Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
DOI 10.1007/s12237-017-0214-5
Shoreline Hardening Affects Nekton Biomass, Size Structure,
and Taxonomic Diversity in Nearshore Waters, with Responses
Mediated by Functional Species Groups
Matthew S. Kornis 1,2 & Donna M. Bilkovic 3 & Lori A. Davias 1 & Steve Giordano 4 &
Denise L. Breitburg 1
Received: 8 September 2016 / Revised: 5 January 2017 / Accepted: 10 January 2017 / Published online: 24 April 2017
# The Author(s) 2017, corrected publication 2019
Abstract Coastal shoreline hardening is intensifying due to
human population growth and sea level rise. Prior studies have
emphasized shoreline-hardening effects on faunal abundance
and diversity; few have examined effects on faunal biomass
and size structure or described effects specific to different functional groups. We evaluated the biomass and size structure of
mobile fish and crustacean assemblages within two nearshore
zones (waters extending 3 and 16 m from shore) adjacent to
Communicated by Marianne Holmer
Electronic supplementary material The online version of this article
(doi:10.1007/s12237-017-0214-5) contains supplementary material,
which is available to authorized users.
* Matthew S. Kornis
matthew_kornis@fws.gov
Donna M. Bilkovic
donnab@vims.edu
Lori A. Davias
ladavias@gmail.com
Steve Giordano
steve.giordano@noaa.gov
Denise L. Breitburg
breitburgd@si.edu
1
Smithsonian Environmental Research Center, P.O. Box 28,
Edgewater, MD 21037, USA
2
Green Bay Fish and Wildlife Conservation Office, US Fish and
Wildlife Service, 2661 Scott Tower Drive, New Franken, WI 54229,
USA
3
Virginia Institute of Marine Science, College of William & Mary,
P.O. Box 1346, Gloucester Point, VA 23062, USA
4
Department of Commerce, NOAA National Marine Fisheries
Service, Southeast Regional Office, 263 13th Avenue South, St.
Petersburg, FL 33701, USA
natural (native wetland; beach) and hardened (bulkhead; riprap)
shorelines. Within 3 m from shore, the total fish/crustacean
biomass was greatest at hardened shorelines, driven by greater
water depth that facilitated access to planktivore (e.g., bay anchovy) and benthivore-piscivore (e.g., white perch) species.
Small-bodied littoral-demersal species (e.g., Fundulus spp.)
had greatest biomass at wetlands. By contrast, total biomass
was comparable among shoreline types within 16 m from
shore, suggesting the effect of shoreline hardening on fish biomass is largely within extreme nearshore areas immediately at
the land/water interface. Shoreline type utilization was mediated by body size across all functional groups: small individuals
(≤60 mm) were most abundant at wetlands and beaches, while
large individuals (>100 mm) were most abundant at hardened
shorelines. Taxonomic diversity analysis indicated natural
shoreline types had more diverse assemblages, especially within 3 m from shore, although relationships with shoreline type
were weak and sensitive to the inclusion/exclusion of crustaceans. Our study illustrates how shoreline hardening effects on
fish/crustacean assemblages are mediated by functional group,
body size, and distance from shore, with important applications
for management.
Keywords Shoreline modification . Shoreline armoring .
Habitat degradation . Fish . Crustaceans . Chesapeake Bay
Introduction
Estuaries and coasts are among the world’s most ecologically
productive ecosystems, providing numerous ecosystem services benefitting society (Lotze et al. 2006; Barbier et al.
2011). Dense human populations in coastal areas continue to
rapidly increase (Crossett et al. 2004), making estuarine systems especially vulnerable to human-linked stressors (Halpern
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et al. 2008; Barbier et al. 2011). Among these stressors, shoreline hardening has gained notoriety for numerous adverse
ecological effects (e.g., Dugan et al. 2011; Gittman et al.
2016a). Demand for shoreline hardening is driven by desire
to protect coastal land from erosion, and as such, shoreline
hardening will likely increase with global sea level rise
(Rahmstorf 2007; Arkema et al. 2013) and increasing human
populations in coastal areas (Crossett et al. 2004). Indeed,
replacement of natural shoreline by hardened structures (e.g.,
bulkheads, seawalls, riprap revetments) is growing in coastal
areas (Brody et al. 2008; Gittman et al. 2015) where natural
shorelines including tidal wetlands have already incurred substantial losses (Lotze et al. 2006; Gedan et al. 2009; Barbier
et al. 2011).
Until recently, available information on the ecological effects of shoreline hardening was limited (Seitz et al. 2006;
Dugan et al. 2011). Over the last few years, however, numerous studies have described a suite of deleterious ecological
effects. Fish (e.g., Toft et al. 2007; Bilkovic and Roggero
2008; Peterson and Lowe 2009; Kornis et al. 2017), invertebrates (e.g., Seitz et al. 2006; Bilkovic et al. 2006; Lowe and
Peterson 2014), submerged vegetation (e.g., Patrick et al.
2014, 2016), and microbial communities (Tan et al. 2015)
have all been shown to change over gradients of natural to
altered shorelines. Shoreline hardening has also been linked
to the expansion of nonindigenous plants (Vaselli et al. 2008;
McCormick et al. 2010; Kettenring et al. 2015; Sciance et al.
2016) and animals (Glasby et al. 2006; Bulleri and Chapman
2010), an indirect but substantial effect. Many studies have
focused on abundance and diversity patterns, and a recent
meta-analysis of prior studies found 23% lower biodiversity
and 45% lower organism abundance on bulkheads/seawalls
relative to natural shorelines (Gittman et al. 2016a).
Nevertheless, there remains an incomplete understanding of
the response of fish and crustacean assemblages to shoreline
hardening (Bilkovic & Roggero 2008; Strayer et al. 2012). For
example, very few studies have examined how faunal biomass
and size structure might be affected by shoreline hardening,
although such effects may be substantial and have important
implications for coastal management.
Our goal was to describe how the biomass, size structure,
and diversity of nearshore fish and crustacean communities
differed among hardened (bulkhead and riprap revetment)
and natural (wetland (native vegetation) and beach) shoreline
types. We also sought to answer two fundamental questions
about potential responses to shoreline type: (1) how do responses differ among species, and (2) how do responses differ
within comparatively narrow and wide definitions of the land/
water interface? To answer question (1), we examined biomass, size structure, and taxonomic distinctness of all species
and also examined biomass and size structure among three
functional groups common to most estuarine systems. We
elected to group species into functional groups such that
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
inference from our results could more easily transfer from
our study system (Chesapeake Bay) to other systems. To answer question (2), we examined responses within two distinct
ecotone widths to differentiate between waters immediately
adjacent to the land/water interface (within 3 m from shore)
and waters farther out from shore (within 16 m from shore).
This study considers nuances of faunal response to shoreline
hardening not previously addressed in most of the literature and
provides a number of considerations for coastal management.
Methods
Site Selection and Study Design
We examined patterns in fish and crustacean biomass density, taxonomic distinctness, and utilization of shoreline
type by size class in 16 mesohaline subestuaries (average
salinity of 4–14) of the middle and upper Chesapeake Bay
(Fig. 1). Here, we define Bsubestuaries^ as independent
estuaries, each with their own watershed, that feed into
and interact with the larger Chesapeake Bay estuarine system; each site cluster in Fig. 1 is from a unique subestuary.
Sampling occurred at two natural (wetland and beach) and
two hardened (bulkhead and riprap) shoreline types prevalent in estuarine systems. Shoreline types were not sampled in proportion to their prevalence in the subestuary but
rather in equal numbers within each subestuary to achieve
a balanced design. Sites were selected in a randomized
block design, in which sites were blocked by subestuary
of capture. Although subestuarine-scale variables are not
addressed in this study, subestuaries were deliberately chosen to include both highly developed (urban or agricultural
land) and predominantly forested watersheds as part of a
larger study focusing on shallow-water fish abundance patterns with land cover (Kornis et al. 2017). Therefore, the
subestuaries in this study are representative of a wide range
of conditions prevalent in the Chesapeake Bay and in other
coastal systems. All fishes (common and scientific names
in Online Resource B) and environmental data were collected between 31 July and 23 September in 2008 (Rhode
River only), 2010, 2011, and 2012. This time window was
chosen to avoid spring seasonal effects on fish and crustacean abundances, which are rather strong in Chesapeake
Bay due to spring migrations of ocean-spawning species
(e.g., blue crab, Atlantic croaker, spot). Although there
may be temporal effects on fish and crustacean abundances
within our sampling time window, these are accounted for
in our analysis by treating subestuary as a blocking variable, as all sites from within the same subestuary were
sampled within a few days of one another.
Shoreline-type definitions follow Davias et al. (2014).
