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Development of a Multimetric Index for Integrated Assessment of Salt Marsh Ecosystem Condition
Jessica L. Nagel1, Hilary A. Neckles2*, Glenn R. Guntenspergen1, Erika N. Rocks3, Donald R. Schoolmaster Jr.4, James B. Grace4, Dennis Skidds5 and Sara Stevens5
1U.S. Geological Survey, Patuxent Wildlife Research Center, 12100 Beech Forest Rd, Laurel, MD 20708 USA
2U.S. Geological Survey, Patuxent Wildlife Research Center, 196 Whitten Road, Augusta, ME 04330 USA
3National Park Service, Northeast Coastal and Barrier Network, 54 Elm Street, Woodstock, VT 05091 USA
4U.S. Geological Survey, Wetland and Aquatic Research Center, 700 Cajundome Blvd., Lafayette, LA 70506 USA
5National Park Service, Northeast Coastal and Barrier Network, University of Rhode Island Coastal Institute in Kingston, 1 Greenhouse Rd., Kingston, RI 02881 USA
*corresponding author: e-mail: [email protected]; tel: (207) 626-6619; fax: (207) 622-8204
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Abstract
Tools for assessing and communicating salt marsh condition are essential to guide decisions
aimed at maintaining or restoring ecosystem integrity and services. Multimetric indices (MMIs)
are increasingly used to provide integrated assessments of ecosystem condition. We employed a
theory-based approach that considers the multivariate relationship of metrics with human
disturbance to construct a salt marsh MMI for five National Parks in the northeastern US. We
quantified the degree of human disturbance for each marsh using the first principal component
score from a principal components analysis of physical, chemical, and land-use stressors. We
then applied a metric-selection algorithm to different combinations of about 45 vegetation and
nekton metrics (e.g. species abundance, species richness, and ecological and functional
classifications) derived from multi-year monitoring data. While MMIs derived from nekton or
vegetation metrics alone were strongly correlated with human disturbance (r-values from -0.80 to
-0.93), an MMI derived from both vegetation and nekton metrics yielded an exceptionally strong
correlation with disturbance (r = -0.96). Individual MMIs included from one to five metrics. The
metric-assembly algorithm yielded parsimonious MMIs that exhibit the greatest possible
correlations with disturbance in a way that is objective, efficient, and reproducible.
Keywords: Multimetric index; Salt marsh; Ecosystem assessment; Ecological indicators;
Coastal wetlands
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Introduction
Coastal wetlands occur at the interface between uplands and oceans within a narrow
range of elevation influenced by tidal characteristics. Salt marshes occupy as much as 45,000
km2 world-wide (Greenberg et al. 2006). These coastal wetlands provide essential livelihood
services to millions of people, as well as critical regulating services such as maintenance of water
quality, protection from storms and erosion, and carbon sequestration valued at US $10,000 per
hectare per year (Barbier et al. 2011; Hopkinson et al. 2012; Möller et al. 2014). However, as
much as half of global coastal wetlands have been lost due to human conversion for agriculture
and other anthropogenic changes in land use (Pendleton et al. 2012), and climate change,
declining water quality, and changes in sediment delivery rates associated with human activity
continue to affect the world’s remaining wetlands (Kirwan and Megonigal 2013).
In the US, direct human modification of coastal wetlands has led to extensive wetland
degradation (Kennish 2001; Bertness et al. 2002) through activities such as ditching and drainage
of the marsh platform; reduction of tidal exchange by dikes, roads, and water control structures;
disposal of dredge spoil; introduction of invasive species; and discharge of nitrogen, phosphorus,
oil and other contaminants (Roman et al. 2000). The impacts of such disturbances on salt marsh
integrity and their ecosystem services are far reaching (Daiber 1986; Wolfe 1996; Adamowicz
and Roman 2005). Salt marsh management and conservation decisions are increasingly focused
on maintaining and improving ecological integrity (MEA 2005). As a consequence, approaches
to monitor and assess ecosystem condition have gained widespread attention (e.g. Simenstad and
Thom 1996; Short et al. 2000; Wigand et al. 2001; Neckles et al. 2002, 2015).
