal biomass. .............................................. 63
Table 13: Summary of predictors of all infaunal response variables................................ 64
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LIST OF FIGURES
Page
Figure 1: Study area and sampling distribution throughout the Patuxent River, MD. ..... 65
Figure 2: Relationship between percent TOC and percent TN. ........................................ 66
Figure 3: Relationship between percent TOC and sediment type
(percentage of sand and gravel). ....................................................................................... 67
Figure 4: Relationship between percent TN and sediment type
(percentage of sand and gravel). ....................................................................................... 68
Figure 5: Shoreline effects on mean 3-mm infaunal density. ........................................... 69
Figure 6: Shoreline effects on mean 3-mm infaunal Shannon diversity........................... 70
Figure 7: (A) Effect of shoreline type on 3-mm biomass; (B) Biomass by shoreline type
after removal of outlier (M. arenaria). .............................................................................. 71
Figure 8: Shoreline effects on mean 500-µm infaunal density. ........................................ 72
Figure 9: Shoreline effects on mean 500-µm infaunal Shannon diversity ....................... 73
Figure 10: Shoreline effects on mean 500-µm infaunal biomass ..................................... 74
Figure 11: Shoreline effects on mean predator abundance ............................................... 75
Figure 12: Relationship between predator abundance and 500-µm infaunal biomass .... 76
Figure 13: Shoreline effects on 3-mm infaunal community composition ........................ 77
Figure 14: Shoreline effects on 500-µm infaunal community composition. .................... 78
Figure 15: Shoreline effects on 3-mm taxa-specific biomass ........................................... 79
Figure 16: Shoreline effects on 500-µm taxa-specific biomass. ....................................... 80
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Figure 17: Shoreline effects on 3-mm infaunal feeding mode abundance. ...................... 81
Figure 18: Shoreline effects on 500-µm infaunal feeding mode abundance.. .................. 82
Figure 19: Comparison of 3-mm and 500-µm infaunal density with long-term (19962006) means from Chesapeake Bay Program data. .......................................................... 83
Figure 20: Comparison of 3-mm and 500-µm infaunal Shannon diversity with
long-term (1996-2006) means from Chesapeake Bay Program data ................................ 84
Figure 21a: Comparison of 3-mm and 500-µm infaunal biomass with long-term
(1996-2006) means from Chesapeake Bay Program data................................................. 85
Figure 21b. Comparison of 3-mm and 500-µm infaunal biomass in mesohaline
regions with long-term (1996-2006) means from Chesapeake Bay Program
data…………………………………………………………………................................86
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LIST OF FIGURES & TABLES IN APPENDIX
Page
Figure A1: Results of bootstrap simulation for infaunal density data from June 2008 pilot
study.. ................................................................................................................................ 87
Table A1: Post-hoc AIC analysis results: 3-mm infaunal density with addition of
variables: salinity, distance upriver................................................................................... 88
Table A2: Post-hoc AIC analysis results: 3-mm infaunal Shannon diversity with
addition of variables: salinity, distance upriver. ............................................................... 89
Table A3: Post-hoc AIC analysis results: 3-mm infaunal biomass with addition of
variables: salinity, distance upriver................................................................................... 90
Table A4: Post-hoc AIC analysis results: 500-µm infaunal density with addition of
variables: salinity, distance upriver................................................................................... 91
Table A5: Post-hoc AIC analysis results: 500-µm infaunal Shannon diversity with
addition of variables: salinity, distance upriver. ............................................................... 92
Table A6: Post-hoc AIC analysis results: 500-µm infaunal biomass with addition of
variables: salinity, distance upriver................................................................................... 93
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ABSTRACT
Natural coastal habitats throughout Chesapeake Bay are increasingly threatened with
shoreline modification due to population growth and rising rates of development. The
replacement of these natural coastlines with hardened structures such as seawalls (bulkheads) and
stone revetments (riprap) not only compromises vegetation at the land-water interface, but also
can influence several elements of local aquatic food webs. Effects of these alterations have been
well-studied with respect to fish assemblages and intertidal communities, particularly in
conjunction with larger-scale watershed development, and recently, interest has shifted toward
investigation of the effects of shoreline development on subtidal benthic infaunal communities.
This study evaluated the direct, local impacts of bulkhead and riprap compared to natural
marsh shorelines, as well as the effects of sediment characteristics, predator abundance, and
system-specific physical features on benthic infauna in the Patuxent River, Chesapeake Bay.
Forty-five sites were divided among three shoreline types and distributed across three main river
zones. At each site, a benthic infaunal suction sample (3-mm mesh), push-core sample (500-µm
mesh), sediment samples, water-quality measurements, and trawls for predators were taken.
Samples were sorted to determine density, diversity, and biomass of infaunal organisms. Data
were assessed using an Information-Theoretic approach (AIC analysis) to determine the most
influential variables, of those measured, on the infaunal community for two benthic data sets: 3mm-suctions and 500-µm-cores. Results from these analyses on 3-mm samples suggested that
shoreline type was the best predictor of diversity, while wave energy, sediment chlorophyll
concentration, sediment type, and predator abundance best predicted density and biomass.
Benthic responses within the 500-µm dataset were not strongly affected by shoreline type.
Rather, responses were best predicted by sediment chlorophyll, wave energy, sediment type,
predator abundance, and sediment organic carbon (TOC) content.
Results indicate that, compared to other Bay tributaries, the Patuxent River is a relatively
degraded system. The small range in long-term responses of Patuxent infauna from previous
work provides a possible explanation as to why I was unable to see significant differences in
infaunal response among shoreline types in the current study (i.e., there was little scope for
change by shoreline in the system as a whole). However, I suggest that natural marsh habitats are
healthier subsystems of the Patuxent River, due to the greater variety of infaunal feeding guilds
and higher infaunal biomass observed at these compared to hardened sites. Higher predator
abundance was associated with higher infaunal biomass at natural marsh sites in both size
fractions, suggesting the bottom-up control of higher-trophic-level species in this system, as
predators seek out suitable prey items. Given these observations, and the fact that influential
variables such as wave energy, sediment nutrient and chlorophyll content, predator abundance,
and sediment type may vary according to shoreline type, the replacement of natural shoreline
with hardened structures will lead to complex changes in subtidal benthic communities in
Chesapeake Bay tributaries and should be minimized to maintain qualities of the natural system.
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The Impacts of Shoreline Development on Shallow-water Benthic Communities in the
Patuxent River, MD
INTRODUCTION
Coastal habitats worldwide are increasingly threatened with the adverse impacts
of factors associated with both natural and anthropogenic global change. In estuaries
such as Chesapeake Bay, these include rising temperature and sea levels, changes in the
magnitude of nutrient and sediment runoff, overfishing, and habitat loss (Kennish 2002;
Kemp et al. 2005). Often, detrimental effects within Chesapeake Bay and other estuarine
systems result from a combination of several of these factors, and these occurrences may
become more likely with increased human use and manipulation of coastal resources.
Coastal counties, which constitute less than 20 percent of the land area in the United
States, held greater than 50 percent of the population in 2003 (Crossett et al. 2004;
Bulleri and Chapman 2010), a figure expected to rise over the next century. The
Chesapeake Bay watershed alone has experienced a three-fold increase in population size
in the past 100 years (Kemp et al. 2005). As human development progresses farther
seaward, changes in land-use patterns may result in alterations of physical and chemical
processes that occur at the land-water interface. These near-shore areas and their
associated biological communities, which are subject to impacts from both the marine
and terrestrial environments, are particularly vulnerable to change.
Salt Marsh Ecosystems and Infauna
As coasts become modified, natural shorelines—and their associated services and
functions—are lost. One major area of concern is tidal salt marshes, which make up
approximately 66 percent of Chesapeake Bay classified wetlands (Wilson et al. 2007)
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and are largely understood to play an integral role in estuarine function (Valiela 1995;
Bertness 2007; Bromberg-Gedan et al. 2009). In recent years, salt marshes have been
recognized to provide a wide range of ecosystem services to coastal communities (Table
1). These areas support highly productive near-shore assemblages, fostering energy flow
and transfer through complex food webs (Teal 1962; Mitsch and Gosselink 2000).
Marshes also provide critical habitat for various life-stages of many commercially and
ecologically important estuarine species (Beck et al. 2001; Dahlgren et al. 2006), offering
refuge from predation and an abundant resource supply. The natural vegetation buffer
created at the land-water interface dissipates wave energy, allowing for the accumulation
of fine sediments and associated organic matter (Snelgrove 2000). Further, the roots of
many marsh grasses facilitate important nutrient transformation processes such as
denitrification and carbon oxidation (Gribsholt and Kristensen 2002; Kemp et al. 2005).
The energetically stable environment and organic-rich sediments of salt marshes tend to
support abundant benthic communities, which themselves play a major role in nutrient
transformation and energy flow (Diaz and Schaffner 1990).
Benthic infauna not only alter fluxes of particles and solutes across the sedimentwater interface (Aller 1977; Diaz and Schaffner 1990; Libes 1992; Snelgrove and
Butman 1994; Karlson et al. 2007), but also serve as a substantial food source for
commercially and recreationally important species in higher trophic levels, such as blue
crab (Callinectes sapidus), spot (Leiostomus xanthurus), croaker (Micropogonias
undulatus), and other bottom-feeding fishes in Chesapeake Bay (Virnstein 1977; Diaz
and Schaffner 1990; Hines et al. 1990). Due to their relatively long life spans, sessile
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nature, and sensitivity to pollutants and other stressors, these communities also serve as
important indicators of ecological integrity (Dauer 1993; Hyland et al. 2005).
Large-scale Anthropogenic Impacts
On a large scale, changes in watershed development and land-use patterns can
affect adjacent near-shore benthic communities. For example, South Carolina tidal
creeks drained by watersheds ranging in development from forested (undeveloped) to
urban or industrial showed significantly different abundances of macrobenthic taxa and
individuals (Lerberg et al. 2000). In the same study, creeks draining areas of unaltered
salt marsh vegetation supported significantly higher diversity and number of taxa than
areas draining altered and highly impacted marshes.
Watershed development also has been linked to altered benthic community
condition in Chesapeake Bay, potentially through the effect of changes in sediment
contamination, dissolved oxygen concentrations, and nutrient enrichment (Dauer et al.
2000); negative biological responses of infaunal benthic communities were correlated
with watershed urbanization, while positive responses were correlated with watershed
forestation, a pattern that has been further supported throughout Chesapeake Bay (King
et al. 2005; Bilkovic et al. 2006).
Small-scale Anthropogenic Impacts
Understanding the impacts of development over multiple scales is essential to
gauge the overall biological response of a system. Of particular interest at smaller scales
are the effects of direct, physical shoreline modification, specifically where natural
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coastline is armored with hardened structures. As waterfront populations increase,
property owners use bulkhead (vertical sea wall, often constructed of treated wood,
metal, or concrete) and riprap (large, sloping stone revetments) to stabilize the shoreline
and prevent erosion. While often effective methods of erosion control, these structures
may alter natural habitat connectivity, as well as patterns of sedimentation and erosion,
nutrient runoff and filtration, and wave-energy dissipation (Bendell 2006). Of the 6600
kilometers of Maryland’s shoreline, roughly 26 percent has already been hardened, and
240 kilometers of Virginia’s shoreline was approved for the construction of bulkheads
and riprap between 2000 and 2007 alone (Nash 2008).
Shoreline development threatens habitat integrity and suitability by potentially
fragmenting contiguous marsh, and it may also prevent landward lateral migration, a
natural response of marsh flora to the concomitant threat of sea level rise (Silliman et al.