Wetlands were stands of predominantly native emergent
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
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Fig. 1 Sample sites. Fishes were
sampled in 16 subestuaries of the
Chesapeake Bay at wetland,
beach, bulkhead, and riprap
shoreline types (n = 188 unique
sites) from 2008 to 2012
vegetation dense enough to form a platform that is inundated
at high tide. Beaches were fine or coarse sandy substrate with
an absence of large woody debris, fine organic material, or
dense vegetation. Bulkheads were vertical retaining walls
made of wood, vinyl, metal, or concrete. Ripraps (short for
Briprap revetments^) were shorelines armored primarily with
large rock (>0.25 m diameter). Both bulkheads and ripraps
had inundated vertical habitat at low tide, and bulkheads or
ripraps that fronted wetland shorelines were excluded from
sampling. Each site was located along a stretch of continuous
shoreline represented by a single shoreline type extending at
least 77 m; no mixed shorelines were sampled. When possible, clusters of four sites (one of each shoreline type) were colocated within the same arm of a subestuary so that dispersal
among sites within a cluster was possible for most species.
Fish Processing
Fish sampling occurred on the ebb tide within waters that
extended 3 m (constant) and 16 m (on average) from shore
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at each shoreline type. Both zones were sampled simultaneously at each of 72 sites (18 of each shoreline type). Areas
3 m from shore were sampled at an additional 72 sites (18 per
shoreline type) and areas 16 m from shore at an additional 44
sites (11 of each shoreline type). Areas 3 m from shore were
sampled using a Bnested parallel^ method (Kornis et al. 2017).
Fifteen minutes prior to pulling nested parallel seines, three
PVC poles were placed 3 m from shore to mark the sampling
area. To limit disturbing the shoreline community prior to
sampling, seines were floated to each site from a boat located
offshore. Two 15.2 m center-bagged beach seines were set in
place in tandem parallel to shore at 3 m distance. Once taut,
the ends of both nets were pulled into shore simultaneously.
The outer net was positioned to create a barrier, and the inner
net was then pulled. The second net was pulled immediately
after captured individuals were removed from the first net and
placed in a bucket with ambient water for processing. Areas
within 16 m of shore (16.1 ± 3.1 m (s.d.) on average, based on
maximum distance of seine net bag from shore) were sampled
by deploying a 61-m center-bagged beach seine by a specially
designed shallow-draft skiff, with the engine mounted just aft
of the bow, to maximize deployment speed (hereafter Bboatdeployed^ method). One person pulled one end of the big
seine to shore and anchored it while the boat drove in an arc
towards the second endpoint, rapidly enclosing the area to be
sampled in <60 s (Supplemental Video File, video credit to H.
Soulen). At sites that were simultaneously sampled at waters
extending both 3 and 16 m from shore, the boat-deployed
seine was set first to enclose the area. Then, after waiting
15 min to allow the site to recover from disturbance, the nested
parallel seines were set and pulled as described above prior to
pulling the boat-deployed seine. All seines were constructed
from Delta knotless material, 4.76-mm bar mesh. For both
nested parallel and boat-deployed methods, a PVC pole was
used to startle fish and crustaceans hiding along the shoreline
towards the net bag as the net ends were drawn together.
Captured individuals were identified, and a representative random sample of up to 50 individuals per species per seine net
were measured for total length to the nearest millimeter (all
individuals of a species were measured if <50 were present in
the net). White perch and striped bass, two species with relatively broad length ranges that often occurred at >50 per sample, were divided into two length categories (<150 mm,
≥150 mm) with up to 50 length measurements per category;
catch in excess of 50 was also counted separately for each
length category for these species. This was done to improve
the biomass estimates by ensuring that representative measurements were taken at random from both large and small
individuals of these species. Length categories were not needed for other species with broad length ranges (e.g., red drum,
American eel) because these species were always encountered
at ≤50 per sample and thus all individuals were measured for
length.
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Habitat Characteristics
Prior to sampling fauna, water temperature, salinity, and
dissolved oxygen were measured from a boat at each site
using a YSI 600 QS (2010–2012, n = 144 sites, 36 sites at
each shoreline type). Sediment cores were collected for
grain size analysis, and Secchi depth and volume of vegetative debris were also measured in 2011 and 2012. Six
sediment cores (31.75 mm diameter PVC cut to approximately 178 mm length) were collected at each site (n = 96
sites, 24 of each shoreline type). Cores were collected at
3 m, 1.5 m, and as close to shore as possible on either side
(laterally) of the area to be seined. All six cores were
pooled into the same bag and placed immediately on ice
and remained frozen until grain size analysis could be completed in the laboratory. Percent of sediment comprised of
gravel (particles >2.0 mm), sand (particles 0.25–2.0 mm),
and mud (particles 0.063–0.25 mm) was assessed at each
site (see Online Resource A for details). Secchi depth
(0.1 m increments) was measured along the shaded side
of the boat prior to faunal sampling by the same individual
at all sites (n = 94, 24 beaches and ripraps and 23 bulkheads and wetlands (failure to record Secchi depth at 1
bulkhead and 1 wetland site was an oversight)).
Vegetative debris was assessed from the nested parallel
beach seines only and included the following categories:
coarse woody debris, coarse leafy debris, filamentous algae, sea grass, Ulva spp., and terrestrial debris (n = 96
sites, 24 of each shoreline type). The total volume of vegetative debris collected by both 15.2 m seines was roughly
estimated by placing the debris into graduated cylinders for
small quantities, or a 5-gal bucket marked in 0.1 L increments for large quantities of debris. Habitat characteristics
were not measured in 2008 (44 sites from the Rhode
River).
Redundancy analysis (RDA) and single-factor blocked
ANOVAs were used to analyze relationships between shoreline type and habitat characteristics. Redundancy analysis
(Borcard et al. 1992) is a direct gradient technique that partitions variation in dependent variables (i.e., habitat characteristics) into components associated with the predictors (i.e.,
shoreline type). Although RDA is more commonly used to
understand patterns in community composition (e.g.,
Angermeier and Winston 1999; Sharma et al. 2011), it is useful in our application of helping to visualize how habitat characteristics vary with shoreline type and co-vary with one another. Habitat characteristic data were z-standardized for the
RDA such that all data were on the same unitless scale.
Average values for a given habitat characteristic across all
measurements were substituted in for missing data (e.g.,
Secchi depth, vegetative debris, and sediment grain size for
sites sampled in 2008 and 2010) such that the missing values
had no effect on the RDA outcome. Redundancy analysis does
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
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not test for statistical significance, and thus single-factor
ANOVAs, blocked by subestuary of capture, were used to
provide significance testing to determine whether each habitat
characteristic differed among shoreline types.
B (Table B1). Biomass density (g m−2) for each species at each
site was estimated by:
Biomass Estimation
where Wave is the average estimated weight (g) of measured
individuals captured at that site, N is the total number of individuals caught (both measured and unmeasured), and A is the
area sampled (m2). The catchability of seine nets (i.e., the % of
present individuals captured by sampling gear) differed
among species, net deployment methods, and among the four
shoreline types. We therefore corrected the total number of
individuals caught (N in above equation) based on catchability
estimates (Q values) from four-net Leslie depletions conducted at 36 sites (nine sites each of beach, bulkhead, riprap, and
wetland; Kornis et al. 2017). The percentage of present individuals likely to be captured by one seine pull (Q1) and by two
seine pulls (Q2) were used to correct boat-deployed and nested
parallel seine methods, respectively (see Kornis et al. 2017 for
details). This corrected for sampling bias in capture efficiency
specific to each shoreline type. The nested parallel method
(samples within 3 m from shore) was probably not as efficient
at sampling large-bodied fish as the boat-deployed method
(16 m from shore) due to greater possibility of escape along
the net edges. However, this difference between nested parallel and boat-deployed methods will not affect our comparisons
among shoreline types, which were performed separately for
each gear type.
Total lengths were measured on 10,865 individuals within 3 m
of shore and on 25,215 individuals within 16 m of shore, spanning a total of 49 taxa including 47 species and 2 genera.
Pumpkinseed (Lepomis gibbosus) and bluegill
(L. macrochirus), as well as Atlantic silverside (Menidia
menidia) and inland silverside (M. beryllina), were grouped to
genus due to difficulty distinguishing juveniles of these species
while processing large numbers of individuals rapidly in the
field. These 49 taxa were organized into functional groups based
on habitat preferences and foraging behaviors (Table 1), which
enabled examination of overall patterns across similar species.
Unmeasured individuals in seines with >50 individuals per species were assumed to be the average length of measured individuals from that site (see equation below). Due to time constraints, grass shrimp carapace lengths were only measured on a
subset of individuals (n = 533, collected on three dates during
summer 2012 from one site and ranging from 9 to 58 mm total
length), and the average length (21.2 ± 0.4 mm) was used for all
unmeasured grass shrimp (equivalent to 0.082 g per individual
based on length-weight relationship in Hartman and Brandt
1995). Weight (g) was estimated for each individual using
established length-weight relationships (carapace width instead
of length for blue crab, wingspan instead of length for Atlantic
stingray) (Online Resource B). Length-weight relationships
could not be found for 10 species, and substitutions were made
based on taxonomy or body shape as noted in Online Resource
Table 1 Functional groups of studied taxa (n = 49), based on known
habitat preferences and foraging behaviors (Murdy et al. 1997 for all
finfish). The Bother^ functional group included unique species that
Functional group
Description
B¼
W ave N
A
Utilization of Shoreline Types by Size Class
We used two-sided bootstrapped Kolmogorov-Smirnov (KS)
tests (n = 1000 Monte Carlo simulations for each test) to
generally had very small contributions to overall biomass. Species from
the Bother^ group were included in biomass estimates for Ball species^.