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Considerable research has been devoted to development of both quantitative and
functional approaches to assess ecosystem integrity and track ecosystem condition over time. In
recent decades, multimetric indices (MMIs) have been used to integrate numerous characteristics
of complex ecosystems into one-dimensional indices of ecosystem condition (Karr 1999; Hering
et al. 2006). By combining multiple indicators of human disturbance into a composite score, an
MMI may be more sensitive to disturbance than any of its individual component metrics (Lee et
al. 2004; Schoolmaster et al. 2012). MMIs for wetland ecosystem assessment have been based on
specific biota, such as fish (Simon 1998), zooplankton (Lougheed and Chow-Frazer 2002), and
plants (Lopez and Fennessy 2002; Mack 2004), and they have also been based on combinations
of taxa (plants, invertebrates, and fish; Wilcox et al. 2002; James-Pirri et al. 2014) or ecosystem
components (plants, various fauna, and physical environmental features; Wigand et al. 2011;
Langman et al. 2012; Miller et al. 2016). As development of wetland MMIs has proliferated,
they have been applied to increasingly larger scales. MMIs have been developed worldwide for
regional assessments of inland (e.g., Ortega et al. 2004; Boix et al. 2005; Miller et al. 2006;
Mereta et al. 2013) and coastal wetlands (e.g. Wigand et al. 2011; Staszak and Armitage 2013;
Nestlerode et al. 2014) and are integral to reporting results of the National Wetland Condition
Assessment (NWCA) in the conterminous US (USEPA 2016).
Traditionally, MMI development has involved exploratory analysis of ecosystem
attributes or metrics measured at sites representing a range of conditions, from a reference
standard to sites that are subject to high levels of anthropogenic disturbance (Karr 1999; Karr
and Chu 2000). Expert knowledge or various ordination techniques may be used to reduce the
number of candidate metrics, organize them into classes, and rank their importance in explaining
the variation among sites, and metrics then may be selected for inclusion in the MMI based on
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the strength of bivariate relationships between disturbance and individual responses. However,
interactions among ecosystem stressors, environmental covariates, and ecological responses may
introduce unexplained variability into metric data sets, reducing the sensitivity of individual
metrics to perceived disturbance factors and obscuring determination of the “best” metrics to
include in the MMI (Schoolmaster et al. 2012; 2013). In such cases, Schoolmaster et al. (2012)
demonstrated that evaluating how metrics respond to disturbance as a group can improve the
potential to generate a maximally sensitive MMI. This may be particularly relevant for salt
marsh ecosystems, where relationships among agents of change, physical and chemical
environmental stressors, and ecological responses are inherently complex.
We applied a multivariate, algorithmic approach for MMI development to salt marsh
ecosystems in the northeastern US. As described originally by Schoolmaster et al. (2012; 2013),
this is an empirical method of MMI construction from a set of candidate metrics, in which the
unique contribution of each metric toward explaining the variation in human disturbance is
evaluated within the context of all candidates. Through an objective, transparent, and
reproducible process for metric selection, an MMI is produced that is statistically robust and
sensitive given the set of available data. This method of MMI construction was tested previously
on freshwater wetlands (Schoolmaster et al. 2013). We applied the algorithmic approach to salt
marshes within US National Park Service units distributed between Massachusetts and
Maryland. Our study shows the broad utility of this approach for developing robust assessment
tools for salt marsh ecosystems.
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Methods
Salt Marsh Monitoring Data
Salt marsh monitoring data were collected from 33 marsh study units (MSUs) in five
National Parks within the NPS Northeast Coastal and Barrier Network (NCBN) along the
northeastern coast of the US (Figure 1, Table 1). MSUs were established at Cape Cod National
Seashore (NS), Sagamore Hill National Historic Site (NHS), Fire Island NS, Gateway National
Recreation Area (NRA), and Assateague Island NS. Individual MSUs were delineated to
encompass the contiguous marsh area bounded by geographic features, such as tidal creeks, salt
marsh-upland borders, grid ditches, shoreline, and other distinguishable features, using GIS. For
all parks except Cape Cod NS, all areas of salt marsh within the park were identified, all
potential MSUs were delineated, and the final sample of MSUs at each park was then randomly
selected from this population. At Cape Cod NS, we used existing MSUs that had been selected
as representative of the park’s salt marsh resources. MSU size at most parks ranged from 5-15
ha, but at Cape Cod NS the MSUs were generally larger (up to 168 ha).