2009). The modification of natural marsh shoreline may also impact the adjacent benthic
infaunal community, and thus, higher-trophic-level predators that exploit this food
source. Several recent studies have focused on the effects of marsh alteration on resident
benthic infauna and pelagic assemblages, and found a link between natural marshes,
benthic infaunal prey in subtidal habitats, and predator abundance (Seitz and Lawless
2008). Along the Mississippi Gulf Coast, locally altered marshes supported lower
abundances of both resident and transient fishes and crustaceans than pristine marshes
(Peterson et al. 2000), and marshes in restricted (generally hardened) areas had lower
infaunal density than unrestricted marshes, a pattern followed by epifauna/nekton at the
same sampling locations (Partyka and Peterson 2008).
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A simultaneous, multi-scale study of watershed land-use and local shoreline
condition demonstrated the importance of natural marsh habitat in species-specific
responses to human impacts (King et al. 2005). Blue crab (C. sapidus) and two species
of tellinid bivalve (Macoma balthica and M. mitchelli) exhibited higher abundances in
association with marsh shorelines, which were, in turn, influenced by larger development
patterns in the surrounding watershed. In temperate freshwater lakes, fish species
richness changed in association with local changes in shoreline structure (Jennings et al.
1999); unstructured sites and those structured sites with higher habitat complexity, such
as riprap, had higher species richness than sites containing seawalls, a trend that the
authors could not isolate from larger changes in watershed development.
Some work has focused on the direct effects of bulkhead and riprap—as
compared with natural shoreline—on fish communities in Chesapeake Bay (Bilkovic and
Roggero 2008). Until recently, however, little has been done to exclusively examine
these direct, local impacts on shallow-water infaunal communities. In lower Chesapeake
Bay, three polyhaline systems—the York River, the Elizabeth-Lafayette River system,
and the Lynnhaven River system—have been studied. In the York River, total infaunal
diversity and density were higher adjacent to natural marsh shorelines, gradually
decreasing from riprap to bulkhead (Seitz et al. 2006). In the Elizabeth-Lafayette River
system, densities of M. balthica were significantly higher adjacent to natural marsh than
to riprap or bulkhead. Somewhat to the contrary, in the shallow, productive Lynnhaven
River system, benthic densities adjacent to riprap shorelines were marginally higher
compared to marsh and bulkhead, though shoreline type in general did not appear to
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affect other benthic variables such as diversity and biomass (Lawless 2008). In
comparisons among three tributaries with 40-80% of the shoreline as natural marsh,
abundance and diversity were intermediate near riprap shorelines, and depended on
landscape features. In more developed systems (e.g., Elizabeth-Lafayette system),
benthos adjacent to riprap was depauperate, whereas in less-developed tributaries (e.g.,
York River and Broad Bay), benthos near riprap was abundant and was similar to that
near natural marsh shorelines (Seitz and Lawless 2008). Though these studies shed light
on the biological consequences of shoreline development in the lower Chesapeake Bay,
the paucity of work showing the direct impacts of shoreline modification on benthic
infauna, the likelihood of continued rises in the coastal population, and the general
concerns of sea level rise and global change, call for further investigation of these trends,
particularly in the mesohaline upper Bay.
Study Objectives and Hypotheses
The specific objectives of this study were to determine the local effects of
shoreline development on the density, diversity, and biomass of benthic infauna and
predators within the Patuxent River, in the Maryland portion of Chesapeake Bay. Results
from three shoreline types were compared: natural marsh, riprap, and bulkhead. While
some work has been conducted to model land use change and its effects on aspects of
watershed function and integrated hydrodynamics in the Patuxent (Costanza et al. 2002),
no previous work has been conducted to examine the direct, local impacts of shoreline
development on benthic fauna in this tributary. Based on results from a pilot study
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conducted in June 2008, I hypothesized that both infaunal and predator responses would
differ among shoreline types, with natural marshes supporting higher infaunal density,
diversity, and biomass as well as higher predator abundances than either riprap or
bulkhead. Moreover, I proposed that riprap, while undesirable when compared to natural
shoreline, would be less likely to negatively affect the benthos than bulkhead due to its
more subtly graded angle and higher habitat complexity.
I also examined the effects of additional variables on benthic community
response. Sediment grain size not only influences benthic community composition
(Boesch 1973; Diaz and Schaffner 1990; Snelgrove and Butman 1994; Snelgrove 2000)
but also varies between hardened and natural shorelines (Ahn and Choi 1998; Seitz et al.
2006; Lawless 2008; Partyka and Peterson 2008). Accordingly, it was measured across a
shoreline gradient to determine its role in benthic community dynamics. Additionally,
because benthic infauna may be subject to both top-down (i.e., predation) and bottom-up
(i.e., resource availability) controls in Chesapeake Bay (Seitz and Lipcius 2001), the
influences of predator abundance, sediment organic carbon (TOC)/total nitrogen (TN)
content, and sediment chlorophyll a concentrations also were evaluated. Finally, I
hypothesized that wave energy would influence benthic fauna in the Patuxent River, as
natural physical processes such as tidal currents and wind-driven flow also can affect
community structure through changes in many factors, including sediment type and
transport, resource availability, recruitment, and physical stress (Eckman 1983; Jumars
and Nowell 1984; Emerson 1989; Snelgrove and Butman 1994; Schaffner et al. 2001).
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Data from the Patuxent River were assessed using an Information-Theoretic
approach in which a set of multiple regression models was established, and several
factors were compared to determine the most influential variables on infaunal community
structure. Overall results from the mesohaline Patuxent can be compared with previous
studies from several (aforementioned) polyhaline systems in coastal Virginia to develop a
more spatially comprehensive understanding of the effects of shoreline hardening on
shallow-water infaunal communities throughout Chesapeake Bay.
MATERIALS AND METHODS
Study Location and Site Selection—This study took place in the Patuxent River,
Maryland, a sub-estuary of Chesapeake Bay. The Patuxent River, a major tributary
within the state of Maryland, is approximately 177 kilometers long and drains an area of
roughly 2,352 square kilometers (Dail et al. 1998). The shoreline of the Patuxent is
comprised of 22% natural marsh, with 34% natural marsh within the study area (Berman
et al. 2003, 2006). Forty-five sampling sites were selected (Figure 1) based on the results
of a power analysis from a pilot study conducted in June 2008. The river was divided
into three zones as follows: (1) up-river (Zone A) with an upper end-point at N
38˚30’40.9”, W 76˚39’49.3”; (2) mid-river (Zone B); (3) down-river (Zone C) with a
lower end-point at N 38˚19’10.4”, W 76˚25’20.6” and fifteen sampling sites were
selected within each zone (Figure 1). To account for variation in response variables from
the 2008 pilot study, those data were bootstrapped using R v. 2.7.2. A separate
simulation was run for each response variable to determine the necessary number of
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samples per shoreline type for the current study. Using the plots created from the
simulation (e.g., Appendix, Figure A1), I chose a sample size of 15 replicates of each
shoreline type, corresponding with an acceptable decrease in standard error across all
responses for all three shoreline types. Data on shoreline type along the Patuxent were
obtained from the Center for Coastal Resources Management (CCRM) data files (Berman
et al. 2003; 2005), and sites were randomly selected based on the following three criteria:
(1) at least 80 continuous meters of a given shoreline type; (2) salinity of at least 5 psu,
and (3) water depth no greater than 1.2 meters. The salinity restriction ensured the
collection of estuarine rather than freshwater fauna, while the depth restriction not only
maintained the focus of the study on shallow-water benthic communities, but also
represents the functional limit of the primary sampling equipment. If a randomly selected
site did not meet the pre-determined criteria once examined in the field, a back-up
location from the randomized list of all possible sampling locations, and one that also
adhered to the criteria, was used. The placement and availability of back-up locations
resulted in Zone B containing four bulkhead and six riprap sites, and Zone C containing
six bulkhead and four riprap sites (Figure 1). All macrofauna and other associated
samples (described below) were taken approximately five meters from the shoreline at
each site. Infauna, water quality, and sediment sampling took place between 23 and 26
June 2009. Predator sampling took place from 13 through 16 July 2009.
Water quality and Sediment—A calibrated YSI Pro-Plus Multi-Parameter Water
Quality meter was used at each site to determine water temperature (˚C), salinity (psu),
and dissolved oxygen (mg l-1) levels. At each site, two sediment samples were taken to a
10
depth of 5 cm with a 2.26-cm-diameter syringe core. These were used for the analysis of
grain size and total organic Carbon and Nitrogen (TOC/TN) content. Grain-size analysis
for each site was conducted using a standard wet-sieving and pipetting technique (Plumb
1981). The remaining sediment samples were sent to an external laboratory where they
were run through an Exeter CE440 elemental analyzer to quantify Carbon, Hydrogen, and
Nitrogen (CHN) content.
A 1.1-cm-diameter syringe core was taken to a depth of 20 mm. Three depth
segments—0-3 mm, 4-10 mm, and 11-20 mm—were analyzed for the concentration of
chlorophyll a and phaeophytin. Sediment samples were stored on ice in the field and
protected from light to avoid pigment degradation. Upon returning to the lab, samples
were frozen until the time of analysis. Photopigments were extracted using methods
developed by Lorenzen (1967) and Pinckney (2007), modified by the addition of a
sonification step and the use of 90% acetone as an extractant (I. Anderson, pers. comm.).
Final processing was conducted with a Beckman-Coulter DU 800 spectrophotometer.
Wave Exposure Model—In this study, I also used the recently developed Wave
Exposure Modeling program (WEMo 4.0) to develop a mechanistic explanation for
differences in benthic infauna across a shoreline gradient. WEMo uses a numerical, onedimensional model based on linear wave theory to calculate wave propagation in shallow
coastal waters, accounting for the combined effects of wind-wave generation, shoaling,
and dissipation (Malhotra and Fonseca 2007). Thus, outputs of incorporated bathymetry,
wind, shoreline coverage, and sampling point data were analyzed to determine the
influence of physical variables on the benthic community response at each site. ArcGIS
11
9.3 was used to create several system-specific data sets: (1) bathymetry, (2) shoreline
coverage, and (3) sampling point locations, all of which were incorporated into WEMo
4.0 (Fonseca and Malhotra 2010). Wind data was also incorporated and was downloaded
from the NOAA National Data Buoy Center (Station # 8577330, SLIM2, Solomon’s
Island, MD, National Ocean Service). Only station data for the infauna sampling dates
(June) were used in the model. A bathymetric grid of the Patuxent River was created
using NOAA National Geophysical Data Center Coastal Relief Model software with an
available resolution of 3 arc-seconds. Shoreline coverage, which was used to determine
shoreline angle and effective fetch length, was created in ArcMap. Specific adjustments
were performed in WEMo 4.0 to accommodate inaccuracies in bathymetry and shoreline
data (A. Malhotra; M. Fonseca, pers. comm.). Each component was fed into WEMo
separately and overlaid to calculate an output value, an index of representative wave
energy (RWE). RWE represents the total wave energy (J m-1) in one wave length per
unit wave crest width (Malhotra and Fonseca 2007; USCOE 1977) at each sampling site.
Infauna—At each site, deep-dwelling macrofauna were collected using a benthic
suction sampler (Orth and van Montfrans 1987; Eggleston et al. 1992). A 0.11-m2 PVC
core was inserted into the sediment to a depth of approximately 30 cm and all sediment
and animals were removed. Material collected was sieved in the field on 3-mm mesh,
placed on ice, and frozen upon returning to the lab. Suction samples were sorted in the
laboratory to separate infauna from sediment and detritus. Organisms were identified to
the lowest possible taxonomic level (usually species) and stored in a solution of 70%
ethanol (EtOH) until dry weights could be obtained.