Scientific names are included in Online Resource B
Included species
Banded killifish, grass shrimp, green goby, mummichog,
Small-bodied bottom-oriented species that are frequently
naked goby, rainwater killifish, striped blenny striped
found in extremely shallow water (<20 cm) and forage
killifish, skilletfish, sheepshead minnow
on small littoral prey. Commonly serve as prey for larger species.
Planktivore
Species that forage on phytoplankton and zooplankton and
Alewife, Atlantic menhaden, Menidia spp., bay anchovy,
tend to prefer open water.
gizzard shad, spottail shiner, striped anchovy
Benthivore/piscivore Large-bodied bottom-oriented species that forage on a suite
American eel, Atlantic croaker, Atlantic ray, black drum,
of benthic invertebrates and small fishes.
blue crab, bluefish, brown bullhead, channel catfish,
chain pickerel, common carp, hogchoker, inshore
lizardfish, largemouth bass, Lepomis spp., red drum,
silver perch, spot, spotted seatrout, striped bass,
summer flounder, weakfish, white perch, yellow perch
Other
Unique species that do not readily fall into one of the other
Atlantic needlefish, harvestfish, lined seahorse, northern
three groups.
pipefish, northern puffer, northern searobin, striped
burrfish, striped mullet
Littoral-demersal
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determine whether there were significant differences in the
cumulative length distributions of fishes captured along the
four shoreline types (Abadie 2002; Sekhon 2011). KS tests
were conducted as pairwise comparisons between each unique
pair of shoreline types; all species were included in the tests
and separate tests were run within each distance from shore.
Only individuals with field-measured lengths were used in this
analysis (n = 10,865 within 3 m from shore and 25,215 within
16 m from shore). Unlike traditional KS analysis, the
bootstrapped KS test can accommodate non-continuous distributions and point masses (i.e., data with ties; Abadie 2002;
Sekhon 2011). This was important in our analysis because
each length value (to the nearest 1 mm) was associated with
many individual fish.
To illustrate how size classes of fish utilized different
shorelines, we grouped all sampled individuals into length
bins using total length and examined the total percent of catch
within each bin that occurred at the each of the four shoreline
types. This was done for all species in aggregate and for each
of the three specified functional groups (i.e., littoral-demersal,
planktivore, and benthivore-piscivore, Table 1). Most length
bins were 10-mm groups, but due to a skewed size distribution
within 16 m of shore, we included two 50 mm length bins for
larger individuals (301–350 and 351–400 mm). All bins had
≥20 individuals for small nets and ≥50 individuals for big nets.
long path lengths with the other 39 observed species (fish,
Phylum Chordata). Therefore, we estimated TD and VarTD
both including and excluding the two arthropod species.
Taxonomic Distinctness
Habitat Characteristics
We used taxonomic distinctness (TD) and the variation in
taxonomic distinctness (VarTD) to evaluate whether biodiversity in estuarine fauna varied along shoreline types.
Taxonomic distinctness, which measures taxonomic relatedness between species pairs in each sample as the mean path
length through the taxonomic tree (Clark and Warwick 1998,
2001), has several advantages over traditional diversity measures (e.g., species richness) including insensitivity to sampling effort (as shown for Chesapeake Bay fish assemblages
by Tuckey and Fabrizio 2013), ability to estimate phylogenetic diversity, and being more closely linked to functional diversity (Clarke and Warwick 1999; Leonard et al. 2006; Bilkovic
and Mitchell 2013). Variation in taxonomic distinctness,
which is not the variance of TD among samples but rather
the variation in branch lengths among all pairs of species in
each sample, assesses unevenness in the taxonomic hierarchy
(Clarke and Warwick 2001). Moreover, the combination of
average TD and VarTD can provide an indication of the relative degradation of assemblages from different locations
(Clarke and Warwick 2001). Generally, the range of average
TD values is narrow for a given assemblage (e.g., fishes) from
a particular study area (e.g., Ellingsen et al. 2005), and thus
seemingly small numeric differences may be meaningful. Two
species (blue crab and grass shrimp, Phylum Arthropoda) may
have had a large influence on TD and VarTD measures due to
Hardened shoreline types (i.e., bulkhead and riprap) had very
similar environmental profiles, while beach and wetland habitats
were more unique (Fig. 2). Water depth, Secchi depth, vegetative debris, and salinity differed significantly among shoreline
types (Table 2). Water depth at 0 and 3 m from shore were
positively correlated with bulkhead and riprap, while vegetative
debris and Secchi depth were positively and negatively correlated, respectively, with wetlands (Fig. 2). Dissolved oxygen, water
temperature, and sediment characteristics (% gravel, sand, and
mud) were not significantly related to shoreline type (Table 2),
although there were negative associations between percent mud
and beach, and percent gravel and wetland shoreline types
(Fig. 2). On average, water depth at shore was 0.51 m deeper
along hardened shorelines (0.59 ± 0.03 and 0.54 ± 0.04 m at
bulkheads and ripraps, respectively) than natural shorelines
(0.12 ± 0.02 and 0.0 ± 0.0 at wetlands and beaches, respectively). Wetlands were also significantly deeper than beaches at
shore. Water depth 3 m from shore was 0.26 m greater on average at hardened vs. natural shorelines, but water depth 16 m
from shore (average distance) did not differ among shoreline
types (Table 2). Secchi depth along wetland shorelines was significantly lower relative to the other three shoreline types (average difference of 0.11 m, p < 0.006 for all pairwise comparisons,
Table 2). Wetlands also had more vegetative debris than all other
habitats (Table 2, difference of 1.9 L on average, p = 0.05, 0.05,
Statistical Analyses
Taxonomic distinctness and taxonomic variation were calculated using Primer version 6.1.15. analyses of variance
(ANOVAs) were performed in R version 2.13.2, as were
bootstrapped KS tests (function ks.boot, ‘Matching’ package).
All ANOVAs included subestuary of capture as a blocking
factor to account for possible confounding factors associated
with subestuary, including date of capture, salinity, and
subestuary-level differences in prey abundance, pollution, or
habitat. For all statistical analyses, α = 0.05 was used as the
threshold for statistical significance. However, we also report
all p values between 0.05 and 0.10 because they may indicate
biologically relevant trends. Mean values are reported ±standard error, except when noted by [s.d.] (standard deviation).
Standard error was reported to assess confidence in the estimate of the mean, while standard deviation was reported to
describe the magnitude of variation around the mean.
Results
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
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Fig. 2 Redundancy analysis (RDA) axes one and two showing associations between habitat characteristics with shoreline type. Habitat
characteristics are shown in small black letters. Large, bold letters and
arrows correspond with the four shoreline types. The direction of each
arrow indicates how well each habitat characteristic correlates with
shoreline type: habitat characteristics along the positive direction of
each arrow (i.e., towards the point) are positively associated with that
shoreline type, whereas habitat characteristics in the opposite direction
of an arrow are negatively associated. Average water temperature is
abbreviated as BTemp.;^ average dissolved oxygen is abbreviated as
BDO;^ vegetative debris is abbreviated as BVeg.;^ and percent mud,
gravel, and sand are abbreviated as Bmud,^ Bgravel,^ and Bsand.^
Overlapping habitat characteristic codes at (−0.1, −0.1) are Temp. and
Depth_16m. Results of significance testing for the effect of shoreline type
on each habitat characteristic are available in Table 2
and 0.12 for comparisons with riprap, beach, and bulkhead,
respectively). Average salinity was significantly higher at beach
(11.2 ± 0.4) compared to wetland (10.8 ± 0.04) shorelines; however, this difference is not likely to be physiologically relevant to
the fauna examined in this study.
77.7% of all biomass captured within 3 m of shore and was
largest along bulkhead compared to all other shoreline types
(p < 0.0001 for pairwise comparisons with wetland and beach;
p = 0.003 for comparison with riprap). Several taxa, including
blue crab, Lepomis spp., spot, striped bass, and white perch
contributed to larger benthivore-piscivore biomass at bulkheads.
Planktivore biomass was also largest along altered shorelines
(riprap > wetland (p = 0.01) and beach (p = 0.04);
bulkhead > wetland (p = 0.02) and beach (p = 0.07)) within
3 m of shore, largely driven by a single taxa, Menidia spp. In
contrast to other functional groups, littoral-demersal biomass
was largest along wetlands compared to all other shoreline
types (Fig. 3 top panel, p = 0.002, 0.002, and 0.10 in pairwise
comparisons with beach, bulkhead, and riprap, respectively).
Mummichog, grass shrimp, and striped killifish contributed
the most to patterns in littoral-demersal species (Table 3).