MMI development was based on marsh vegetation and nekton (i.e., fish and free-
swimming crustaceans) monitoring data collected from 2008 – 2013 as part of the network-wide
vital signs monitoring program (Stevens et al. 2005). Monitoring occurred biennially in most
MSUs, yielding three years of data during this time period; however, for some MSUs only two
years of data were available (Table 1). Vegetation data were collected in all 33 MSUs and nekton
data were collected at only 24 of the MSUs (Table 1). To allow comparisons of MMIs developed
from different sized datasets (24 vs 33 MSUs) and metric types (vegetation only vs vegetation +
nekton), we generated four separate datasets: vegetation data alone from 33 MSUs (V33);
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vegetation and nekton data combined from 24 MSUs (VN24); vegetation data alone from 24
MSUs (V24); and nekton data alone from 24 MSUs (N24). In addition, salt marsh monitoring
data were collected irregularly between 2003 and 2007 in five of the MSUs located in three parks
as part of a pilot program (Cape Cod NS, Gateway NRA, and Sagamore Hill NHS, Table 1). We
applied the MMIs to the data collected during the pilot program to evaluate changes in salt marsh
condition as revealed by different MMIs.
The percent cover of marsh vegetation and other cover types (e.g., water, bare ground,
wrack, and litter) was measured once during the peak of the growing season (early July through
early September) per sampling year. From 2008 – 2013, cover was measured using the Braun-
Blanquet method (Kent and Coker 1994) within approximately 50 randomized square-meter (1m
x 1m) quadrats along 10 randomized transects per MSU. Percent cover of each species was
estimated visually within cover classes (absent; <1 %; 1-5 %; 6-25 %; 26-50 %; 51-75 %; 76-
100 %), and we converted cover classes to class midpoints for analytical purposes. Before 2008,
cover was measured using either the Braun-Blanquet method or the point-intercept method, in
which cover was recorded as the proportion of points intercepted by each species in a grid of 50
or 100 points per square-meter quadrat (Roman et al. 2001). In addition to percent cover of
vegetation, the height of the introduced common reed, Phragmites australis, was measured when
present, as the distance from the marsh surface to the tallest point of the plant. If there were 20 or
fewer stems within a quadrat, all stems were measured; if there were more than 20 stems, all
stems within a randomly selected quarter of the quadrat were measured.
Nekton populations were monitored in shallow pools, creeks, and ditches within each
MSU twice during the growing season: once in early summer (mid-June through July) and once
in late summer-early fall (August through September). During each sampling event, between 5
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and 20 stations were sampled per MSU, depending on the availability of pool, creek, or ditch
habitat. Wherever possible, a mix of habitats (e.g., pools and creeks) was sampled. Sampling
stations were established randomly around the perimeter of pools or along the length of creeks
and ditches. Shallow pools and creeks greater than 1 m wide were sampled using throw traps,
and ditches were sampled using ditch nets (James-Pirri et al. 2012). Each individual captured
was counted and identified to species and up to 15 individuals of each species were measured for
total length (fish and shrimp) or carapace width (crabs) at each station. Species counts were
converted to density (number m-2) by dividing by sampled area.
Candidate Response Metrics
We derived a wide variety of vegetation and nekton response metrics for potential
inclusion in each MMI. Our initial list included 50 vegetation metrics and 28 nekton metrics at
species and community levels that were suggested by other assessments of salt marsh ecosystem
condition (Konisky et al. 2006; Moore et al. 2010; Wigand et al. 2011; Neckles et al. 2013).
Species-based vegetation metrics were derived from estimates of percent cover or frequency of
seven species or species groups (Spartina alterniflora, Spartina patens, Distichlis spicata, Iva
frutescens, Phragmites australis, Salicornia spp., Scirpus and Schoenoplectus spp.) that are
broadly representative of different salt marsh environments. Species-based nekton metrics were
calculated using estimates of relative abundance (percent of total density) of five common native
species or species groups (Fundulus spp., Cyprinodon variegatus, Palaemonetes spp., Anguilla
rostrata, Menidia spp.) and one invasive species (Carcinus maenas). Species-level metrics also
included the height of the invasive plant Phragmites australis as an indicator of plant vigor (cf.
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Howard and Mendelssohn 1999) and length of the common fish species Fundulus heteroclitus as
an indicator of life-history stage (Raposa et al. 2003).