12
An additional 0.011-m2 hand-pushed core was taken at each site to a depth of
approximately 10 cm and sieved in the field on 500-µm mesh. Due to logistical
constraints, only samples from 21 of the 45 shorelines were processed. Organisms were
stored in ambient water in the field and transferred to the laboratory in a solution of
Normalin® preservative. In the lab, samples were transferred to 70% EtOH, stained with
Rose Bengal vital stain, and sorted under a dissecting microscope. Organisms were
identified to the lowest possible taxonomic level (usually species) and stored in a solution
of 70% EtOH until dry weights could be obtained.
Once individuals were sorted and identified, density estimates (per m2) for each
sampling method (core) were obtained by standardizing raw infaunal abundance per
sample by the surface area of the core. PRIMER v. 6.1.6 was used to calculate Shannon
diversity (H’), which integrates species richness and evenness (Krebs 1998), at each site.
Organisms that were not identifiable to at least family (e.g., misc. decapod) were not
included in diversity calculations. All organisms were dried for 48 hours at 70˚C, and
then ashed in a muffle furnace for 6 hours at 550˚C to obtain ash-free dry weights
(AFDW). For the 3-mm samples, bulk weights of each species per site were taken except
for the large bivalve Macoma balthica, which was weighed individually across all sites.
For 500-µm samples, bulk weights of all taxa were taken unless a species appeared to
contribute substantially to overall abundance. Thus, the bivalves Gemma gemma and
Mya arenaria, the polychaete Neanthes succinea, and the amphipod Leptocheirus
plumulosus were weighed separately from the larger taxonomic groups.
13
I also assessed infaunal feeding guild structure at each shoreline type. Although
N. succinea is frequently classified as both a surface deposit feeder and an omnivore, I
categorized it here as a surface deposit feeder. Feeding mode descriptions for infaunal
species (Tables 4, 5) were obtained from the literature (Wilcox and Jeffries 1974;
Fauchauld and Jumars 1979; Rader et al. 1984; Bianchi and Rice 1988; Ruppert and Fox
1988; Diaz and Schaffner 1990; Hentschel 1998; Cunha et al. 2000).
Predators—Estimates of predator abundance were obtained using a small otter
trawl (2-m-wide, 4.9-m-long, and 3/8-inch-mesh). At each site, 2-minute trawls were
performed both with and against the current, and both trawls were pooled and analyzed as
one sample. Organisms collected were identified and measured in the field and
immediately released (as per approved IACUC regulations; permit # IACUC-2009-0710-6074-rdseit). Individuals that could not be positively identified were placed in an ice
slurry and taken back to the lab for species confirmation.
Statistical Analyses—Infauna data were analyzed with an Information-Theoretic
approach, using Akaike’s Information Criterion (AIC), a statistical measure that explains
the amount of information “lost” when a model approximates reality (Gotelli and Ellison
2004; Anderson 2008; R. Latour, pers. comm.). Parameters measured in the field were
used in varying combinations to construct a set of general linear models (Equations 1, 2).
𝑌 = 𝛽0 + 𝛽1 (𝑏𝑢𝑙𝑘ℎ𝑒𝑎𝑑) + 𝛽2 (𝑟𝑖𝑝𝑟𝑎𝑝) + 𝛽3 (𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡) + 𝛽4 (𝑝𝑟𝑒𝑑𝑎𝑡𝑜𝑟)
𝑌 = 𝛽0 + 𝛽1 (𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡) + 𝛽2 (𝑝𝑟𝑒𝑑𝑎𝑡𝑜𝑟)
14
(1)
(2)
General linear models represent a simple, direct way to characterize the relationship
between two variables and, thus, were used unless further assessment revealed the need
to explore non-linear methods. Due to the correlative nature of the three shoreline types,
each shoreline type could not be represented as an independent variable in the linear
models. Thus, only two were represented at a time. For models that included shoreline
type as an explanatory variable, natural marsh served as the constant or intercept value
(β0, Eq. 1). The relative strength of each model was determined by its individual AIC
value, calculated using Equation 3.
𝐴𝐼𝐶𝑐 = 𝑛 ∗ ln(𝜎 2 ) + 2𝑘 �
𝑛
�
(3)
𝑛−𝑘−1
Where,
𝑅𝑆𝑆
n is equal to the sample size, σ2 is equal to�
𝑛
�, or the residual sum of squares from a
linear regression ANOVA output divided by the sample size (n), and k is equal to the
number of parameters included in each model. The first, or “deviance” term, of Eq. 3
represents the lack of model fit, while the second term represents both a penalty for the
error associated with added parameters and a correction for low sample size (Anderson
2008).
Based on the AIC values, a weighted probability (Eq. 4) was calculated and used to
determine the model that best fit the data, using the guideline of a weight (wi) of 0.10 to
indicate strong support for a given model (Anderson 2008).
𝑊𝑖 =
1
exp(−2∆𝐴𝐼𝐶𝑐)
(4)
1
Σ𝑅
𝑟=1 exp(−2Δ𝐴𝐼𝐶𝑐)
15
A set of models was constructed for analysis of both benthic datasets (3-mm and
500-µm size fractions), consisting of varying combinations of the explanatory variables
that were hypothesized to most strongly affect infaunal response (Table 2). Due to the
strong correlation (R2 = 98%) between TOC and TN, both variables could not be
represented in regression models and, thus, only TOC was included. Before model
construction and regression analysis, each response variable was evaluated for normality
and homogeneity of variance through visual analysis of QQ plots as well as a Bartlett
test, and response data were Box-Cox transformed where assumptions were not met.
Analyses were completed using R-statistical software (version 2.7.2) and Minitab
(version 16), and an independent AIC analysis was conducted for each infaunal response
variable: density, diversity, and biomass. In addition, PRIMER v. 6.1.6 was used to
perform non-parametric analyses to help elucidate patterns in the data. These analyses
were less revealing than AIC analyses and are not presented.
Post-Hoc AIC Analyses—I did not include salinity or zone as explanatory
variables in the initial AIC model sets. The narrow salinity range, less than 4 psu
throughout the entire study area, that I observed in the 2008 pilot study was confirmed in
the current work. Additionally, Zones A, B, and C were established to ensure a stratified
random sampling design and to account for any spatial autocorrelation of the primary
“treatments” (i.e., shorelines), rather than to investigate any geographical trends.
However, due to a lack of high confidence in primary AIC results, I performed post-hoc
AIC analyses that incorporated salinity and distance upriver into the original model sets.
16
Distance upriver (km) was used as opposed to “Zone” because more robust results are
achieved with continuous data. See Appendix I for post-hoc AIC results.
Meta-Analysis of Bay-wide Infauna—Finally, I performed a meta-analysis to
gauge how my observations of infaunal communities in the Patuxent River compared
with infaunal community characteristics in other tributaries throughout Chesapeake Bay.
Infaunal density, diversity (H’), and biomass data from the Chesapeake Bay Program’s
(CBP) sampling in 1996-2006 (D. Dauer, pers. comm.) were assessed and compared with
my observed 2009 responses. For details on CBP design and collection methods, see
Seitz et al. 2009.
RESULTS
Water Quality, Sediment, and Wave Energy—Water temperature ranged from 24.3
to 30.6˚C, salinity from 7.13 to 11.01 psu, and dissolved oxygen ranged from 3.6 to 9.9
mg l-1 (Table 3). However, none of these variables differed considerably among
shoreline types. For analyses, the percentage of sand and gravel at each site was chosen
to represent sediment grain size and ranged from 9.65 to 100%. Mean grain size was
highest adjacent to riprap and lowest adjacent to natural marsh and was generally coarse,
with sand and gravel rarely falling below 50 percent. Wave energy for the system was
estimated to range from 10 to 184 J m-1. While each shoreline type exhibited a similar
range in values, the highest RWE values were associated with riprap sites, with
intermediate values at bulkhead and lowest at natural marsh. Mean total surface
chlorophyll (0-3 mm) ranged from 0.45 to 68.50 µg cm-2, was highest adjacent to riprap,
17
and was lowest adjacent to marsh. Both TOC and TN were higher adjacent to natural
marsh than to riprap or bulkhead and ranged from 0.1 to 14.32% for TOC and 0.014 to
0.86% for TN (Table 3). TOC and TN were highly correlated with one another (Figure
2), and also were inversely correlated with sediment grain size (Figures 3, 4).
Infauna, 3-mm Suctions—Across the 45 sampling sites, 14 benthic species were
collected (Table 4). Bivalves were the dominant taxa (8 species), followed by
polychaetes (4 species) and crustaceans (2 species). Of the bivalves, M. balthica and G.
gemma were the most common, while N. succinea was the most prevalent polychaete.
Within these larger, deeper cores, bivalves made up anywhere from 14% to 100% of the
total community, with their contribution seldom falling below 50%. Contribution of
polychaetes to overall density ranged from 0% to 86%. Bivalves were also biomass
dominants, making up an average of 75% across all shoreline types, while polychaetes
made up an average of 4.3% of the overall biomass and crustaceans 0.09%.
Patterns in density, diversity, and biomass differed by shoreline type. Mean
infaunal density was highest adjacent to riprap shorelines, and was ~3 times greater than
natural marsh, where density was lowest, but variability was high (Figure 5). Mean
Shannon Diversity (H’) was higher adjacent to bulkhead than natural marsh, and each
exhibited ~1.5 to 2 times the diversity associated with riprap (Figure 6). Natural marsh
was associated with the highest mean infaunal biomass and bulkhead was marginally
lower. However, the elevated bulkhead biomass was due to one large bivalve (M.
arenaria, shell length: 78 mm), an unrepresentative value that was removed prior to
further analyses (Figure 7A, B). After the removal of the outlier, the lowest mean
18
biomass was associated with bulkhead, which was roughly 1.5 to 2.5 times lower than
either riprap or natural marsh, respectively.
Infauna, 500-µm Cores—Across the 21 sites, 29 species were collected (Table 5).
Polychaetes were the dominant taxa (14 species), followed by crustaceans (9 species) and
bivalves (6 species). N. succinea and L. plumulosus were the most abundant polychaete
and crustacean, respectively, while G. gemma and M. arenaria made up a majority of the
bivalves. Within these small cores, contribution of bivalves to overall density ranged
from 0 to 97%, while polychaetes made up 3% to 88%. The community was more evenly
distributed among taxa in the 500-µm cores, particularly concerning crustacea, whose
contribution ranged from 0.59% to 88% in 16 of 21 sites, as opposed to 17% at only one
site in the 3-mm cores. Bivalves made up the greatest percentage of the overall biomass
at ~53%, while polychaetes made up ~24% and crustaceans ~7.5% of the total.
Similar to the 3-mm samples, mean density was highest adjacent to riprap, but
was lowest adjacent to bulkhead and variation was high (Figure 8). Both natural marsh
and bulkhead shorelines supported a mean density that was ~1.5 times lower than riprap.
Diversity was greatest at natural marsh and, similar to 3-mm results, was lowest at riprap
shorelines (Figure 9). The highest biomass occurred at riprap and was marginally lower
at natural marsh, and each was ~2.5 times greater than that at bulkhead sites (Figure 10).
Predators—Seventeen species of benthic predators were collected across all sites
(Table 6). The highest mean abundance of individuals was associated with natural
marsh, which supported approximately twice the mean abundance of either riprap or
bulkhead (Figure 11). Dominant species included white perch (Morone americana), blue
19
crab (Callinectes sapidus), and spot (Leiostomus xanthurus). Of these species,
abundance of M. americana was approximately 1.5 to 2.5 times greater in association
with natural marsh, and abundance of C. sapidus was approximately three to four times
greater near natural marsh shorelines. Bulkhead shorelines, on average, supported
slightly greater numbers of L. xanthurus relative to marsh or riprap sites.
Factors Predicting Community Structure—For 3-mm density, representative
wave energy (RWE) emerged as the best predictor (probability = 0.52), though sediment
chlorophyll concentration was also a strong predictor (probability = 0.14; Table 7).