Total biomass density was comparable among all four
shoreline types in samples extending 16 m, on average, from
shore (Table 4; F3,77 = 0.3, p = 0.86; Fig. 3 bottom panel); this
was in contrast to patterns within 3 m of shore. The difference
Biomass Density
Effects of shoreline type on biomass density (g m−2) varied
among functional species groups and between waters that extended 3 and 16 m from shore. Shoreline type had a significant
effect on total biomass (i.e., all species in aggregate), benthivorepiscivore biomass, planktivore biomass, and littoral-demersal
biomass within 3 m of shore (Table 3, all p ≤ 0.001). Total
biomass density was largest along bulkheads compared to all
other shoreline types (Fig. 3 top panel, p < 0.0001 for pairwise
comparisons with wetland and beach; p = 0.009 for comparison
with riprap). Total biomass density was also larger along riprap
vs. beach shorelines (p = 0.06). This pattern was driven
largely by benthivore-piscivore biomass, which accounted for
between the 16 m and extreme nearshore patterns was due to
larger amounts of planktivore and benthivore-piscivore biomass at wetland and beach shorelines within 16 m of shore
compared to within 3 m of shore (Fig. 2). Average planktivore
biomass density increased from 1.1 to 37.4 g m−2 along
beaches and from 0.7 to 26.0 g m−2 along wetlands (3 m vs.
16 m from shore for all comparisons). Similarly, average
benthivore-piscivore biomass density increased from 0.3 to
15.9 g m−2 along beaches and from 5.0 to 21.2 g m−2 along
wetlands. Nevertheless, benthivore-piscivore biomass density
was still larger along hardened shoreline types (bulkhead > wetland and beach (p < 0.0001 for both); riprap > wetland (p = 0.08)
and beach (p = 0.02)). Similarly, littoral-demersal biomass density remained largest along wetlands within 16 m of shore compared to all other shoreline types (p < 0.0001 for all pairwise
comparisons). Planktivore biomass density did not vary significantly among shorelines type within 16 m of shore (Table 4).
The average proportion of fish community biomass represented by different functional groups within 3 m of shore was
very different between natural and hardened shoreline types
(Fig. 4). Littoral-demersal species dominated along wetland
and beach shorelines (58.4 and 54.3%, respectively), while
benthivore-piscivore species dominated along bulkhead and
riprap shorelines (79.3 and 72.1%, respectively). In contrast,
benthivore-piscivores had the greatest fraction of fish community biomass at all four shoreline types within 16 m of shore
(Fig. 4, 50.3% at beaches, 53.3% at wetlands, 73.1% at ripraps, and 85.4% at bulkheads). Notably, planktivores contributed more to fish community biomass at beaches than at other
shoreline types within both 3 and 16 m from shore.
Utilization of Shoreline Types by Size Class
Sig. significantly, BC beach, WT wetland, BH bulkhead, RR riprap
Significant effects (single-factor ANOVAs blocked by subestuary of capture) are bolded, and shoreline types that are significantly different from one another at α = 0.05 are listed (Tukey-Kramer post-hoc
pairwise comparison procedures). Values for each shoreline type are mean ± S.E. Dissolved oxygen, salinity, and temperature were measured in 2010–2012 and are average of surface and bottom values.
Water depth was also measured in 2010–2012. Depth measurements at 0 and 3 m from shore were collected during nested parallel sampling and are precise distances. Depth measurements taken at the bag
of a 61-m net deployed by boat were collected at an average distance of 16.0 m from shore
0.67 ± 0.03
1.05 ± 0.19
0.42 ± 0.02
0.99 ± 0.31
Depth 3 m offshore (m)
Depth 16 m offshore (m)
0.46 ± 0.02
1.07 ± 0.13
0.72 ± 0.03
1.05 ± 0.24
F3,122 = 41.2, P < 0.0001
F3,52 = 0.2, P = 0.89
F15,122 = 1.7, P = 0.06
F15,52 = 3.0, P = 0.002
BH & RR > WT & BC;
WT > BC
BH & RR > WT & BC
All others > WT
WT > RR & BC
F15,114 = 3.5, P < 0.0001
F15,125 = 148.5, P < 0.0001
F15,125 = 6.4, P < 0.0001
F10,80 = 37.5, P < 0.0001
F10,82 = 2.5, P = 0.01
F10,82 = 2.6, P = 0.009
F10,82 = 1.4, P = 0.21
F10,82 = 0.8, P = 0.66
F15,124 = 2.1, P = 0.02
7.1 ± 0.3
11.1 ± 0.4
27.9 ± 0.3
0.58 ± 0.04
0.3 ± 0.2
9.6 ± 2.6
81.3 ± 3.2
9.1 ± 2.5
0.54 ± 0.04
7.3 ± 0.3
11.2 ± 0.4
28.0 ± 0.6
0.59 ± 0.06
0.3 ± 0.1
15.0 ± 4.2
81.5 ± 4.4
3.5 ± 1.2
0.0 ± 0.0
Dissolved oxygen (μg L−1)
Salinity
Temperature (°C)
Secchi depth (m)
Vegetative debris (L)
% gravel (>2.0 mm)
% sand (0.25–2.0 mm)
% mud (0.063–0.25 mm)
Depth at shore (m)
6.8 ± 0.2
10.8 ± 0.4
27.9 ± 0.3
0.47 ± 0.04
2.3 ± 1.1
7.5 ± 2.4
81.8 ± 3.9
10.7 ± 3.7
0.12 ± 0.02
7.0 ± 0.3
10.9 ± 0.4
28.1 ± 0.3
0.57 ± 0.05
0.6 ± 0.3
14.8 ± 3.6
77.1 ± 4.8
8.1 ± 2.8
0.59 ± 0.03
F3,114 = 0.7, P = 0.55
F3,125 = 3.1, P = 0.03
F3,125 = 0.1, P = 0.98
F3,80 = 7.1, P < 0.0001
F3,82 = 3.2, P = 0.03
F3,82 = 1.6, P = 0.21
F3,82 = 0.3, P = 0.83
F3,82 = 1.3, P = 0.29
F3,124 = 125.8, P < 0.0001
Blocking factor
Shoreline type effect
Riprap
Bulkhead
Wetland
Beach
Response
Environmental profile of beach, wetland, bulkhead, and riprap shorelines
Table 2
BC > WT
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
Sig. different pairs
S166
Cumulative length distributions for all species pooled (Fig. 5)
were unique for each of the four shoreline types, both within
3 m of shore (all p < 0.0001; Fig. 5 upper panel) and within
16 m of shore (all p < 0.004; Fig. 5 lower panel). However,
cumulative length distributions were more similar within hardened and natural shoreline types than between them, based on
the Kolmogorov-Smirnov test statistic, D, which is the maximum vertical deviation between two cumulative length distribution curves. Within 3 m from shore, bulkhead vs. riprap
(D = 0.12) and wetland vs. beach (D = 0.17) were more similar
than comparisons between hardened and natural shoreline
types (D values ranged from 0.41 to 0.46 for the comparisons
bulkhead vs. beach, bulkhead vs. wetland, riprap vs. beach, and
riprap vs. wetland). Similarly, bulkhead vs. riprap (D = 0.05)
and wetland vs. beach (D = 0.03) were more similar within
16 m of shore than comparisons between hardened and natural
shoreline type (D values ranged from 0.20 and 0.21 for the
comparisons bulkhead vs. beach, bulkhead vs. wetland, riprap
vs. beach, and riprap vs. wetland). D values were also greater
for comparisons within 3 m of shore than within 16 m of shore,
Relationships between shoreline type and biomass density (g m−2) in waters within 3 m of shore for commonly occurring species and functional species groups (described in Table 1)
Fn. group or species
Beach
Wetland
Bulkhead
Riprap
Shoreline type effect
Blocking factor
Sig. different pairs
Menidia spp.
1.1 ± 0.3
0.0a ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.6 ± 0.2
0.0a ± 0.0
0.0 ± 0.0
0.1 ± 0.1
2.3 ± 0.5
0.6 ± 0.5
0.1 ± 0.0
0.4 ± 0.2
2.6 ± 0.7
0.0a ± 0.0
0.0 ± 0.0
1.0 ± 0.6
F3,124 = 5.2, P = 0.002
F3,124 = 1.2, P = 0.33
F3,124 = 3.2, P = 0.03
F3,124 = 1.8, P = 0.15
F15,124 = 2.1, P = 0.01
F15,124 = 1.1, P = 0.33
F15,124 = 1.0, P = 0.42
F15,124 = 0.7, P = 0.77
RR > WT & BC; BH > WT
All planktivores
1.1 ± 0.3
0.7 ± 0.2
3.4 ± 0.7
3.6 ± 1.1
Grass shrimp
Mummichog
Striped killifish
All littoral-demersals
Blue crab
Lepomis spp.
Spot
Striped bass
White perch
All ben.-pisc.