Vegetation and nekton community metrics described the abundance and proportional
distribution of species within key ecological and taxonomic categories. Each plant species was
classified according to salt marsh zone (low marsh; high marsh; pannes, pools, and creeks; salt
marsh border; brackish border; or upland), salinity tolerance (high, medium, or low), and native
status (native or introduced) using information available from various sources (Duncan 1974;
Eleuterius 1990; Schafale and Weakley 1990; Haines and Vining 1998; Magee and Ahles 1999;
Silberhorn 1999; Shumway and Banks 2001; Swain and Kearsley 2001; Edinger et al. 2002;
Harrison 2004; Konisky et al. 2006; Tiner 2009; Sperduto 2011; Fleming and Patterson 2013;
NYNHP 2013; USDA 2014; Supplemental Table 1). Percent cover of vegetation community
classes in each quadrat was calculated by summing over the percent cover estimates of all
species within each community. Similarly, nekton species were classified according to broad
taxonomic group (e.g., fish, shrimp, crab, etc.), migrational status (resident or transient), and
native status (native or introduced) using various sources (NYNHP 2013; Chesapeake Bay
Program 2014; FishBase 2014; IRL Species Inventory 2014; Supplemental Table 2), and density
and relative abundance of nekton communities in each sample were derived by summing over all
species within each community. Total and relative (percent of total species) species richness
metrics for each vegetation and nekton community category were calculated at both the sample
and the MSU level. An additional nekton community metric, species dominance, was derived by
calculating the minimum number of species forming greater than 90% of abundance in the
sample (Hughes et al. 2002).
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Temporal means of most species- and community-based metrics derived from the 2008-
2013 monitoring data were derived by averaging the annual MSU means. Frequency metrics
(proportion of occurrences relative to total sample number) were calculated both as the average
of annual means and as the total frequency of occurrence across all samples and years (2008-
2013). Within each dataset, metrics that contained a large proportion of zero values (> 66 % of
the total values within a metric) were omitted from further analysis (Stoddard et al. 2008;
Schoolmaster et al. 2013). In addition, metrics that duplicated another metric were removed, as
they would have contributed essentially identical information to the MMI. We identified
duplicate metrics by high values of inter-metric correlation (r > 0.95 or r < -0.95; Schoolmaster
et al. 2013). For each duplicate pair we retained the metric that was most interpretable in the
context of salt marsh ecosystem condition. Following these metric-reduction steps, the V33 data
set contained 27 vegetation metrics (Table 2), the V24 data set contained 28 vegetation metrics
(Table 2), the N24 data set contained 18 nekton metrics (Table 3), and the VN24 data set
contained 27 vegetation (Table 2) and 18 nekton metrics (Table 3).
Human Disturbance Index
We derived a Human Disturbance Index (HDI) to use for selecting response metrics for
inclusion in the MMI. We used existing aerial imagery and the 2006 National Land Cover
Database (NLCD; Fry et al. 2011) to quantify human impacts to each MSU in terms of physical,
chemical, and land-use stressors (Table 4). Categorical landscape metrics and classes were
adopted from the New England Rapid Assessment Method for assessing salt marsh condition
(Carullo et al. 2007) and aerial photographs of marshes representing each category were used as
a guide in classification. The metrics were scored by a trained technician using the most recently
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available orthophotography in ArcMapTM 10.2 in consultation with park staff familiar with the
MSUs. Continuous metrics quantifying the amount of developed land cover within 150-m and 1-
km buffers of each MSU were calculated from NLCD data using ArcGISTM 10.2. The percent of
developed land area in each buffer zone was derived by aggregating developed land cover
classes in the NLCD (Developed Open Space, Developed Low Intensity, Developed Medium
Intensity, Developed High Intensity, and Barren Land) into a single category. Values were then
relativized to the size of the MSU by multiplying by (buffer area / MSU area) (Table 4). Only
one MSU (CACO_10) had measurable agricultural land in the buffer zone (relativized percent of
agricultural land in the 150-m buffer = 0.8 %), thus this land cover was not included in the HDI
formulations.
Categorical and continuous disturbance metrics were converted to an ordinal scale
following scoring criteria outlined in the New England Rapid Assessment Method (Carullo et al.
2007). Categorical metrics were assigned a rank score in which 0 represented undisturbed
conditions and 6 represented the most disturbed condition. Continuous variables were converted
to ranked values using three break-points in the distribution: values ≤ (mean - ½SD) were
assigned 0; values > (mean - ½SD) and ≤ mean were assigned 2; values > mean and ≤ (mean +
½SD) were assigned 4; and values > (mean + ½SD) were assigned 6.