Density was slightly positively correlated with both variables (back-transformed data; R2
= 5.0%, 1.8% respectively). Shoreline type alone was the best predictor of 3-mm
infaunal diversity (probability = 0.32; R2 = 11.6%), although models with sediment
chlorophyll alone (probability = 0.13), percent TOC alone (probability = 0.12), and
predator abundance alone (probability = 0.11; Table 8) also had strong support. Higher
diversity was associated with both higher chlorophyll levels and percent TOC (R2 =
3.0%, 2.3%), and diversity exhibited a weakly negative correlation with predator
abundance (R2 = 2.0%). Biomass was best predicted by sediment type (probability =
0.39), and predator abundance (independently and combined with sediment type) also
emerged among the top models (probability = 0.18, 0.21; Table 9). While biomass was
positively correlated with predator abundance (back-transformed data; R2 = 7.5%), it
showed a negative relationship with sediment grain size (R2 = 3.5%).
The same set of models was used for analysis of the 500-µm dataset (Table 2).
For 500-µm density, like the 3-mm results, sediment chlorophyll concentration and wave
20
energy emerged among the top predictors (probability = 0.46, 0.12) and were positively
correlated with the response (back-transformed data; R2 = 14.2%, 8.8%). Additionally,
density was predicted by sediment type (probability = 0.17; Table 10), with higher
densities occurring with lower percentages of sand and gravel (R2 = 8.8%). Sediment
chlorophyll and wave energy also were the two strongest predictors of diversity
(probability = 0.66; 0.23; Table 11), and both showed a negative correlation with this
response variable (R2 = 27.3%; 19.8%). As with the 3-mm size fraction, 500-µm
biomass was predicted by predator abundance (probability = 0.43), sediment type
(probability = 0.15) and the model containing both variables (probability = 0.12). Unlike
the dataset for the larger size fraction, however, percent TOC also came out among the
top models for predicting 500-µm biomass (probability = 0.11; Table 12). Biomass
showed a positive relationship with predator abundance (Figure 12), and a negative
relationship with sediment grain size (back-transformed data; R2 = 10.2%), similar to 3mm results, and was positively correlated with percent TOC (R2 = 7.1%).
Post-Hoc Analyses—The addition of salinity and distance variables to each model
set achieved varied results. While distance upriver only emerged among the top models
for predicting 3-mm biomass, salinity affected this response as well as 500-µm density
and biomass. Density increased with higher salinities, and predictably, while 3-mm
biomass was positively correlated with distance upriver, it also showed a negative
relationship with salinity. Diversity in both size fractions remained unaffected by the
addition of these factors. Inclusion of these predictor variables did not increase the
21
clarity of results, and, as post-hoc additions, their interpretation should be treated with
caution (Anderson 2008).
Community Composition by Shoreline Type—Species-specific differences were
observed along the shoreline gradient. In the 3-mm size fraction, while G. gemma and M.
balthica were the most abundant bivalves overall, they differed by shoreline type. G.
gemma was considerably more abundant at riprap and bulkhead compared to natural
marsh. For M. balthica, marsh and riprap shorelines supported slightly higher densities
than bulkhead. Like G. gemma, I observed higher numbers of the polychaete N. succinea
adjacent to riprap and bulkhead than to natural marsh (Figure 13). Of the dominant
species in the 500-µm size fraction, riprap and bulkhead again supported higher numbers
of G. gemma than marsh shorelines. M. arenaria was more abundant at natural marsh
than riprap or bulkhead, and marshes also supported noticeably higher densities of the
burrowing amphipod L. plumulosus. Natural marsh and bulkhead supported comparable
numbers of the polychaete N. succinea in this size fraction, and both supported higher
numbers than riprap shorelines (Figure 14).
In the 3-mm samples, biomass was highest at natural marsh, intermediate at
riprap, and lowest at bulkhead shorelines, with bivalves contributing considerably more
than other taxa in this larger size fraction (Figure 15). In the smaller size-fraction, though
bivalves remained dominant, polychaetes and crustaceans contributed more to overall
biomass relative to their contribution in the 3-mm samples (Figure 16).
I saw differences between size fractions in the distribution of feeding modes
among the three shoreline types. With the larger infauna, natural marsh shorelines
22
consisted primarily of deposit feeders, followed by suspension feeders and then
carnivores and omnivores. Riprap and bulkhead were more similar at this size fraction
and were both largely made up of suspension feeders and deposit feeders (Figure 17). In
the smaller fraction of the infaunal community, all shoreline types were primarily made
up of suspension-feeding species, followed by deposit feeders and, similar to the larger
fraction, a small percentage of carnivores and omnivores (Figure 18). Natural marshes in
both size fractions had a more even distribution of all feeding modes than the other
shoreline types.
Meta-analysis—In a meta-analysis of my infaunal data and a collection of benthic
data gathered over ten years (1996-2006) by the Chesapeake Bay Program, the density,
diversity (H’), and biomass of infauna from 2009 exhibited varied results in comparison
with the long-term data. Infaunal density for the 3-mm samples in 2009 was much lower
on average compared to either the 10-year mean density for the Patuxent (which was
lower than all other tributaries) or mean density in other Chesapeake Bay tributaries. The
range in density for the Patuxent over 10 years was also among the lowest of the Bay
tributaries. The 2009 mean for 500-µm samples, however, was markedly higher than
many of the CBP defined strata over the 10-year period (Figure 19). The diversity
observed in 2009 in 3-mm and 500-µm samples was lower than the Patuxent 10-year
mean from CBP samples and it was among the three lowest when compared with other
Bay strata (Figure 20). Biomass in 2009 was higher than the long-term mean for several
strata, particularly in the low- to mid-Bay (Figure 21a). However, when data were
broken down into low- and high- mesohaline regions, both 3-mm and 500-µm biomass
23
exceeded that of every stratum except the Upper Bay (e.g., Figure 21b), and this response
variable is where clearer differences by shoreline type were seen. Three-mm data were
included in the interest of a thorough evaluation, but since a sieve size of 500-µm was
used in collection of CBP data, the smaller infaunal size fraction from 2009 may
represent a more reasonable comparison with CBP results.
DISCUSSION
General Community Characteristics
Numerically dominant infauna in the Patuxent River included the bivalves M.
balthica, M. mitchelli, G. gemma, and M. arenaria, the polychaete N. succinea, and the
amphipod L. plumulosus, all of which are known to exert a strong presence in benthic
communities throughout Chesapeake Bay (Holland 1985; Hines and Comtois 1985; Diaz
and Schaffner 1990; Seitz et al. 2006; Lawless 2008; Gillett and Schaffner 2009). The
numerical and biomass dominance of bivalves and polychaetes in the Patuxent is also
consistent with other findings in shallow polyhaline (Schaffner et al. 2001; Seitz et al.
2001; Lawless 2008) and mesohaline (Hines and Comtois 1985; Holland 1985) systems
in the Bay. Most of the infaunal crustaceans – while generally commonly observed
species throughout Chesapeake Bay – were not efficiently sampled with the larger mesh;
hence they were only abundant within the smaller size fraction. Dominants of the benthic
predator community in the Patuxent River, such as M. americana, C. sapidus, and L.
xanthurus, as well as other less-abundant species like summer flounder (Paralichthys
24
dentatus) and hogchoker (Trinectes maculatus), are widely observed throughout the
region’s shallow-water habitats (Virnstein 1977; Holland et al. 1980; Murdy et al. 1997).
Infaunal Density and Diversity
Density—Shoreline type did not influence density in the current study. Rather,
wave energy and chl-a concentration best predicted changes in faunal density in both size
fractions, suggesting that these are the likeliest predictors of the response, although
variation was high. Sediment type also emerged as an influential factor for 500 µm
density (Table 13). Highest infaunal densities were observed with the highest wave
energy and concentrations of benthic chl-a, both of which occurred in conjunction with
riprap shorelines. Riprap also had the highest overall percentage of sand and gravel
relative to the other shoreline types, which may be due to the high energy dynamics,
which allow only large particles to settle out of suspension (Kennett 1982). The low
levels of fine sediments in these areas would result in lower turbidity levels, potentially
increasing water clarity and light availability, and fostering of the establishment of
benthic microalgae and high levels of chl-a. This may either have provided a direct food
source to large, deposit-feeding species adjacent to these shoreline types, or played an
indirect role by fueling a more abundant meiofaunal prey community for small
omnivores.
Though little evidence has been found to support the idea of bottom-up control by
autotrophs on grazers or herbivores in pelagic or benthic systems (Michelli 1999; Posey
et al. 2002), evidence of infaunal response to changes in resource availability has been
25
found elsewhere in Chesapeake Bay. In a 2001 study, Seitz and Lipcius reported smallscale increases in the density of M. balthica in conjunction with increases in sedimentary
organic carbon, another important food source for these deposit-feeding clams (Pohlo
1982; Lopez and Levinton 1987). My results show greater densities of benthic species of
both size fractions in association with areas of high benthic chl-a, suggesting that areas of
high benthic primary productivity in the Patuxent River are able to support a more
abundant infaunal community. Sediment and wave effects, which also influenced the
benthos, may be secondary effects of shoreline development.
While previous work has examined general numerical differences in fauna
between hardened and natural shorelines, little has been done to quantify differences in
feeding guild structure in response to coastal development. In the Patuxent, I observed
differences in the density of infaunal feeding modes across the gradient of shorelines.
There was a greater variety of feeding modes associated with natural marsh for both size
fractions, potentially indicating a healthier habitat. Muddier sediments in natural marshes
can clog the feeding apparatus of suspension feeders, and higher levels of TOC benefit
deposit-feeders such as M. balthica and Spionid polychaetes, which accounted for a large
percentage of the surface-feeding portion of this group in marsh habitats. Although
densities of suspension-feeding infauna were highest adjacent to bulkhead sites, the
occurrence of this feeding mode was comparable at the two hardened shoreline types but
lower at natural marsh sites. The small, suspension-feeding bivalve G. gemma dominated
densities at riprap, and to a lesser extent at bulkhead. The success of this and other
suspension-feeding species in association with riprap may be due to secondary effects of
26
wave energy dynamics. The positive relationship between representative wave energy
(RWE) and density suggests that the relatively high wind-driven flow at riprap sites in the
Patuxent provides a mechanism to enhance vertical and/or advective mixing in the water
column, replenishing food particles that may be utilized by benthic suspension-feeders.
Flow dynamics are known to play a role in the transport and delivery of nutrients and
food to benthic communities (Emerson 1989; Leonard et al. 1998; Schaffner et al. 2001),
and this transport is particularly important for suspension-feeding species, which rely on
the water column for their food sources.
The energy level of shallow systems can dictate the type of substrate available to
the benthos, and these differences in substrate type can affect the local community. In
previous work, hardened and/or heavily impacted shorelines were associated with coarser
sediment grain size than natural shorelines (Ahn and Choi 1998; Jennings et al 1999;
Peterson et al. 2000; Seitz et al. 2006; Lawless 2008; Partyka and Peterson 2008). Some
sites with higher percentages of sands vs. muds tend to support higher benthic densities
(Boesch 1973; Seitz et al. 2006; Lawless 2008), and this relationship is reflected in the
current findings. However, despite the high densities observed in both infaunal size
fractions at Patuxent riprap sites, where grain size was highest, sediment type only played
a predictive role at the 500-µm level and induced a negative response, with lower
infaunal densities occurring with higher percentages of sand and gravel (as expected).