All species
0.0 ± 0.0
0.6 ± 0.3
1.0 ± 0.2
1.6 ± 0.4
0.2 ± 0.1
0.0 ± 0.0
0.0 ± 0.0
0.0a ± 0.0
0.0 ± 0.0
0.3 ± 0.1
3.0 ± 0.6
0.4 ± 0.1
5.9 ± 0.9
2.0 ± 0.7
10.8 ± 2.3
2.7 ± 0.4
0.6 ± 0.4
0.1 ± 0.1
0.0 0.0
1.4 ± 0.6
5.0 ± 0.9
16.5 ± 2.3
0.0 ± 0.0
0.8 ± 0.4
0.5 ± 0.2
1.5 ± 0.5
10.0 ± 1.4
10.0 ± 4.8
1.7 ± 0.6
1.2 0.4
43.1 ± 12.1
66.9 ± 16.8
71.8 ± 16.6
0.0 ± 0.0
3.6 ± 2.4
1.1 ± 0.5
5.1 ± 2.5
6.8 ± 1.1
3.4 ± 1.4
0.6 ± 0.2
0.4 0.2
13.1 ± 5.5
24.8 ± 6.1
33.5 ± 6.6
F3,124 = 5.4, P = 0.001
F3,124 = 32.3, P < 0.0001
F3,124 = 3.6, P = 0.02
F3,124 = 2.1, P = 0.11
F3,124 = 6.2, P = 0.0006
F3,124 = 21.3, P < 0.0001
F3,124 = 3.9, P = 0.01
F3,124 = 6.1, P = 0.0006
F3,124 = 3.2, P = 0.02
F3,124 = 2.8, P = 0.04
F3,124 = 13.3, P < 0.0001
F3,124 = 12.4, P < 0.0001
F15,124 = 1.2, P = 0.29
F15,124 = 0.8, P = 0.69
F15,124 = 0.5, P = 0.92
F15,124 = 1.0, P = 0.42
F15,124 = 0.8, P = 0.69
F15,124 = 1.1, P = 0.40
F15,124 = 2.8, P = 0.001
F15,124 = 1.6, P = 0.09
F15,124 = 1.0, P = 0.49
F15,124 = 5.0, P < 0.0001
F15,124 = 2.6, P = 0.002
F15,124 = 2.4, P = 0.005
RR > WT & BC; BH > WT
WT > all others
WT > BH & BC
Atlantic menhaden
Bay anchovy
Gizzard shad
BH > WT
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
Table 3
WT > BH & BC
BH & RR > WT & BC
BH > WT & BC
BH > WT & BC
BH > BC
BH > all others
BH > all others
Significant effects (single-factor ANOVAs blocked by subestuary of capture) are bolded, and shoreline types that are significantly different from one another at α = 0.05 are listed (Tukey-Kramer post-hoc
pairwise comparison procedures). Values for each shoreline type are mean ± S.E
Fn. functional, Sig. significantly, BC beach, WT wetland, BH bulkhead, RR riprap
a
Denote true values of 0.0; other 0.0 values reflect rounding (e.g., average biomass density <0.05 g m−2 )
S167
S168
Fig. 3 Average biomass density
(g m-2) of all species and three
functional groups along natural
(beach, wetland) and hardened
(bulkhead, riprap) shoreline
types. Error bars are ±S.E.
Biomasses were estimated from
length data using established
length-weight relationships
(Online Resource B). Areas 3 m
from shore were sampled by
stretching two 15.2 m beach
seines parallel to shore at 3 m
distance, then pulling the
endpoints of both nets into shore
simultaneously. Areas 16 m from
shore were sampled a 61-m seine
deployed by boat; one person
pulled one end of the big seine to
shore and anchored it while the
boat drove in an arc towards the
second endpoint. At sites that
were simultaneously sampled at
both 3 and 16 m from shore, the
61-m seine was deployed first to
enclose the area, and individuals
captured within 3 m of shore were
included in the data collected
within 16 m from shore
suggesting increased similarity in cumulative length distributions at the greater distance from shore.
Shoreline type utilization was clearly related to body size
within 3 m of shore, with percent of catch at hardened shorelines increasing with body size (Fig. 6). Approximately 90% of
all individuals ≤40 mm were captured at either wetland or
beach shorelines, while approximately 90% of all individuals
≥115 mm were captured at either bulkhead or riprap shorelines.
This pattern held true for all functional species groups. For
example, even though the average biomass densities of
benthivore-piscivores and planktivores were significantly
greater at hardened shorelines within 3 m of shore (Fig. 3,
Table 3), approximately 60% of benthivore-piscivores (Fig. 6
lower left panel) and planktivores (Fig. 6 upper right panel)
≤50 mm were captured along natural shorelines. Most small
benthivore-piscivores were captured along wetlands, while
most small planktivores were captured at beaches. Similarly,
approximately 65% of littoral-demersal individuals >100 mm
were captured at hardened shorelines, especially riprap (Fig. 6
lower right panel), despite significantly greater biomass density
of littoral-demersals at wetland habitats (Fig. 3, Table 3). In
fact, the percent of littoral-demersals captured at riprap shorelines started to increase for individuals >70 mm.
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
3 m From Shore
16 m From Shore
Patterns between body size and shoreline type within 16 m
of shore were similar to the patterns observed within 3 m of
shore in that smaller individuals were most abundant at natural
shorelines and larger individuals at hardened shorelines, but
differences between shoreline types were much less distinct.
Approximately 65% of all individuals ≤40 mm were captured
at either wetland or beach shorelines, while approximately
50–70% of individuals ≥100 mm were captured along either
bulkhead or riprap shorelines (Fig. 7 upper left panel);
benthivore-piscivore species followed a similar pattern
(Fig. 7 lower left panel). Planktivore patterns were highly
variable, and two planktivore size classes (90–140 and 180240 mm) were strongly associated with natural shorelines.
Post-hoc analyses suggested these size classes were predominantly Atlantic menhaden (approximately 75 and 65% for the
smaller and larger size ranges, respectively). Littoral-demersal
species of all sizes were overwhelmingly captured along natural shorelines within 16 m of shore (Fig. 6 lower right panel).
Taxonomic Distinctness—All Species
Shoreline type had a significant effect on taxonomic distinctness (TD) both within 3 m of shore (F3,124 = 5.5, p = 0.001)
Relationships between shoreline type and biomass density (g m−2) in waters within 16 m of shore for commonly occurring species and functional species groups (described in Table 1)
Fn. group or species
Beach
Wetland
Bulkhead
Riprap
Shoreline type effect
Blocking factor
F3,77 = 2.0, P = 0.13
F3,77 = 0.7, P = 0.53
F3,77 = 1.2, P = 0.32
F3,77 = 1.5, P = 0.23
F3,77 = 0.8, P = 0.50
F15,77 = 1.5, P = 0.13
F15,77 = 0.6, P = 0.86
F15,77 = 1.3, P = 0.21
F15,77 = 3.7, P < 0.0001
F15,77 = 0.6, P = 0.83
F3,77 = 12.0, P < 0.0001
F3,77 = 13.9, P < 0.0001
F3,77 = 7.6, P = 0.0001
F3,77 = 20.5, P < 0.0001
F3,77 = 8.2, P < 0.0001
F3,77 = 4.3, P = 0.007
F3,77 = 8.5, P < 0.0001
F3,77 = 1.9, P = 0.14
F3,77 = 5.3, P = 0.002
F3,77 = 11.7, P < 0.0001
F3,77 = 0.3, P = 0.86
F15,77 = 1.4, P = 0.15
F15,77 = 0.5, P = 0.95
F15,77 = 2.0, P = 0.03
F15,77 = 1.6, P = 0.09
F15,77 = 2.5, P = 0.005
F15,77 = 6.3, P < 0.0001
F15,77 = 3.4, P = 0.0002
F15,77 = 1.9, P = 0.03
F15,77 = 2.8, P = 0.002
F15,77 = 3.5, P = 0.0001
F15,77 = 0.8, P = 0.65
Menidia spp.
2.3 ± 0.5
1.1 ± 0.4
1.3 ± 0.3
1.5 ± 0.4
Atlantic menhaden
Bay anchovy
Gizzard shad
All planktivores
31.9 ± 21.3
1.0 ± 0.6
1.9 ± 0.8
37.4 ± 21.3
22.0 ± 15.9
0.5 ± 0.2
2.3 ± 0.8
26.0 ± 15.8
1.7 ± 0.9
0.4 ± 0.1
2.7 ± 0.7
6.1 ± 1.1
23.1 ± 11.1
0.2 ± 0.1
4.1 ± 1.3
28.9 ± 11.2
Grass shrimp
0.0 ± 0.0
0.2 ± 0.1
0.0 ± 0.0
0.0 ± 0.0
Mummichog
Striped killifish
All littoral-demersals
Blue crab
Lepomis spp.
Spot
Striped bass
White perch
All ben.-pisc.