A Principal Components Analysis (PCA) of the seven ranked disturbance metrics was
used to derive two HDIs: HDI 24 corresponded to the metric datasets using 24 MSUs (VN24,
V24, N24) and HDI 33 corresponded to the dataset using 33 MSUs (V33). A Spearman rank
correlation matrix of the disturbance metrics was used as input for the PCA and we evaluated
the Principal Components (PCs) by examining the eigenvectors, scree test, variance explained,
and comprehensibility (Kachigan 1982). The first Principal Component score (PC-1) for each
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site was used as the basis of the HDI score (Falcone et al. 2010) as PC-1 accounted for the
largest part of the total variance inherent in the data. The PC-1 scores were transformed to HDI
by first reordering if necessary so that more positive values represented higher human
disturbance, and then rescaling to the HDI range of 0 (minimal human disturbance) to 100
(highest human disturbance). This type of disturbance index has been used successfully for
watershed and wetland assessment in the Great Lakes Basin (Brazner et al. 2007; Danz et al.
2007), in New England (Wigand et al. 2011), and throughout the western US (Falcone et al.
2010).
MMI Construction
We applied the MMI construction algorithm to the four sets of candidate metrics derived
from the 2008-2013 salt marsh monitoring data (V33, V24, VN24, N24; Table 2; Table 3).
Because the metrics have different units, all metrics were rescaled to unitless measures with
similar ranges using a continuous scoring method (Blocksom 2003):
m scaled=m-LU-L
where m = metric; L = 2.5 percentile values of m; U = 97.5 percentile values of m. Values of m
that were < L or > U were set to L or U, respectively. Metrics that were positively correlated
with HDI were reflected about their midpoints to ensure that the MMI would be negatively
correlated with disturbance. MMI assembly then followed a stepwise metric selection process to
maximize the likelihood of predicting the observed HDIs. Details of the MMI assembly
algorithm are provided by Schoolmaster et al. (2012; 2013). In general, each candidate metric
was used as a starting point, m1. The initial ml was added site-by-site to each of the remaining
metrics, mj, to find the combination m1 + mj yielding the strongest negative correlation with HDI.
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This metric combination was then added to each of the remaining metrics, mj, to find the new
combination + mj yielding the strongest negative correlation with HDI. This metric selection
process was repeated until the addition of another mj failed to improve the ability to predict HDI
(likelihood ratio test, p > 0.05). This entire assembly process was repeated using each metric as
the starting point m1, thereby yielding as many candidate MMIs as there were candidate metrics.
Each candidate MMIj thus represented a model, HDI = β0 + β1 MMIj , with k parameters
(effectively, the number of metrics in MMIj + 2). The Akaike Information Criterion (AIC) of
each model was then calculated as the basis for comparing MMIs and selecting those with the
highest capacity to predict disturbance. For each candidate MMI, the difference between its AIC
and that of the MMI with the lowest AIC was determined (i.e., ΔAIC). All MMIs with ΔAIC < 2
were retained as having substantial support. If more than one MMI resulted for any dataset, a
single, model-averaged MMI was derived from the sum of the matrix products of the candidate
MMI and the Akaike weights for each candidate MMI (Schoolmaster et al. 2013). MMI scores
for each MSU were calculated by averaging the rescaled metric values for that MSU. The MMI
construction algorithm was written and run in the R statistical computing environment (R Core
Team 2016).
MMI Application
To explore how MMI formulation influenced perceived trends in salt marsh condition
over time, we applied the MMIs derived from the VN24 and V24 datasets to data collected
within five of the MSUs during a pilot study between 2003 and 2007 (Table 1). Values for
metrics included in each MMI were calculated for each year that data were available for a given
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MSU. Metric values were then rescaled and combined as described above to generate MMI
scores for each year of pilot data.
Results
HDI Scores
The PCAs of the ranked disturbance metrics revealed a substantial drop in eigenvalue
magnitude, or the amount of variation explained by each PC, between the first and second PC for
each dataset (Table 5); PC-1 explained from 38 % (HDI 33) to 41 % (HDI 24) of the total
variation, and PC-2 explained 21 % (HDI 33) or 20 % (HDI 24). The metrics with the highest
loadings relative to the first PC were Fill / Fragmentation, Tidal Flushing and Tidal Restriction
(Table 5).
The HDI scores for MSUs were similar between the 24- and 33-MSU datasets (Figure 2).
There was substantial variation in HDI among MSUs. Approximately two thirds of the MSUs
exhibited relatively low HDI scores (i.e., HDI< 30 on a scale of 0 to 100). The MSUs with the
lowest HDI scores occurred on the barrier islands of Fire Island NS and Assateague Island NS.