The emergence of sediment type as an important predictor of density in the 500-µm size
fraction, with lower infaunal densities occurring with higher percentages of sand and
gravel, indicates that increases in sediment grain size may be more likely to negatively
27
affect new recruits and the smaller members of the benthos in the Patuxent River. This
could be due to the effects of either abrasion of extremely small or delicate species or
their inability to effectively escape predation through burrowing activities. However, as
several correlates of sediment type may drive changes in infaunal community response
(Snelgrove and Butman 1994), other factors, which were not examined here, may have
played a role. The mean density and range of densities in the Patuxent, both in 2009 3mm samples and in the 10-year average from CBP sampling, was lower than all
Chesapeake Bay tributaries, suggesting that there was little scope for variation among
sites or shoreline types (i.e., all habitats were equally poor), which may be why there
were not major differences in density by shoreline.
Diversity—Shoreline type emerged in the strongest model for predicting 3-mm
diversity (Table 13). In this larger size fraction, I observed the highest infaunal diversity
adjacent to bulkhead shorelines, while natural marsh and riprap showed lower, yet
statistically comparable, values. There were clear differences in the two components of
the Shannon Diversity Index, richness and evenness, among the shoreline types, with
species evenness likely lowered at riprap shorelines due to the dominance of G. gemma.
In addition, for both the 2009 samples and the 10-year average from CBP samples, the
mean and range of Shannon diversity in the Patuxent was among the lowest of all
Chesapeake Bay tributaries, so the scope for change among shoreline types was low.
Diversity of large infauna (3-mm samples) was also strongly predicted by
sediment chl-a concentration and percent TOC, and it showed a positive relationship with
both variables. In terms of these predictors, bulkhead and marsh sites exhibited
28
contrasting relationships: natural marsh sites had relatively high levels of TOC and
relatively low levels of chl-a, with the opposite trends occurring at bulkhead sites. The
positive relationship between both variables and diversity (as well as their similarly
weighted probabilities) indicates that, regardless of shoreline type, greater food
availability may result in a greater ability to support more taxa and/or species (within the
larger size fraction), as competition for resources weakens (MacArthur 1972; Huston
1979).
In addition to evidence for bottom-up control on 3-mm infaunal diversity, I also
found weak evidence for top-down control. Predators influence species diversity in
general (as summarized in Menge and Sutherland 1976) and in benthic communities in
Chesapeake Bay (Virnstein 1977; Lawless 2008). The negative relationship between
predator abundance and infaunal diversity in the Patuxent indicates that predatory fishes
and crabs are able to locally limit the number of benthic species. Further support of this
trend is indicated when shoreline effects are examined simultaneously, as the highest
diversity of the larger infauna occurred in conjunction with bulkhead shorelines, which
also supported the lowest abundance of predators.
Like the larger size fraction of the community, diversity in the 500-µm size
fraction was strongly predicted by, though negatively correlated with, chl-a
concentration. Thus, while benthic chlorophyll may both directly fuel the larger (3-mm)
fraction of the community, and contribute to the resource pool by sustaining other
meiofaunal prey items (Montagna 1995), its contribution to the smaller size fraction is
less clear. Other factors that influence chl-a levels, such as sediment composition
29
(Snelgrove and Butman 1994; Cahoon et al. 1999), may have had an effect, though the
natural variation in the system may have masked their influence in favor of other
correlates (i.e., chl-a) driving changes in diversity.
Diversity in the 500-µm samples was also strongly predicted by RWE, with
higher diversity occurring in association with lower levels of wave energy. At this
sampling level, the highest H’ was associated with natural marsh sites, which were
subject to the lowest levels of wave energy in the Patuxent during my sampling period.
This may be because, at hardened shorelines, the higher wave energy provides a watercolumn food source that allows the dominance of suspension feeding species like G.
gemma in contrast to natural marsh sites, which had higher percentages of fine sediments.
At natural marsh sites, I observed a more even distribution of both species and feeding
modes, rather than the dominance of suspension-feeding species like G. gemma. I
suggest that the smaller size fraction of the benthic community is unable to cope with the
potentially high levels of suspended solids adjacent to marshes, which can clog feeding
apparatuses and otherwise cause stress and/or feeding inhibition at high concentrations
(Steele-Petrovic 1975; Fauchald and Jumars 1979). Additionally, the organic matter that
is exported from salt marshes can play a role in the food web of these habitats (Teal
1962; Currin et al. 1995), particularly for deposit-feeding species (Grall and Chauvauld
2002). Thus, the differences in food availability (i.e., allochthonous organic carbon) may
contribute to the wider range of species and feeding modes of small infauna at natural
marsh shorelines along the Patuxent.
30
Infaunal Biomass
Shoreline type did not emerge as a strong predictor of infaunal biomass at either
sampling level, though patterns for 3-mm samples showed higher biomass in natural than
developed shorelines (Figures 7B & 15). Biomass was strongly predicted by sediment
type, and higher percentages of fine sediments supported higher biomass in both faunal
size fractions (Table 13). Thus, natural marsh sites, which exhibited both the highest
percentages of fine sediments and the highest concentration of TOC, supported the
highest infaunal biomass at the 3-mm level. Riprap shorelines supported a biomass
slightly higher than (though comparable with) natural marsh at the 500-µm level and this
is likely due to both the presence of relatively larger (though fewer) individual M.
arenaria, as well as the extremely high densities (particularly of G. gemma) that I
observed at these sites.
At the 500-µm level, TOC concentration also emerged among the top predictors,
and higher biomass occurred with higher levels of sediment organic carbon. The high
infaunal biomass in conjunction with natural marsh as opposed to hardened shorelines is
consistent with observations of bivalve biomass in the York River, a subestuary of lower
Chesapeake Bay (Seitz et al. 2006). High levels of organic enrichment in sediments can
cause stress in the benthos, as oxygen is depleted and toxins accumulate (Pearson and
Rosenberg 1978; Hyland et al. 2005). However, several studies have reported higher
infaunal biomass in association with moderate (i.e., non-enriched) levels of sediment
organic carbon (Herman et al. 1999; Seitz and Lipcius 2001; Kemp et al. 2005), an
important food source for many infaunal species (Sanders 1958; Lopez and Levinton
31
1987). While little evidence directly links the TOC content of sediments with infaunal
community characteristics adjacent to natural versus hardened shorelines (Lawless 2008;
Partyka and Peterson 2008), it appears that the organic-rich fine sediments of natural
marshes in the Patuxent are more likely to support a higher biomass and more productive,
though less dense, benthic community than developed areas such as riprap and bulkhead.
Higher infaunal biomass in the Patuxent River also was associated with higher
predator abundance for both size fractions. Abundance of benthic fishes and crabs
previously has been associated with natural versus hardened shorelines in Chesapeake
Bay (Seitz et al. 2006; Bilkovic and Roggero 2008; Lawless 2008), and similar trends
have been reported in other estuarine (Peterson et al. 2000) and freshwater (Jennings et
al.1999; Goforth and Carman 2009) systems. This relationship between mobile fauna
and shoreline type has been attributed to the structural habitat heterogeneity associated
with natural coasts. Estuarine nekton can respond to differences in food availability, and
studies that have examined concurrent responses of infauna and predators to local habitat
alteration have reported positive relationships between these predators and their infaunal
prey (Seitz et al. 2003; King et al. 2005; Seitz et al. 2006; Partyka and Peterson 2008).
These patterns suggest, in addition to habitat heterogeneity, the influence of bottom-up
(e.g., food resource) control on mobile predators in these systems, and my data for the
Patuxent support these patterns. Several of the infaunal species observed in abundance at
natural marsh sites are known to serve as key prey items for common benthic predators in
Chesapeake Bay. For example, large, deep-burrowing bivalves like M. balthica (Hines
and Comtois 1985) can constitute up to 55% of the diet of C. sapidus (Hines et al. 1990;
32
Mansour 1992; Seitz et al. 2003; Lipcius et al. 2007), an ecologically and commercially
important species throughout the Bay and a primary contributor to the predator
community in this study. Epibenthic fishes also feed on clam siphons (Peterson and
Skillet 1994), and bivalves were a large percentage of infaunal biomass in both size
fractions. Thus, in addition to a loss of structural refuge, decreases in M. balthica and
other large, suitable prey species may be a contributing factor to the lower abundance of
predatory species associated with riprap and bulkhead shorelines.
Benthic biomass and/or secondary production has been used in previous
evaluations of health in Chesapeake Bay (e.g., Weisberg et al. 1997). Generally, higher
biomass is attributed to ecosystems that are not subject to external stressors like human
disturbances (Odum 1985), as the stable conditions of these areas are conducive to
advanced community successional stages and larger, non-opportunistic species.
Moreover, it may be said that “healthier” ecosystems support a larger range of trophic
levels, as they perpetuate the flow of energy throughout a system (Odum 1971). Thus,
based on the data collected in this study, natural marsh habitats in the Patuxent can be
considered healthier subsystems of the estuary compared to riprap or bulkhead due to
their tendency to support not only larger infaunal species, but also greater numbers of
commercially and recreationally important benthivorous predators that can capitalize on
these suitable food sources.
33
Post-hoc Analyses
The emergence of a positive correlation between density and salinity is expected,
as areas of higher salinity are known to support more productive and abundant infaunal
communities (Möller et al.1985; Diaz and Schaffner 1990). This trend is further
supported by a negative correlation between density and distance upriver, despite
distance (km from river mouth) not emerging as a strong predictor of density in this
study.
Both salinity and distance upriver emerged as predictors of biomass (in the larger
fraction) in the post-hoc analyses, with expected contrasting relationships. However, the
incorporation of both variables suggests the influence of additional factors that may vary
along the river gradient. Throughout the entire study area, salinity varied by less than
four psu, a range that is unlikely to influence the species observed here, many of which
are found throughout a wider range of salinity regimes throughout Chesapeake Bay.
Thus, factors such as freshwater and nutrient runoff, which were unaccounted for but
which also vary with distance, may have contributed to these trends. For example, landuse in the Patuxent River basin increases from predominantly forest and agriculture to
urban and residential development with distance upriver (Dail et al. 1998; Karrh et al.
2007). Furthermore, the northern portion of the watershed is bordered by two major
cities, (Washington D.C. and Baltimore), which may contribute to various upstream
loadings that may, in turn, influence benthic production.
34
The Patuxent River vs. Other Tributaries
Results of the current study show distinctions from methodologically similar
studies conducted in lower Chesapeake Bay (e.g., the amount of variation observed), but
are similar in terms of other patterns. Predators, for instance, exhibited expected patterns
(e.g., high at natural marsh, intermediate at riprap, low at bulkhead) based on
observations in the York River (Seitz et al. 2006), but differed from patterns observed in
the Lynnhaven River System (Lawless 2008). Interestingly, although polyhaline areas
tend to support more a productive and diverse benthic community, both density and
diversity of infauna in the Patuxent were similar to or higher than levels observed in both
the York and Lynnhaven systems. However, CBP data indicate that, over a longer time
scale, infaunal diversity is generally lower in the Patuxent and other mesohaline strata
than the polyhaline lower Bay. Only infaunal biomass differed markedly among these
short-term studies, with mean biomass at all shorelines in the Patuxent much lower than
that in the Lynnhaven. Also, abundance of predators in the Patuxent far exceeded the
abundance previously observed in shoreline studies of the York, Lynnhaven or Elizabeth
River systems.
The tidal Patuxent is about 95 kilometers long, with an average depth of 5.4 m
and a tidal range of 0.5-1.0 m (Boynton et al. 2008; Hartzell et al. 2010). Differences
observed in both infauna and predator response among the Patuxent, York, Lynnhaven,
and Elizabeth River systems may stem from issues related to depth, as the Lynnhaven is a
comparatively shallow, productive area (Dauer et al.1979; Seitz and Lawless 2008).
However, other physical differences such as freshwater input (of which the Lynnhaven
35
has little; Lawless 2008) or tidal flux, both of which can influence loadings (e.g.,
nutrients, organic matter) that affect productivity, may also play a role.