All species
0.1 ± 0.0
1.3 ± 0.3
1.4 ± 0.3
1.5 ± 0.2
0.4 ± 0.3
1.3 ± 0.4
0.8 ± 0.4
11.3 ± 5.5
15.9 ± 6.1
54.8 ± 22.7
2.9 ± 0.7
2.7 ± 0.7
6.0 ± 1.1
2.8 ± 0.5
0.6 ± 0.4
3.0 ± 0.9
0.4 ± 0.2
12.0 ± 4.0
21.2 ± 4.2
53.3 ± 18.4
0.1 ± 0.0
0.3 ± 0.1
0.4 ± 0.2
5.9 ± 1.0
3.9 ± 1.9
7.8 ± 1.9
2.3 ± 0.9
37.1 ± 9.0
60.0 ± 10.5
66.5 ± 10.6
0.1 ± 0.1
0.6 ± 0.3
0.8 ± 0.4
6.0 ± 1.3
1.5 ± 0.8
7.7 ± 1.6
4.4 ± 2.7
18.1 ± 3.9
41.2 ± 5.6
71.0 ± 13.4
Sig. different pairs
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
Table 4
WT > all others
WT > all others
WT > BH & RR
WT > all others
BH & RR > WT & BC
BH > WT & BC
BH & RR > WT & BC
BH > all others
BH > WT & BC; RR > BC
Significant effects (single-factor ANOVAs blocked by subestuary of capture) are bolded, and shoreline types that are significantly different from one another at α = 0.05 are listed (Tukey-Kramer post-hoc
pairwise comparison procedures). Values for each shoreline type are mean ± S.E
Fn. functional, Sig. significantly, BC beach, WT wetland, BH bulkhead, RR riprap
S169
S170
100
Percent of Total Fish Biomass
Fig. 4 The average percentage of
total fish community biomass
represented by different
functional groups within 3 m of
shore and 16 m of shore. Areas
3 m from shore were sampled by
stretching two 15.2 m beach
seines parallel to shore at 3 m
distance, then pulling the
endpoints of both nets into shore
simultaneously. Areas 16 m from
shore were sampled a 61-m seine
deployed by boat
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
80
60
40
20
nd
Be
ac
h
R
ip
Bu rap
lk
he
ad
et
la
W
W
et
la
nd
Be
ac
h
R
ip
Bu rap
lk
he
ad
0
Benthivore-Piscivore
Littoral-Demersal
Planktivore
and within 16 m of shore (F3,77 = 4.5, p = 0.006) when all
species were included. Interestingly, TD patterns were opposite in waters extending 3m and 16 m from shore: beaches had
the lowest TD within 3 m of shore, but the highest TD (barely)
within 16 m of shore. Within 3 m of shore, there was significantly less TD along beach shorelines (66.5 ± 4.0) compared
to wetland (76.2 ± 0.8, p = 0.006), riprap (76.9 ± 1.0,
p = 0.003), and bulkhead (74.9 ± 1.1, p = 0.02) shorelines
(Fig. 8, upper left panel). TD along wetland, riprap, and bulkhead shoreline types was comparable (all p > 0.89). Within
16 m of shore, TD was significantly lower along riprap revetments (69.4 ± 0.05) than along wetlands (71.3 ± 0.5, p = 0.04)
and beaches (71.7 ± 0.06, p = 0.01) (Fig. 8). Average TD was
also higher along beaches and wetlands than bulkheads
(70.0 ± 0.7), but these difference were not statistically significant (p = 0.10 and 0.27 for comparisons between bulkhead
vs. beach and wetland, respectively). Shoreline type also had a
significant effect on taxonomic variation (VarTD) within 3 m
of shore (F3,124 = 5.0, p = 0.003) but not within 16 m of shore
(F3,77 = 0.68, p = 0.57). This pattern was driven by greater
VarTD along wetland shorelines (552.7 ± 28.3) compared to
beach (377.5 ± 47.8, p = 0.001), bulkhead (445.8 ± 28.6,
p = 0.09), and riprap (449.7 ± 26.4, p = 0.11) shorelines.
Taxonomic Distinctness—Finfish only
Two crustacean species (blue crab and grass shrimp) accounted
for much, but not all, of differences in TD and VarTD among
shoreline types. The general relationship between TD and shoreline type was the same when these two crustaceans were excluded (i.e., only finfish considered in the analysis; Fig. 8), but statistical support was not as strong for fish assemblages within 3 m
of shore (F3,124 = 2.4, p = 0.07) or 16 m of shore (F3,77 = 2.64,
p = 0.056). Pairwise comparisons within 3 m of shore were
similar, with less TD along beach shorelines (51.1 ± 3.7) than
riprap (58.7 ± 1.8, p = 0.09) wetland (58.1 ± 1.1, p = 0.14) and
bulkhead (58.0 ± 1.9, p = 0.15) shorelines. Within 16 m of shore,
finfish TD was generally comparable among shoreline types,
but tended to be greater along beach (62.2 ± 0.3) and wetland
(61.8 ± 0.2) shorelines than bulkhead (61.1 ± 0.4) or riprap
(61.3 ± 0.3) shorelines; only the comparison between beach
and bulkhead had a p < 0.1 (p = 0.06). VarTD at wetlands within
3 m from shore changed substantially with the exclusion of
crustaceans. Nonetheless, VarTD of finfish tended to differ
among shoreline types within 3 m of shore (F3,124 = 2.3,
p = 0.08) but not within 16 m of shore (F 3,77 = 0.46,
p = 0.71). Finfish VarTD was higher along natural shorelines
(wetland =224.5 ± 23.6; beach =211.8 ± 34.4) than hardened
shorelines (bulkhead =167.3 ± 19.8; riprap =145.5 ± 23.8), but
no pairwise comparison was significant (lowest p was 0.11 for
the comparison between wetland and riprap).
Discussion
Our results indicate that the response of estuarine fish and
crustacean biomass and size structure to shoreline hardening
varies substantially among functional groups, and also by the
width of the land/water ecotone considered. The likelihood of
species-group-specific responses to shoreline hardening has
been hypothesized by others (e.g., Munsch et al. 2016;
Gittman et al. 2016a) but has largely been overlooked in empirical analyses—in part because information on the ecological effects of shoreline hardening has been sparse until very
recently (Dugan et al. 2011). As a result, earlier works on fish
and crustaceans emphasized broad-brush effects on total
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
S171
Fig. 5 Cumulative length
distributions along wetland,
beach, riprap, and bulkhead
shoreline types. All species and
functional groups are included
abundance and diversity (Gittman et al. 2016a) and effects on
small-bodied and juvenile fish most likely to be affected by
disruption to natural shorelines (e.g., Peterson et al. 2000;
Rozas et al. 2007). These works contributed substantially to
our understanding of how shoreline hardening affects estuarine fauna, but our study offers an improved understanding of
the nuances of shoreline type effects, with important applications for management.
The biomass response of littoral-demersal species to shoreline hardening that we found was consistent with prior studies,
but the high amounts of benthivore-piscivore and planktivore
biomass at bulkheads relative to other shoreline types
contrasted with general findings on total species abundance.
A recent meta-analysis of 54 published studies found a substantial effect of bulkheads/seawalls on flora, benthic infauna,
birds, epibiota, and nekton, noting 23% lower biodiversity and
S172
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
100%
All
From
Shore
All Individuals,
Individuals, 3 3mm
From
Shore
90%
90%
80%
80%
Total Percent of Catch
Total Percent of Catch
100%
70%
60%
50%
40%
30%
60%
50%
40%
30%
20%
10%
10%
0%
0%
40
90%
60
80
<41
100 120 140 160 180 200 220
Length Category (mm)
50
100%
Benthivore-Piscivores,
m From Shore
Small
Nets (Benthivore3Piscivores)
60
70
80
Length Category (mm)
90
>90
LiƩoral-Demersals, 3 m From Shore
90%
80%
80%
70%
Total Percent of Catch
Total Percent of Catch
70%
20%
100%
PlankƟvores,
m From Shore
Small Nets3(PlankƟvores)
60%
50%
40%
30%
20%
10%
70%
60%
50%
40%
30%
20%
<41
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
>220
0%
Length Category (mm)
Beach
10%
0%
<41
Wetland
50
Riprap
60
70
80
90
Length Category (mm)
100
>100
Bulkhead
Fig. 6 Utilization of waters within 3 m of natural (wetland, beach) and
hardened (bulkhead, riprap) shoreline types by fishes of different size
classes, measured by percent of catch. The border line of the gray and
green areas reflects the total percent of catch from natural shoreline types.