Sites with highest HDI scores were located at Cape Cod NS (CACO_1, CACO_2, CACO_3,
CACO_6), the Long Island coast of Fire Island NS (FIIS_1, FIIS _2, FIIS _3) and Gateway NRA
(GSH_1, GSH_3).
MMI Development
Application of the MMI assembly algorithm to the vegetation-only metric sets yielded
individual MMIs consisting of three to five metrics. We refer to MMIs based on the metric set of
origin (e.g., MMI V33 was based on the V33 dataset). Single-letter subscripts refer to candidate
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MMIs with ΔAIC < 2, and the subscript wt refers to the model-weighted MMI. All of the
vegetation-only MMIs were strongly correlated with HDI (r = -0.89 to -0.93; Table 6). A single
MMI (MMI V33) was constructed based on 33 MSUs, and two candidate MMIs with ΔAIC < 2
(MMI V24a, MMI V24b) were constructed based on 24 MSUs. Averaging the two MMI V24s
using model weights slightly improved the correlation with HDI (MMI V24wt; Table 6). There
was strong similarity among the metric sets included in the vegetation-only MMIs, with four
metrics identical between MMI V33 and MMI V24wt (Table 6). Metrics associated with the
prevalence of Phragmites australis and upland border communities were positively correlated
with HDI, and metrics describing the frequency of high-marsh species and the proportion of
species with high salinity tolerance were negatively correlated with HDI.
When nekton metrics alone were used as the basis for MMI construction, the assembly
algorithm identified five candidate MMIs composed of seven unique metrics, with from one to
three metrics each (MMI N24a-e; Table 7). Correlation of the nekton-only MMIs with HDI
ranged from -0.80 to -0.84. The model-weighted MMI composed of all seven metrics had a
correlation of -0.85 with HDI (MMI N24wt, Table 7). Metrics describing the relative abundance
of resident shrimp and the number of introduced species were most positively correlated with
HDI, and metrics associated with the relative abundance and number of species of resident fish
were most negatively correlated with HDI.
When both vegetation and nekton metrics were included in the MMI construction, a
single MMI (MMI VN24) resulted that was highly correlated with HDI (r = -0.96). MMI VN24
consisted of four metrics (two vegetation, two nekton), three of which had been selected during
the vegetation- and nekton-only constructions. The individual metrics were correlated with HDI
as follows: percent cover of native vegetation species, r = -0.44; percent cover of brackish border
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vegetation species, r = 0.64; relative abundance of resident shrimp, r = 0.49; relative abundance
of resident fish, r = -0.79. Including both metric types in MMI construction resulted in the
strongest correlation with HDI in comparison to either vegetation or nekton metrics alone
(Figure 3).
In many cases, the relative differences in MMI scores across the MSUs were similar
between the individual MMI formulations (Figure 4). For example, all of the MSUs at
Assateague Island National Seashore and several at Fire Island National Seashore (FIIS_5, 6, 7,
9) generally had the highest MMI scores regardless of MMI formulation. However, some distinct
differences in the pattern of MMI scores across MSUs did emerge among the MMIs. In
particular, CACO_2 had among the lowest MMI scores based on MMI N24, but among the
highest scores based on the other MMI formulations. In addition, the nekton-only MMI
formulation (N 24) yielded the lowest MMI scores at SAHI_1 and FIIS 1, 2, and 3, but yielded
the highest MMI scores for most of the ASIS MSUs.
MMI Application to Earlier Years
For two of the MSUs with earlier-years’ monitoring data available, the MMI formulation
used to derive MMI scores did not affect the perceived trends in salt marsh condition over time:
within GSH_1, the MMI VN24 and the MMI V24 scores both decreased from 2003 to 2008-
2013, and within SAHI_1, they increased from 2004 to 2008-2013 (Figure 5). In contrast, for
three of the MSUs, the temporal pattern of MMI scores varied with the MMI formulation. Within
CACO_1 and CACO_2, MMI VN24 scores decreased from 2006 to 2008-13 while MMI V24
scores remained fairly stable over the same time period, and within CACO_3, MMI VN24
increased from 2005 to 2008-13 while MMI V24 decreased (Figure 5).