The dominant shoreline type should also be considered, as the relative
percentages (of bulkhead, riprap, and natural marsh) are different in these regions. Of the
miles of shoreline surveyed, the Lynnhaven and York River systems each consist of
approximately 70-90% natural marsh (Berman et al. 2007; Berman et al. 2008; Seitz and
Lawless 2008; Berman et al. 2009). This stands in stark contrast to the comparatively
highly developed Patuxent, with ~22% of marsh as a whole and 34% in our current study
area (Berman et al. 2003; Berman et al. 2006). These higher levels of development may
contribute to the overall low biomass if pollutant-based runoff in the Patuxent limits the
productivity of the system. However, another possibility is that alternative factors favor
the relatively higher abundance of benthivorous species in the Patuxent, the presence of
which may prevent the development of the high levels of infaunal secondary production
found in the Lynnhaven.
Assessment of Bay-wide data indicates that while some infaunal responses in
2009 samples fall within the long-term range of values observed in other major
tributaries, several factors highlight the Patuxent as a relatively degraded system. In
addition to 3-mm density from 2009 being markedly lower than the long-term mean, the
10-year average from CBP data also indicates that the Patuxent is generally depauperate
relative to other tributaries in the Bay. Diversity (in 2009 and CBP data) in the Patuxent
was also low compared with other tributaries, and the low range of both density and
diversity likely prevented the emergence of a distinct relationship along the shoreline
36
gradient. As in the comparison with the Lynnhaven, density (500 µm) far surpassed that
in every stratum except the Maryland mainstem. The patchy distribution of some of the
most abundant species in my study, especially G. gemma (Jackson 1968; Commito et al.
1995), may have resulted in elevated overall densities compared with broader trends if
sampling occurred more often in these dense patches and at a snapshot in time when this
species was in high abundance.
Otherwise, it is important to note that CBP infauna were sampled at deeper
locations than in the current work. Shallow benthic areas are typically more productive
than deep areas (Diaz and Schaffner 1990; Seitz et al. 2003; Seitz et al. 2006), and the
observed biomass patterns are consistent with this premise. The larger range in the 10year average biomass in the Patuxent relative to other CBP strata may explain why I was
able to see clearer trends in response to shoreline type in the current study.
CONCLUSIONS
In this study, I compared benthic infaunal and predator response across a
shoreline gradient in the Patuxent River, MD, to determine the influence of habitat
modification on these communities. While the trends suggest support for the hypothesis
that infaunal density, diversity, and biomass, as well as predator abundance, differ among
shoreline types, the system under question exhibited a considerable amount of variation.
In spite of this variability, I did find differences in community composition between
altered and natural shorelines. Thus, while hardened coastline may support an adjacent
37
benthic assemblage that is numerically comparable to that of a natural shore, the make-up
of infaunal species may be noticeably changed. Also, I observed differences among the
shoreline types in several additional variables, some of which emerged in the AIC
analysis as important predictors of infaunal responses, suggesting that changes observed
in the infaunal community may be the result of secondary impacts of shoreline hardening
on other influential factors.
Although benthic infaunal communities did not differ numerically among
shoreline types in the Patuxent River, there were decreases in infaunal diversity, biomass,
and predator abundance along a shoreline gradient from natural to hardened areas.
Furthermore, results from infaunal biomass analyses, as well as observations of predator
abundance, uphold the idea that riprap tends to act as an intermediate habitat compared to
bulkhead or natural marsh. I also show support for bottom-up control by infauna on
higher trophic levels in the Patuxent, specifically regarding biomass, given its weak
positive correlation with predator abundance and similar trends among shoreline types.
It is clear from both the degree of variability observed and the dispersal of
information through several predictive models, that a complex set of bottom-up and topdown factors interact to influence benthic infauna in the Patuxent River. Of the variables
that emerged as important predictors, many exhibited trends that showed variation by
shoreline type (e.g., sediment chl-a, %TOC, sediment grain size, predator abundance).
Perhaps a more rigorous sampling design or a larger sample size would help not only to
gain further insight as to mechanisms that drive changes in secondary variables, but also
to account for variability observed in the system as a whole.
38
The concurrent examination of both multiple habitat characteristics and multiple
trophic levels suggests that the impacts of shoreline hardening in the Patuxent River may
cause changes in natural habitats and that these changes may transfer to higher trophic
levels, ultimately affecting the local food web. With the evidence provided here, I
suggest that natural marshes are relatively healthier subsystems of the Patuxent River,
given that these areas support the presence of large, deep-dwelling bivalves, a wider
variety of infaunal feeding guilds, generally higher infaunal biomass, and a higher
abundance of predators than either riprap or bulkhead shorelines. Several qualities of
these habitats, such as higher sediment organic carbon/nitrogen content and lower wave
energy, may contribute to their maintaining higher levels of productivity and food web
complexity. The subtle changes that I was able to observe in infaunal response among
shoreline types within a relatively highly degraded tributary – where the ranges in longterm responses are low – provide compelling support for the preservation of natural
marsh habitats. The replacement of these and other natural shorelines with riprap and
bulkhead in Chesapeake Bay should be minimized in an effort to maintain the natural
qualities of these coastal communities, and other “living shoreline” options (e.g., riparian
buffer, marsh toe revetment, etc.) should be prioritized methods of erosion control.
39
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50
Table 1: Value of ecosystem services of tidal marshes. (Adapted from Bromberg-Gedan et al. 2009)
Ecosystem Service
Human Benefits
Average Value
(Adj. 2007 $ ha -1 year -1)
Disturbance regulation
Storm/shoreline protection
$2824
Waste treatment
Nutrient removal/transformation
$9565
Habitat/refugia
Fish/shrimp nurseries
$280
Food production
Fishing, hunting, gathering, aquaculture
$421
Raw materials
Fur trapping
$136
Recreation
Hunting, fishing, bird watching
$1171
Total
$14,397
51
Table 2: Explanatory variables and general linear model construction for each AIC calculation. Predictors contained in each model (g) are indicated in
parentheses. SL=Shoreline type; Sed. = Sediment type (percent sand + gravel); Pred.= Predator abundance; RWE = Representative Wave Energy; TOC =
percent Total Organic Carbon; Chl = Sediment chlorophyll a concentration; K= number of parameters in a given model (g1-g10), including σ2and β0. For
models containing shoreline type (i.e., g1, g2, g3, g4), β0 is represented by natural marsh. The global model (g1) includes all explanatory parameters.
Model
K
g1 (global)
g2 (SL+ Sed. + Pred.)
9
6
g3 (SL)
g4 (SL + Sed.)
g5 (Sed.)
g6 (Pred.)
g7 (Sed. + Pred.)
g8 (RWE)
g9 (TOC)
g10 (Chl)
Constant:
(β0)
Riprap
(x1)
Bulkhead
(x2)
Sediment
(x3)
Predators
(x4)
RWE
(x5)
TOC
(x6)
Chl
(x7)
β0
β0
β1
β1
β2
β2
β3
β3
β4
β4
β5
β6
β7
4
5
β0
β0
β1
β1
β2
β2
β3
3
3
4
3
3
3
β0
β0
β0
β0
β0
β0
β3
β3
β4
β4
β5
β6
β7
52
Table 3: Mean values of water quality, sediment, and wave energy measurements (±SE) at each shoreline type. Chlorophyll values represent
mean total chlorophyll (including chlorophyll a plus phaeophytin) from 0-3mm. TOC = Total Organic Carbon; TN = Total Nitrogen; RWE =
Representative Wave Energy.
Natural Marsh
Riprap
Bulkhead
27.25 (0.41)
27.09 (0.28)
27.34 (0.30)
9.40 (0.29)
9.60 (0.22)
9.49 (0.25)
Dissolved O2 (mg l )
6.92 (0.30)
7.48 (0.28)
7.13 (0.32)
% Sand + Gravel
72.15 (8.17)
91.02 (4.75)
89.51 (3.72)
Chlorophyll (µg cm-2)
6.31 (0.81)
10.99 (2.09)
10.37 (4.23)
% TOC
2.59 (1.13)
0.16 (0.01)
0.21 (0.06)
% TN
0.19 (0.07)
0.02 (0.00)
0.03 (0.01)
100.1847 (7.53)
105.3047 (7.75)
105.2153 (8.77)
Temperature (˚C)
Salinity (psu)
-1
RWE (J m-1)
53
Table 4: Species-specific 3-mm densities (Ind. m-2, ± SE) across three shorelines. Abbreviations for feeding modes are as follows: SD =
Surface deposit feeder; SF = Suspension feeder; O = omnivore; C = carnivore; BD = subsurface deposit feeder (see RESULTS for references).
Species Name
Natural
Marsh
Riprap
Bulkhead
Macoma balthica (SD, SF)
109.7 (30.8)
133.9
(76.4)
87.3
(38.3)
Macoma mitchelli (SD, SF)
18.80 (7.4)
45.5
(38.6)
19.4
(14.9)
Macoma tenta (SD, SF)
0
0.6
(0.6)
0
Mya arenaria (SF)
14.50 (9.7)
10.3
(9.7)
44.2
(21.7)
Mulinia lateralis (SF)
3.000 (1.5)
4.2
(3.1)
9.100
(4.1)
Gemma gemma (SF)
7.900 (6.7)
266.1 (119.0)
235.8 (121.0)
Rangia cuneata (SF)
0.600 (0.6)
0
1.200
(1.2)
Tagelus plebeius (SF)
0
0.6
(0.6)
2.400
(1.4)
Neanthes succinea (SD, O)
32.10(20.9)
77.6
(57.7)0
Leonereis culveri (SD, O)
1.200 (1.2)
0
0
Glycera dibranchiata (C, BD)
9.700 (9.7)
0
0
Leitoscoloplos spp. (BD)
0
0
3.000
(3.0)
Crangon septemspinosa (C, O)
0.600 (0.6)
0
0.600
(0.6)
Misc. decapod
0.600 (0.6)
0
0
539 (206)
467 (158)
Bivalves
Polychaetes
63.60 (38.7)
Crustacea
Total
199 (50.3)
54
Table 5: Mean species-specific 500-µm densities (Ind. m-2, ± SE) across three shorelines. Abbreviations for feeding modes are as follows: SD
= surface deposit feeder; SF = suspension feeder; O = omnivore; C = carnivore; BD = subsurface deposit feeder; HD = head-down deposit
feeder; ? = this feeding mode is possible, but unconfirmed (see RESULTS for feeding mode references). Spionid sp. 1 is one of two species in the
family Spionidae: Spiophanes bombyx or Marenzellaria viridis.
Species Name
Natural
Marsh
Riprap
Bulkhead
Bivalves
Macoma balthica (SD, SF)
168.8
(72.8)
90.9
(65.9)
Gemma gemma (SF)
1415.6 (934.0)
11168.8 (5702.0)
6168.8 (3004.0)
Mya arenaria (SF)
740.3 (550.0)
324.7
207.8
(106.0)
Mulinia lateralis (SF)
13.0
(13.0)
0
129.9
(115.0)
Macoma mitchelli (SD, SF)
51.9
(33.6)
51.9
(27.1)
39.0
(27.1)
Unidentifiable (tellinid) bivalve
26.0
(16.8)
13.0
(13.0)
0
(172.0)
64.9
(38.3)
Polychaetes
Neanthes succinea (SD, O)
1623.4 (416.0)
779.2
(288.0)
1558.4 (425.0)
Laeonereis culveri (SD, O)
26.0
(16.8)
39.0
(27.1)
13.0
(13.0)
Eteone heteropoda (C)
246.8 (106.0)
64.9
(43.1)
129.9
(86.1)
Eteone spp. (C)
168.8 (120.0)
51.9
(39.0)
103.9
(104.0)
Spionid sp. 1 (SD, SF)
311.7 (268.0)
13.0
(13.0)
142.9
(71.1)
Spionidae
90.9
13.0
(13.0)
0
Leitoscoloplos spp. (BD)
0
1454.5 (1409.0)
0
Glycinde solitaria (C)
0
0
13.0
(13.0)
Paraprionospio pinnata (SD, SF)
0
0
39.0
(27.1)
(90.9)
55
Orbinidae
0
13.0
(13.0)
0
Polydora cornuta (SD)
13.0
(13.0)
13.0
(13.0)
0
Capitella spp (BD).