All sampled individuals are grouped into 10 mm length bins, and each bin
is represented along the x-axes by the upper limit (e.g., B60^ refers to
individuals that are 51-60 mm). The lowest size bin in all panels contains
individuals that are 0–40 mm. The upper size bin varied by species group
in order to maintain at least 20 individuals in each length bin, and thus the
x-axes are not consistent among panels. The upper size bin in the panels
for all individuals and for benthivore-piscivores contains individuals
>220 mm, compared to >90 mm for planktivores and >100 mm for
littoral-demersals. As a result, patterns for all individuals are driven predominantly by benthivore-piscivores at lengths >100 mm
45% lower organism abundance relative to natural shorelines
(Gittman et al. 2016a). Although these general patterns are
important, both biomass (this study) and abundance
(Munsch et al. 2015; Kornis et al. 2017) data indicate that
the response to shoreline hardening varies substantially
among species. Earlier studies have also documented this variability in the context of resident vs. transient species. Lowe
and Peterson (2014), for example, found that small-bodied
resident species are more negatively impacted by wetland
fragmentation and loss to hardened structures than transient
species that can easily move between locations (Lowe and
Peterson 2014). This finding is consistent with our observations of strongly reduced biomass of small-bodied littoral-demersal species, which include several wetland residents (e.g.,
mummichog, striped killifish, grass shrimp), but increased
biomass of more transient species comprising the
benthivore-piscivore group (e.g., spot, striped bass, blue crab)
along bulkheads and riprap revetments.
Although functional-group-specific responses to shoreline
type were important, patterns in size-class-specific utilization
of shoreline type superseded functional group-specific biomass
responses—particularly within the extreme nearshore zone
within 3 m of the shoreline. Cumulative length distributions
were significantly different among all shoreline types.
However, the magnitude of difference was much greater between hardened and natural shoreline types than between
shoreline types within the hardened (i.e., bulkhead vs. riprap)
or natural (i.e., wetland vs. beach) shoreline groups. Large
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
100%
80%
80%
40%
Length Bin (mm)
100%
90%
80%
80%
Total Percent of Catch
90%
70%
60%
50%
40%
30%
400
300
280
260
240
220
200
180
160
0%
140
>350
30%
0%
120
300
40%
10%
100
280
50%
20%
80
260
60%
10%
60
LiƩoral-Demersals, 16 m from Shore
70%
20%
Length Category (mm)
Length Category (mm)
Beach
240
Length Bin (mm)
Big Nets (Benthivore
Benthivore-Piscivores,
16 mPiscivores)
From Shore
40
Total Percent of Catch
100%
220
40
400
300
280
260
240
220
200
180
160
0%
140
0%
120
10%
80
10%
100
20%
60
20%
200
30%
180
30%
50%
160
40%
140
50%
60%
120
60%
70%
80
70%
PlankƟvores, 16 m From Shore
100
Total Percent of Catch
90%
60
Nets (All16Fish)
AllBig
Individuals,
m From Shore
90%
40
Total Percent of Catch
100%
S173
Wetland
Riprap
Bulkhead
Fig. 7 Utilization of waters within 16 m of shore (on average) of natural
(wetland, beach) and hardened (bulkhead, riprap) shoreline types by
fishes of different size classes, measured by percent of catch. The
border line of the gray and green areas reflects the total percent of
catch from natural shoreline types. Sampled individuals are grouped
into 10 mm length bins, except for two 50 mm length bins (301–350
and 351–400 mm) for larger sizes. Each bin is represented along the xaxes by the upper limit (e.g., B60^ refers to individuals that are 5160 mm). The lowest size bin in all panels contains individuals that are
0–40 mm. The upper size bin varied by species group in order to maintain
at least 50 individuals in each length bin, and thus the x-axes are not
consistent among panels. The upper size bin in the panels for all
individuals and for benthivore-piscivores contains individuals
>400 mm, compared to >350 mm for planktivores and >150 mm for
littoral-demersals. As a result, patterns for all individuals are driven predominantly by benthivore-piscivores and planktivores at lengths
>150 mm
sample sizes may also have contributed to statistically significant differences among similar cumulative length distributions
for wetland vs. beach and bulkhead vs. riprap. Differences
between hardened vs. natural shoreline types, rather than within
each type, are therefore likely to have the greatest biological
importance and are the focus of our discussion.
Small individuals of every functional group were more
abundant along natural shoreline types, supporting findings
of a plethora of studies documenting the importance of natural
shorelines, especially wetlands, as nursery habitats (e.g., Beck
et al. 2001; Minello et al. 2003; Rozas et al. 2007; Sheaves
et al. 2015). Even benthivore-piscivores, whose biomass was
largely associated with bulkhead and riprap shorelines, were
most often found along natural shorelines at small sizes, consistent with earlier studies (e.g., Hodson et al. 1981; Ross
2003; Nemerson and Able 2003, 2004; Able et al. 2012) including one showing that fish transitioned from extreme shallows to deeper waters as they grew (Munsch et al. 2016).
Similarly, larger-bodied individuals of all functional groups
were more likely to be encountered at hardened shoreline
types. Although this pattern was driven by benthivorepiscivores and planktivores for individuals >150 mm in size,
even littoral-demersal species show this pattern for larger individuals within that species group (90–150 mm) that
S174
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
Fig. 8 Average taxonomic distinctness (upper panels) and taxonomic variation (lower panels) along four commonly occurring shoreline types. Closed
black cricles include all taxa (both finfish and crustaceans), while open white circles include only finfish species. Error bars are ±S.E.
predominantly associated with riprap habitat. These findings
suggest the heavy utilization of hardened shorelines by some
functional groups may be at least partially dependent upon the
existence of nursery and refuge habitat provided by natural
shorelines, a hypothesis consistent with many earlier works
showing the integration of nursery production to broader systems (e.g., Childers et al. 2000; Weinstein et al. 2005; Sheaves
et al. 2015). Moreover, a study from Puget Sound noted similar effects of shoreline hardening on fish size distributions
(Munsch et al. 2016), suggesting shoreline hardening effects
on fish size distribution—including predation risk as perceived by fish (see below)—may be generalizable among
systems.
The fact that size-class-specific shoreline utilization patterns were much stronger within 3 m of shore compared to
16 m of shore suggests a mechanism of shoreline hardening
largely specific to waters immediately at the land/water interface. We argue that this mechanism is most likely increased
water depth and the elimination of shallow-water habitat at
hardened shorelines. Average depth at shore for both bulkhead
and riprap habitats was over 0.5 m, and vertical bulkhead and
riprap habitats were inundated even at low tide. The exclusion
of shallow-water habitat, including horizontal intertidal areas
in marine systems, substantially reduces the value of nearshore habitat as refuge from predators. Prior studies have noted the importance of shallow-water habitat for predation refuge, and by extension, the increased predation risk associated
with deeper waters (Ruiz et al. 1993; Hines and Ruiz 1995;
Patterson and Whitfield 2000). Water depth/intertidal habitat
also likely explained the strong association of littoraldemersal biomass with natural shorelines. Small resident fishes have repeatedly been shown to prefer shallow water, and
large predatory species to prefer deep water (Akin et al. 2003;
Ng et al. 2007), although Becker and Suthers (2014) found
predatory species tend to increase their presence in shallow
habitats (≈1 m deep) at night.
Size-class utilization patterns may also be driven by environmental characteristics or food availability. We found that
wetland shorelines had reduced water clarity and greater
amounts of vegetative debris, both of which enhance the value
of wetland habitats as refuge for small-bodied individuals,
especially from visual predators. In addition, Bilkovic and
Roggero (2008) found greater amounts of subtidal benthic
structure (e.g., shellfish beds, woody debris) along wetland
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
shorelines. Natural shorelines with shallow waters also have
ample sources of food for small-bodied or juvenile fish (Seitz
et al. 2006; Sobocinski et al. 2010). Both gut contents and
stable isotope signatures, which can reflect feeding in wetland
areas even at small spatial scales (Davias et al. 2014), suggest
juvenile benthivore-piscivores from a variety of species including Scianeids and Moronids feed in wetland areas (Able
et al. 2001; Nemerson and Able 2003, 2004; Weinstein et al.
2010). For example, spot undergo an ontogenetic diet shift
from planktivores as postlarvae to benthic feeders as juveniles,
and during this critical transition spot typically feed in shallow
intertidal wetland habitats (Hodson et al. 1981), where both
zooplankton (David et al. 2016) and benthic invertebrates
(King et al. 2005; Seitz et al. 2006; Bilkovic et al. 2006) are
plentiful.
Our observation of high biomass of benthivore-piscivores
at bulkheads and riprap revetments has implications for the
effect of fish on benthos, and vice versa, along hardened
shorelines. High biomass of benthivore-piscivores could exert
substantial predation pressure on benthic prey. Indeed, decreased or altered prey resources have been reported along
hardened shorelines (King et al. 2005; Seitz et al. 2006;
Bilkovic et al. 2006; Heerhartz et al. 2015); some species
(e.g., juvenile Pacific salmon) have been shown to feed at high
rates along both hardened and natural shoreline types
(Heerhartz and Toft 2015); and slight negative relationships
between predator abundance and benthic infaunal biomass
and diversity have been shown in nearshore areas (within
4 m of shore) by at least one study (Lawless and Seitz
2014). Conversely, hardened shorelines could alter prey resources independent of predation pressure, as reported by
Lawless and Seitz (2014) for benthic infaunal density. Small
fish may also avoid deeper waters created by shoreline
armoring (Munsch et al. 2016), given the value of shallow
waters as refuge habitat (Ruiz et al. 1993; Hines and Ruiz
1995). Alterations to prey resources could in turn negatively
affect fish condition along hardened shorelines. For example,
empty stomachs and different prey items found in gulf killifish
and spot from hardened wetlands in coastal Mississippi (i.e.,
wetlands that were fragmented and dominated by hardened
surfaces) contributed to lower body condition of those species
(Lowe and Peterson 2015). We do not have direct evidence of
either mechanism in our study, but based on our biomass
findings, we speculate that shoreline hardening will likely
affect predator–prey dynamics in nearshore waters.