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Discussion
The MMI construction algorithm (Schoolmaster et al. 2012; 2013) used in this study for
NPS northeastern salt marshes produced MMIs with very strong correlations to human
disturbance. This algorithmic approach identifies the metrics that, in combination, best reflect the
multivariate response to disturbance. The vegetation metrics that were positively correlated with
disturbance (frequency or percent cover of the introduced species Phragmites australis and
brackish border species, Table 6) are indicative of vegetation communities associated with lower
salinity (Supplemental Table 1) and likely reflected the degree to which tidal restriction reduces
integrity of northeastern salt marshes by reducing salt water influx (cf. Roman et al. 2000). In
addition, Phragmites australis tends to form monotypic stands that reduce wetland diversity and
compromise ecosystem services (Zedler and Kercher 2004). Vegetation metrics describing the
frequency, abundance, or percent composition of Distichlis spicata, Spartina patens, high
salinity tolerant species, and native species were all negatively correlated to disturbance (r = -
0.44 to -0.80, Table 6), and are indicative of natural salt marsh communities. The direction and
magnitude of the correlations of the selected nekton metrics to HDI (Table 7) were also
consistent with observations in northeastern salt marshes. For example, several studies have
shown that resident fish abundance is reduced and shrimp abundance is increased in salt marshes
in densely populated and developed watersheds (James-Pirri et al. 2014) or those with a high
degree of fragmentation or subsidence (Deegan 2002). Ultimately, the vegetation and nekton
metrics selected by the MMI assembly algorithm provide a basis for developing causal models of
these systems. It is important to note that the vegetation metrics that were positively correlated
with disturbance in our study of northeastern salt marshes would not indicate degraded
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conditions in naturally-occurring lower salinity marshes, which are typically dominated by
brackish border species (Supplemental Table 1). These results emphasize the value of clearly
defining the system of interest and the candidate metrics to ensure MMI applicability and
interpretability.
The strong similarity in the vegetation-based MMIs regardless of sample size (MMI V24
and MMI V33, Table 6) suggested that the vegetation communities in the smaller, 24-MSU
dataset adequately represented those of the larger, 33-MSU group of northeastern salt marshes,
and lends confidence to the applicability of nekton metrics included in MMI N24 and MMI
VN24 beyond the 24 MSUs with nekton data. Which MMI provides the “best” integrative index
of northeastern salt marsh condition, however, depends on various considerations. Each MMI
assembly produced MMIs that were maximally sensitive to disturbance within the range of data
available. The exceptionally strong correlation between MMI scores and HDI that emerged when
the assembly algorithm included both vegetation and nekton metrics would argue for MMI
VN24 as the most robust indicator of human disturbance (Figure 3). However, the final MMIs
resulting from each of the other data sets also exhibited strong correlations to HDI (Figure 3) and
might be deemed preferable under certain conditions. In particular, time and resource constraints
often dictate the types of data that can be collected for salt marsh assessment, so an MMI based
on metrics that are the least costly to quantify could be warranted for long-term implementation.
Our analysis suggested that MMIs incorporating strictly vegetation metrics (MMI V33, MMI
V24wt) are highly sensitive to anthropogenic disturbance in these systems. MMI selection may
also be guided by specific management, monitoring, and assessment questions. For example,
estuarine restoration goals often focus partly on re-establishing the hydrologic connectivity
necessary for estuarine and marine fish to access salt marsh habitat (Simenstad and Thom 1996;
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Dionne et al. 1999; Roman et al. 2002). In such instances, it would be desirable to include nekton
metrics in an MMI used to assess restoration responses. During MMI construction, all candidate
MMIs with Δ AIC < 2 were retained as viable alternatives, and two nekton-only MMIs relied on
one or two metrics alone (MMI N24a, MMI N24e, Table 7); these MMIs provide parsimonious,
highly focused assessment tools. Schoolmaster et al. (2013) described how certain metrics of
intrinsic management value could be weighted to increase the likelihood of being selected by the
MMI assembly algorithm. We did not employ such value-weighting in this study, but that
approach would offer further opportunity to develop MMIs that are highly relevant to defined
management goals.
MMIs are often developed as tools to detect changes in ecosystem condition over time or
to compare the existing condition of multiple systems in space. Our study revealed some
differences in salt marsh MMI trends (Figure 5) and spatial patterns (Figure 4) depending on
MMI formulation, illustrating the potential influence of the metrics used in MMI construction on
interpreting marsh condition. For example, at CACO_3, MMI VN24 scores increased after 2005,
whereas MMI V24 scores decreased during the same period (Figure 5). This MSU, located in
East Harbor Lagoon on outer Cape Cod, MA, underwent tidal restoration between 2001 and
2008, with a concomitant increase in the density of resident fish in marsh pools (Smith et al.