220.8
(86.2)
350.6
(158.0)
26.0
(26.0)
Clymenella torquata (HD)
0
13.0
(13.0)
13.0
(13.0)
Demonax microphtalamus (SF)
13.0
(13.0)
0
Crangon septemspinosa (C, O)
26.0
(26.0)
13.0
Corophium spp. (SD)
77.9
(54.1)
0
Leptocheirus plumulosus (SF, SD)
3909.1 (3505.0)
103.9
(64.1)
480.5 (164.0)
Cyclaspis varians (SD)
116.9
(65.0)
77.9
(41.8)
103.9 (77.9)
Hargeria rapax (SD, O?)
13.0
(13.0)
13.0
(13.0)
13.0
Cyathura polita (C)
51.9
(52.0)
0
0
Harpacticoida (SD)
26.0
(26.0)
0
0
Unidentifiable amph.
26.0
(16.8)
0
0
Unidentifiable decapod
0
0
13.0
14662 (5673)
9286 (3079)
0
Crustacea
Total
9377 (3646)
(Table 5, con’t)
56
(13.0)
0
26.0
(16.8)
(13.0)
(13.0)
Table 6: Mean abundance (No. of ind., ± SE) of predator species across three shoreline types.
Species Name
Common Name
Natural
Marsh
Callinectes sapidus
Blue Crab
4.35 (1.56)
1.47 (0.61)
1.07 (0.36)
Morone americana
White Perch
13.07(6.10)
8.67 (3.96)
5.13 (1.90)
Leiostomus xanthurus
Spot
2.23 (0.76)
1.60 (0.99)
2.47 (0.84)
Morone saxitilis
Striped Bass
0.13 (0.13)
0.13 (0.09)
0.20 (0.15)
Pomatomus saltatrix
Blue Fish
0.13 (0.09)
0.07 (0.07)
0.07 (0.07)
Trinectes maculatus
Hogchoker
2.32 (0.90)
0.33 (0.16)
0.33 (0.21)
Paralichthys dentatus
Summer Flounder
0.33 (0.23)
0.07 (0.07)
0
Pseudopleuronectes americanus
Winter Flounder
0
0
0.07 (0.07)
Scophthalmus aquosus
Windowpane Flounder
0.07 (0.07)
0
0
Panopeus spp.
Mud crab
0
0.20 (0.15)
0.27 (0.21)
Gobiesox strumosus
Skillet fish
0.08 (0.07)
0
0.07 (0.07)
Fundulus heteroclitus
Mummichog
0.07 (0.07)
0
0
Cynoscion regalis
Weakfish
0.13 (0.09)
0
0.20 (0.15)
Micropogonias undulatus
Croaker
0
0.07 (0.07)
0.07 (0.07)
Malaclemys terrapin
Diamondback Terrapin
0
0.07 (0.07)
0
Opsanus tau
Oyster Toadfish
0
0.07 (0.07)
0
Arius felis
Hardhead Catfish
0.07 (0.07)
0
0
22.98 (7.33)
Total
57
Riprap
12.73 (3.99)
Bulkhead
9.93 (2.78)
Table 7: AIC results for 3-mm density. Bolded rows indicate models with strong support (weighted values
≥ 0.10). Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi.
Model
g8 (RWE)
g10 (Chl)
g5 (Sed)
g6 (Pred)
g9 (TOC)
g3 (SL)
g4 (SL+Sed)
g7 (Sed+Pred)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl)
K
AICc
∆AICc
wi
3
3
3
3
3
4
5
4
6
9
130.29
132.87
133.97
134.41
134.39
135.07
136.02
136.33
138.68
140.33
0.00
2.58
3.68
4.12
4.10
4.78
5.73
6.05
8.40
10.04
0.524
0.144
0.083
0.067
0.067
0.048
0.030
0.025
0.008
0.003
58
Table 8: AIC results for 3-mm diversity. Bolded rows indicate models with strong support (weighted
values ≥ 0.10). Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi.
Model
g3 (SL)
g10 (Chl)
g9 (TOC)
g6 (Pred)
g4 (SL+Sed)
g8 (RWE)
g5 (Sed)
g2 (SL+Sed+Pred)
g7 (Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl)
K
AICc
∆AICc
wi
4
3
3
3
5
3
3
6
4
9
-34.85
-33.09
-32.79
-32.62
-32.32
-32.28
-31.75
-30.57
-30.44
-25.82
0.00
1.76
2.06
2.23
2.53
2.56
3.10
4.28
4.41
9.03
0.322
0.134
0.115
0.105
0.091
0.089
0.068
0.038
0.036
0.004
59
Table 9: AIC results for 3-mm biomass. Bolded rows indicate models with strong support (weighted
values ≥ 0.10). Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi.
Model
g5 (Sed)
g7 (Sed+Pred)
g6 (Pred)
g10 (Chl)
g8 (RWE)
g9 (TOC)
g4 (SL+Sed)
g2 (SL+Sed+Pred)
g3 (SL)
g1(SL+Sed+Pred+RWE+TOC+Chl)
K
AICc
∆AICc
wi
3
4
3
3
3
3
5
6
4
9
-62.05
-60.76
-60.47
-57.97
-57.95
-57.81
-57.38
-56.07
-55.89
-50.54
0.00
1.30
1.58
4.09
4.11
4.24
4.68
5.99
6.16
11.52
0.392
0.205
0.178
0.051
0.050
0.047
0.038
0.020
0.018
0.001
60
Table 10: AIC results for 500-µm density. Bolded rows indicate models with strong support (weighted
values ≥ 0.10). Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi.
Model
g10 (Chl)
g5 (Sed)
g8 (RWE)
g9 (TOC)
g6 (Pred)
g4 (SL+Sed)
g7 (Sed+Pred)
g3 (SL)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl)
K
AICc
∆AICc
wi
3
3
3
3
3
5
4
4
6
9
56.09
58.01
58.84
59.42
60.07
60.92
61.07
62.00
64.92
75.55
0.00
1.93
2.75
3.33
3.98
4.83
4.98
5.92
8.83
19.46
0.455
0.174
0.115
0.086
0.062
0.041
0.038
0.024
0.006
0.000
61
Table 11: AIC results for 500-µm diversity. Bolded rows indicate models with strong support (weighted
values ≥ 0.10). Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi.
Model
g10 (Chl)
g8 (RWE)
g9 (TOC)
g3 (SL)
g5 (Sed)
g6 (Pred)
g4 (SL+Sed)
g7 (Sed+Pred)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl)
K
AICc
∆AICc
wi
3
3
3
4
3
3
5
4
6
9
-13.52
-11.45
-7.00
-6.97
-6.84
-6.83
-4.68
-3.78
-0.72
5.28
0.00
2.07
6.52
6.55
6.68
6.69
8.84
9.74
12.80
18.80
0.656
0.233
0.025
0.025
0.023
0.023
0.008
0.005
0.001
0.000
62
Table 12: AIC results for 500-µm biomass. Bolded rows indicate models with strong support (weighted
values ≥ 0.10). Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi.
Model
g6 (Pred)
g5 (Sed)
g7 (Sed+Pred)
g9 (TOC)
g10 (Chl)
g8 (RWE)
g3 (SL)
g4 (SL+Sed)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl)
K
AICc
∆AICc
wi
3
3
4
3
3
3
4
5
6
9
-48.82
-46.66
-46.32
-46.00
-45.47
-45.27
-42.85
-41.76
-40.96
-24.95
0.00
2.16
2.50
2.82
3.35
3.56
5.98
7.06
7.86
23.87
0.430
0.146
0.123
0.105
0.080
0.073
0.022
0.013
0.008
0.000
63
Table 13: Summary of predictors of all infaunal response variables. “Pos.” and “Neg.” indicate directionality of the observed relationships.
3 mm
Density
(Ind. m-2)
Shoreline Type
3 mm
Diversity (H’)
(bits ind. -1)
3 mm
Biomass
(g AFDW
m-2)
500 µm
Density
(Ind. m-2)
Neg.
Neg.
500 µm
Diversity
(H’)
(bits ind.-1)
500 µm
Biomass
(g AFDW
m-2)
Yes
Sediment Type (%
Neg.
sand + gravel)
Predator Abundance
RWE
Neg.
Pos.
% TOC
Chl-a
Pos.
Pos.
Pos.
Neg.
Pos.
Pos.
Pos.
Pos.
Pos.
64
Neg.
Figure 1: Location of zones (A, B, C) and distribution of sampling sites within each zone of the Patuxent River, MD (n = 15 per zone; total N = 45).
65
1.2
1
Percent TN
0.8
0.6
y = 0.07x + 0.01
R² = 0.98
0.4
0.2
0
0
2
4
6
8
10
12
14
Percent TOC
Figure 2: Correlation between percent Total Organic Carbon (TOC) and Total Nitrogen (TN) across all
sampling sites (n = 45).
66
16
14
Percent TOC
12
10
8
y = -0.0607x + 6.0961
R² = 0.2823
6
4
2
0
0
10
20
30
40
50
60
70
80
90
100
Percent Sand + Gravel
Figure 3: Relationship between percent Total Organic Carbon (TOC) and percentage of sand and gravel
across all sampling sites (n = 45).
67
1
0.9
0.8
Percent TN
0.7
0.6
0.5
y = -0.0046x + 0.4631
R² = 0.364
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
60
70
80
90
100
Percent Sand + Gravel
Figure 4: Relationship between percent Total Nitrogen (TN) and percentage of sand and gravel across all
sampling sites (n = 45).
68
800
Mean Density (ind. m -2) ± SE
700
600
500
400
300
200
100
0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 5: Effect of shoreline type on mean 3-mm density. Error bars represent one standard error (SE)
about the mean.
69
1.6
Mean H' (bits. ind.-1) ± SE
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 6: Effect of shoreline type on mean 3-mm diversity (H’). Error bars represent one standard error
(SE) about the mean.
70
12.00
A
Mean AFDW (g m -2) ± SE
10.00
8.00
6.00
4.00
2.00
0.00
Natural Marsh
Riprap
Bulkhead
Shoreline Type
B
12.00
Mean AFDW (g m -2) ± SE
10.00
8.00
6.00
4.00
2.00
0.00
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 7: (A) Effect of shoreline type on 3-mm biomass. Error bars represent one standard error (SE)
about the mean; (B) Biomass by shoreline type after removal of outlier (M. arenaria).
71
25000
Mean Density (ind. m-2)
20000
15000
10000
5000
0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 8: Effect of shoreline type on 500-µm density. Error bars represent one standard error (SE) about
the mean.
72
Mean H' (bits ind. -1) ± SE
2.5
2
1.5
1
0.5
0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 9: Effect of shoreline type on 500-µm diversity (H’). Error bars represent one standard error (SE)
about the mean.
73
7.0
Mean AFDW (g m-2)
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 10: Effect of shoreline type on 500-µm biomass. Error bars represent one standard error (SE) about
the mean.
74
Mean Predator Abundance (No. ind.) ± SE
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 11: Effect of shoreline type on predator abundance. Error bars represent one standard error (SE)
about the mean
75
20
18
Biomass (g AFDW m-2)
16
14
y = 0.0941x + 2.7119
R² = 0.280
12
10
8
6
4
2
0
-5
5
15
25
35
45
55
65
75
85
95
Predator Abundance (no. ind.)