Differences in biomass responses at extreme nearshore interface areas and broader nearshore zones (defined in our study
as waters extending 3 and 16 m from shore, respectively) suggest that biomass patterns may be driven by small-scale distributional shifts related to shoreline characteristics within 3 m
from shore. For example, total biomass across all groups was
substantially greater at bulkheads than all other types within
3 m from shore, but did not differ among shoreline types within
S175
16 m from shore. Similarly, overall size-specific shoreline utilization was more similar among shoreline types within 16 m
from shore than within 3 m from shore, although littoraldemersal species were an exception. We speculate that this
difference is driven by greater depth-at-shore at bulkheads,
which facilitated access of large-bodied, biomass-rich individuals to waters within 3 m from shore. Similarly, the high biomass of planktivores at riprap and bulkhead shorelines relative
to wetland and beach shorelines within 3 m of shore may reflect
deeper water and similarity to open-water habitat. Within 16 m
of shore, bulkheads held the lowest biomass of planktivores
relative to other shoreline types (6.1 vs. ≥26.0 g m−2). Taken
together, this suggests that planktivores at bulkheads were congregated within 3 m of shore. Predation risk in the 3 to 16 m
zone may have been higher than in waters extending only 3 m
from shore, as the benthivore-piscivore assemblage included
larger individuals within 16 m from shore than within 3 m from
shore. In addition, planktivorous fish biomass may have been
higher along wetland and beach shoreline types within 16 m
from shore than in the narrower 3 m ecotone because natural
shorelines preserve intertidal areas and planktivores have been
shown to feed preferentially on intertidal zooplankton assemblages (David et al. 2016). Some planktivores—specifically,
Menidia spp. in our study—also rely on wetland shorelines
for spawning and nursery habitat (Balouskus & Targett
2012). Finally, littoral-demersal species had high biomass
along riprap shorelines relative to wetlands within 3 m from
shore (about 47% of the mean biomass observed at wetlands)
but not within 16 m from shore (about 13%). This suggests the
larger littoral-demersal individuals that utilize riprap habitat do
not often venture outside of the relative safety of the riprap
itself, while the shallow, turbid, debris-rich waters along wetland shorelines support littoral-demersal species at much greater distances from shore.
Although our study focused on four distinct shoreline
types, a number of prior studies have examined changes in
fish and invertebrate communities along gradients of partially
armored wetlands or beaches (e.g., Bilkovic and Mitchell
2013; Lowe and Peterson 2014; Heerhartz et al. 2015). For
example, Lowe and Peterson (2014) described decreasing
abundances of three key fish species along a gradient of natural, partially urbanized (i.e., fragmented and hardened), and
completely urbanized wetlands, with stronger effects of urbanization on resident species compared to transient species.
Several studies have shown living shorelines, which combine
wetland plants with a hardened structure, to be more similar to
native wetlands than to riprap revetments; living shorelines
are therefore considered less harmful than conventional coastal protection methods like bulkhead and riprap and as a result
are becoming more widespread (Davis et al. 2008; Currin
et al. 2010; Balouskus and Targett 2016; Bilkovic et al.
2016; Gittman et al. 2016b). Greater nekton diversity has also
been detected at living shorelines relative to native wetlands,
S176
likely due to the diversity of shoreline habitat (Partyka and
Peterson 2008; Peters et al. 2015). Our results suggest living
shorelines may have different effects on different functional
groups or size classes, meriting additional research.
Taxonomic distinctness and taxonomic variation of assemblages that included both fish and crustaceans indicated natural shoreline types had more diverse assemblages, especially
within 3 m from shore, although relationships between taxonomic distinctness, variation and shoreline type were weak
overall. Taxonomic distinctness patterns across shoreline type
were similar for all species and finfish-only analyses at 3 m
from shore, where low taxonomic distinctness at beaches may
have reflected a lack of water depth relative to bulkhead and
riprap (this study), and less subtidal structure relative to wetlands (Bilkovic and Roggero 2008). Taxonomic distinctness
was greater at natural shorelines than hardened shorelines
within 16 m of shore for both analyses, but the magnitude of
the difference was greater for all species (1.7 units greater on
average) than for finfish only (0.8 units greater). This difference is likely driven by the exclusion of grass shrimp, a
littoral-demersal species that strongly associates with natural
shorelines, from the finfish only analysis. Taxonomic variation was higher for all species along wetland shorelines within
3 m from shore, likely due to the influence of grass shrimp.
Importantly, taxonomic variation was still higher along natural
shorelines than hardened shorelines within 3 m from shore
even for the finfish-only analysis. Average-to-high values of
taxonomic diversity combined with high values of taxonomic
variation, as observed for natural shorelines within 3 m from
shore for all species and finfish, typically indicate more pristine locations (Clarke and Warwick et al. 2001). Furthermore,
taxonomic variation measures complexity and low values, as
observed for hardened shoreline types in our study within 3 m
from shore, may indicate environmental disturbance (Yang
et al. 2016). The differences in taxonomic distinctness and
variation within 3 m from shore contrast their relative consistency within 16 m from shore, and underscore the other strong
effects of hardened shorelines on fauna documented by our
study within extreme nearshore interface zones.
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
applicable to other systems. Based on our findings, it appears
that one of the largest effects of shoreline hardening is the
alteration of shallow waters at the interface between land
and water. These extreme nearshore interface areas, represented in this study by waters within 3 m from shore, tend to be
deeper at hardened shorelines than at natural shoreline.
Differences in water depth, environmental characteristics,
and food availability between natural and hardened shorelines
likely contributed to our observed functional group and sizeclass-specific patterns, and should be considered when making management decisions.
Although this study focused on local-scale effects, systemscale effects may also be important. One major finding of this
study is that natural shorelines serve as critically important
habitat for resident littoral-demersal species and for juveniles
of species from other functional groups. As such, natural
shorelines, especially wetlands, are substantially integrated
components of larger systems (e.g., Childers et al. 2000;
Weinstein et al. 2005), as nursery ground production often
moves across habitats and ecosystems (Kneib 1997; Sheaves
et al. 2015). Furthermore, whole-system production in coastal
estuaries is strongly tied to overall production—including resident species—in intertidal wetlands (Teal 1962; Kneib 1997;
Weinstein et al. 2014), and many marine transient species
benefit from wetland shorelines and their production without
directly occupying those habitats (Litvin and Weinstein 2003;
Weinstein et al. 2005). Therefore, shoreline hardening that
comes at the expense of wetland habitat likely reduces estuarine production. Given this, it is not surprising that hardened
shorelines and wetland loss appear to have cumulative effects
on abundance of a variety of fish and crustacean taxa in estuaries (Peterson and Lowe 2009; Dethier et al. 2016; Kornis
et al. 2017). Fortunately, high ecosystem productivity, such as
when the majority of a subestuary is wetland habitat, can
overcome small-scale negative effects of shoreline hardening
on infauna, and thus may be important to resilience against
shoreline alteration (Lawless and Seitz 2014). We join others
(e.g., Weinsten and Litvin 2016) in suggesting a whole-system
approach to shoreline management to ensure increased coastal
protection needs are met in ways that best maintain healthy
estuaries and the ecosystem services they provide.
Conclusion—Management Considerations
Management applications from our results include considering species-specific or functional-group-specific responses
and placing the effects of shoreline hardening into a wholesystem context. We provide clear evidence that shoreline types
can affect different functional groups in different ways.
Although our study focused on the Chesapeake Bay, responses of specific species were generally similar to the response of their respective functional group (Tables 3 and 4).
Since the functional groups represented in our study are prevalent in estuaries worldwide, our results may be broadly
Acknowledgements We thank H. Soulen, K. Heggie, K. Evans, C.
Hause, C. Kliewer, M. Odabaş-Geldiay, D. Shikashio, J. Wilhelm, and
many others for help with field collections. We also thank the NOAA
Chesapeake Bay Office (Annapolis, MD) for leveraged, in-kind support
for field collections, specifically the use of a specially designed skiff and
associated equipment, supplies, and personnel. Scientific collection permits were obtained from the Maryland and Virginia Departments of
Natural Resources prior to sampling, and animal handling conformed to
the Smithsonian Environmental Research Center’s animal care protocols.
Mention of specific product or trade names does not constitute endorsement by the U.S. Government. This is publication #17-012 of the NOAA/
CSCOR Mid-Atlantic Shorelines project, grant number
NA09NOS4780214.
Estuaries and Coasts (2018) 41 (Suppl 1):S159–S179
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creativecommons.
org/licences/by/4.0/), which permits use, duplication, adaptation, distribution
and re[production in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to Creative
Commons license and indicate if changes were made.
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