Figure 12: Relationship between 500-µm infaunal biomass (back-transformed) and predator abundance
across 21 sampling sites.
76
600
Misc. decapod
Leitoscoloplos sp.
500
Mean Density (ind. m-2)
C. septemspinosa
G. dibranchiata
400
L. culveri
N. succinea
300
T. plebeius
R. cuneata
200
M. tenta
G. gemma
100
M. lateralis
M. arenaria
0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
M. mitchelli
M. balthica
Figure 13: 3 mm infaunal community composition by shoreline type. For SE of means, refer to Figure 5.
77
16000
G. gemma
Unidentifiable decapod
Unidentifiable amph.
14000
Harpacticoida
Cyathura polita
H. rapax
12000
C. varians
L. plumulosus
Corophium sp.
C. septemspinosa
Mean Density (ind. m-2)
10000
D. microphtalamus
Clymenella torquata
Capitella sp.
8000
P. cornuta
Orbiniidae
Paraprionospio pinnata
Glycinde solitaria
6000
Leitoscoloplos sp.
Spionidae
Spionid sp. 1
4000
Eteone sp.
E. heteropoda
L. culveri
N. succinea
2000
Unidentifiable (tellinid) bivalve
M. mitchelli
M. lateralis
M. arenaria
0
Natural Marsh
Riprap
Bulkhead
M. balthica
Axis Title
Figure 14: 500-µm infaunal community composition by shoreline type. For SE of means, refer to Figure 8.
78
8.0
7.0
Mean AFDW (g m-2)
6.0
5.0
4.0
Crustacean
Polychaete
3.0
Bivalve
2.0
1.0
0.0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 15: Taxa-specific composition of 3-mm infaunal biomass by shoreline type. For SE of means, refer
to Figure 7B.
79
5.0
Mean AFDW (g m-2)
4.0
3.0
Crustacean
Polychaete
2.0
Bivalve
1.0
0.0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 16: Taxa-specific composition of 500-µm biomass by shoreline type. For SE of means, refer to
Figure 10.
80
100
90
0
5
13
80
52
Percent (%)
70
63
60
50
40
Carn./Omn.
81
Suspension
Deposit
30
48
20
37
10
0
Natural Marsh
Riprap
Bulkhead
Shoreline Type
Figure 17: Feeding mode distribution of 3-mm samples across three shoreline types.
81
100
5
1
3
79
75
90
80
Percent (%)
70
60
65
50
Carn./Omn.
40
Suspension
30
Deposit
20
10
30
20
22
Riprap
Bulkhead
0
Natural Marsh
Shoreline Type
Figure 18: Feeding mode distribution of 500-µm samples across three shoreline types.
82
16000
14000
Mean Density (ind. m-2) ±SE
12000
10000
8000
6000
4000
2000
0
Bay Stratum
Figure 19: Density of benthic infauna in primary bay strata, as defined by the Chesapeake Bay Program.
JAM=James River; YRK=York River; VBY=Virginia Mainstem Bay; RAP=Rappahannock River;
PMR=Potomac River; MMS=Maryland Mainstem Bay; MET=Maryland Eastern Tributaries;
PXR=Patuxent River; MWT=Maryland Western Tributaries; UPB=Upper Bay. Orange bars (PAX 3 mm
and 500 µm) represent data from the current (2009) study. Grey bars represent long-term means from
1996-2006.
83
3.5
Mean H' (bits ind. -1) ± SE
3
2.5
2
1.5
1
0.5
0
Bay Stratum
Figure 20: Shannon Diversity (H’) of benthic infauna in primary bay strata, as defined by the Chesapeake
Bay Program. See Figure 20 for abbreviations. Orange bars represent data from the current (2009) study.
Grey bars represent long-term means from 1996-2006.
84
30
Mean AFDW (g m-2) ± SE
25
20
15
10
5
0
Bay Stratum
Figure 21a: Biomass of benthic infauna in primary bay strata, as defined by the Chesapeake Bay Program.
See Figure 20 for abbreviations. Orange bars represent data from the current (2009) study. Grey bars
represent long-term means from 1996-2006.
85
14
12
Mean AFDW (g m-2) ± SE
10
8
6
4
2
0
Bay Stratum
Figure 21b: Biomass of benthic infauna in high mesohaline regions of CBP strata (see Fig. 20 for
abbreviations). Orange bars represent data from the current (2009) study. Grey bars represent long-term
means from 1996-2006.
86
APPENDIX
Figure A1: Results of bootstrap simulation for infaunal density data from June 2008 pilot study.
Simulations were also run for infaunal diversity and biomass and resulted in similar patterns.
87
Table A1: Post-hoc AIC results for 3-mm density. Bolded rows indicate models with strong support (weighted values ≥ 0.10).
Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi. Adj. R2 = model adjusted R2 values.
Model
g8 (RWE)
g10 (Chl)
g5 (Sed)
g12 (Distance)
g9 (TOC)
g11 (Salinity)
g6 (Pred)
g3 (SL)
g4 (SL+Sed)
g7 (Sed+Pred)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl+Sal+Dist)
K
3
3
3
3
3
3
3
4
5
4
6
11
88
AICc
130.287
132.866
133.969
134.134
134.392
134.406
134.411
135.067
136.018
136.335
138.684
145.711
∆AICc
0.000
2.579
3.682
3.847
4.105
4.120
4.124
4.780
5.731
6.048
8.397
15.424
wi
0.460
0.127
0.073
0.067
0.059
0.059
0.058
0.042
0.026
0.022
0.007
0.000
Adj.R2
6.6%
1.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4%
0.0%
0.0%
4.0%
Table A2: Post-hoc AIC results for 3-mm diversity. Bolded rows indicate models with strong support (weighted values ≥ 0.10).
Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi. Adj. R2 = model adjusted R2 values.
Model
g3 (SL)
g10 (Chl)
g9 (TOC)
g6 (Pred)
g4 (SL+Sed)
g8 (RWE)
g11 (Salinity)
g5 (Sed)
g12 (Distance)
g2 (SL+Sed+Pred)
g7 (Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl+Sal+Dist)
K
4
3
3
3
5
3
3
3
3
6
4
11
89
AICc
-34.849
-33.092
-32.787
-32.619
-32.317
-32.284
-32.032
-31.746
-31.748
-30.571
-30.443
-23.054
∆AICc
0.000
1.757
2.062
2.230
2.532
2.565
2.817
3.103
3.101
4.278
4.406
11.795
wi
0.281
0.117
0.100
0.092
0.079
0.078
0.069
0.060
0.060
0.033
0.031
0.001
Adj.R2
7.4%
0.7%
0.1%
0.0%
5.1%
0.0%
0.0%
0.0%
0.0%
4.7%
0.0%
9.4%
Table A3: Post-hoc AIC results for 3-mm biomass. Bolded rows indicate models with strong support (weighted values ≥ 0.10).
Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi. Adj. R2 = model adjusted R2 values.
Model
g11 (Salinity)
g5 (Sed)
g12 (Distance)
g7 (Sed+Pred)
g6 (Pred)
g8 (RWE)
g10 (Chl)
g9 (TOC)
g4 (SL+Sed)
g2 (SL+Sed+Pred)
g3 (SL)
g1(SL+Sed+Pred+RWE+TOC+Chl+Sal+Dist)
K
3
3
3
4
3
3
3
3
5
6
4
11
90
AICc
-62.567
-62.053
-61.200
-60.755
-60.472
-57.946
-57.967
-57.809
-57.377
-56.066
-55.892
-49.100
∆AICc
0.000
0.514
1.367
1.812
2.095
4.621
4.600
4.758
5.191
6.501
6.675
13.467
wi
0.288
0.223
0.145
0.116
0.101
0.029
0.029
0.027
0.021
0.011
0.010
0.000
Adj.R2
8.1%
7.0%
5.2%
7.2%
3.7%
0.0%
0.0%
0.0%
3.1%
3.6%
0.0%
9.5%
Table A4: Post-hoc AIC results for 500-µm density. Bolded rows indicate models with strong support (weighted values ≥ 0.10).
Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi. Adj. R2 = model adjusted R2 values.
Model
K
3
3
3
3
3
3
3
5
4
4
6
11
g10 (Chl)
g5 (Sed)
g11 (Salinity)
g8 (RWE)
g12 (Distance)
g9 (TOC)
g6 (Pred)
g4 (SL+Sed)
g7 (Sed+Pred)
g3 (SL)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl+Sal+Dist)
91
AICc
56.087
58.012
58.610
58.835
58.895
59.421
60.069
60.918
61.070
62.003
64.917
91.209
∆AICc
0.000
1.926
2.523
2.749
2.808
3.334
3.983
4.832
4.984
5.916
8.831
35.122
wi
0.367
0.140
0.104
0.093
0.090
0.069
0.050
0.033
0.030
0.019
0.004
0.000
Adj.R2
13.2%
4.9%
2.2%
1.1%
0.8%
0.0%
0.0%
10.8%
0.0%
0.0%
5.2%
1.4%
Table A5: Post-hoc AIC results for 500-µm diversity. Bolded rows indicate models with strong support (weighted values ≥ 0.10).
Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi. Adj. R2 = model adjusted R2 values.
Model
g10 (Chl)
g8 (RWE)
g11 (Salinity)
g12 (Distance)
g9 (TOC)
g3 (SL)
g5 (Sed)
g6 (Pred)
g4 (SL+Sed)
g7 (Sed+Pred)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl+Sal+Dist)
K
3
3
3
3
3
3
3
5
4
4
6
11
92
AICc
-13.518
-11.452
-10.111
-9.647
-7.003
-6.973
-6.837
-6.830
-4.683
-3.782
-0.715
21.100
∆AICc
0.000
2.066
3.408
3.871
6.515
6.545
6.682
6.688
8.835
9.736
12.803
34.618
wi
0.540
0.192
0.098
0.078
0.021
0.020
0.019
0.019
0.007
0.004
0.001
0.000
Adj.R2
23.5%
15.6%
10.0%
8.0%
0.0%
4.8%
0.0%
0.0%
4.9%
0.0%
0.0%
15.2%
Table A6: Post-hoc AIC results for 500-µm biomass. Bolded rows indicate models with strong support (weighted values ≥ 0.10).
Abbreviations are as in Table 2. Weights (probabilities) are indicated with wi. Adj. R2 = model adjusted R2 values.
Model
g6 (Pred)
g5 (Sed)
g11 (Salinity)
g7 (Sed+Pred)
g12 (Distance)
g9 (TOC)
g10 (Chl)
g8 (RWE)
g3 (SL)
g4 (SL+Sed)
g2 (SL+Sed+Pred)
g1(SL+Sed+Pred+RWE+TOC+Chl+Sal+Dist)
K
3
3
3
4
3
3
3
3
4
5
6
11
93
AICc
-48.824
-46.660
-46.438
-46.320
-46.298
-46.000
-45.470
-45.266
-42.846
-41.759
-40.964
-10.961
∆AICc
0.000
2.164
2.386
2.504
2.526
2.824
3.354
3.558
5.978
7.065
7.860
37.863
wi
0.344
0.116
0.104
0.098
0.097
0.084
0.064
0.058
0.017
0.010
0.007
0.000
Adj.R2
11.8%
2.3%
1.2%
9.5%
0.6%
0.0%
0.0%
0.0%
0.0%
0.0%
8.1%
0.0%
VITA
CASSIE DANIELLE BRADLEY
Born in Kalamazoo, Michigan, 8 October, 1984. Salutatorian of graduating class of
2002, Galesburg-Augusta High School. Earned a B.S. in Zoology with High Honor from
Michigan State University in 2006. Entered the Master of Science program at the School
of Marine Science, Virginia Institute of Marine Science, College of William & Mary in
2008.
94