2010). Nekton often respond rapidly when hydrologic connections to salt marsh habitat are
reestablished (e.g. Simenstad and Thom 1996; Burdick et al. 1997), and by incorporating nekton
metrics, MMI VN24 reflected this improved marsh function. Vegetation monitoring from 2008 –
2013 in CACO_2, located in a tidally-unrestricted section of Hatches Harbor on outer Cape Cod,
revealed an abundance of salt marsh grasses with high salinity tolerance, contributing to high
MMI scores among formulations including vegetation metrics; nekton samples, however, were
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dominated by introduced European green crabs (Carcinus maenas), contributing to a low MMI
N24 score (Figure 4). These differences reinforce the importance of considering monitoring and
assessment goals carefully in identifying, and possibly value-weighting, the candidate metrics
used during MMI construction.
The algorithm-based MMI is, by design, dependent also on the formulation of the HDI.
The PCA approach we used to generate the HDIs summarized the majority of the variation in the
disturbance metrics into a single value for each site. Consequently, the HDIs were related most
to metrics with the highest loadings relative to the first Principal Component score (e.g., Fill /
Fragmentation, Tidal Flushing, and Tidal Restriction). Using different approaches to generating
disturbance indices, such as an additive method (Carullo et al. 2007), might yield different HDIs
and, thus, different MMI metric sets and MMI scores across individual MSUs (Falcone et al.
2010). It is equally important to note that the MMIs characterized marsh condition in the context
of the anthropogenically-influenced disturbance metrics that entered into the HDI. We
incorporated disturbance metrics that quantified a combination of physical, chemical, and land-
use stressors that were easily calculated from existing aerial imagery in a cost-effective and
efficient manner. Other important aspects of marsh degradation, such as the rate of sea-level rise
relative to marsh surface elevation, are not represented in our HDI, and thus the MMI scores we
generated may omit potentially important components of marsh condition.
The MMIs generated here for northeastern salt marshes fit within the multilevel
framework for wetland assessment used widely in the US (Fennessy et al. 2007; Wardrop et al.
2007; Miller et al. 2016). Consisting of a landscape-scale analysis of remotely sensed data (level
1), a rapid field assessment of structural attributes (level 2), and intensive field measurements of
wetland biological, chemical, and physical characteristics (level 3), the multilevel framework
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provides a comprehensive set of surveys that can be employed singly or in concert, depending on
the goals and purpose of the assessment and the resources available (Wardrop et al. 2007). The
majority of wetland rapid assessment methods include condition indicators focused on vegetation
communities (Fennessy et al. 2007), and many level-2 MMIs are vegetation based (e.g. Wigand
et al. 2011; Miller et al. 2016). The NWCA also relies heavily on a vegetation MMI (VMMI) for
reporting on wetland condition at the national scale (USEPA 2016). Our MMIs generated from
vegetation metrics only (MMI V24, MMI V33) are useful for reporting on salt marsh condition
at the level of resolution of rapid assessments and the NWCA VMMI. An important criterion of
wetland rapid assessments and NWCA monitoring is the amount of field effort demanded:
Fennessy et al. (2007) defined “rapid” assessments as requiring no more than two people a half
day to complete on site, and NWCA field sampling for each site is intended to be accomplished
in one day (USEPA 2011). The quadrat-based vegetation monitoring within each NPS MSU was
generally accomplished by two people in from 0.5 – 1 d per site, and sampling methods requiring
even less field time could be employed as desired to generate the plant species and plant
community metrics that were derived from these data (Table 2). Our nekton-based MMIs were
derived from intensive field measurements of biotic indicators of wetland trophic functions, and
would fall within level-3 wetland monitoring. At both levels, the MMI assembly algorithm
generated MMIs exhibiting the greatest possible correlation with disturbance.
The MMIs produced in this study can be used to assess condition and detect change in
northeastern National Park salt marshes, but we believe the algorithmic approach used here is
broadly applicable to salt marsh assessment. The causal networks linking human disturbance to
the measured biological metrics in salt marsh ecosystems are complex, and interactions among
agents of change, stressors, and responses may introduce unexplained variability and reduce
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sensitivity of individual metrics to perceived disturbance. The assembly algorithm identified the
optimal metric set, reducing a long list of potential candidate metrics to the vital few using an
objective and transparent selection process. We expect this approach to MMI development
would be useful for salt marsh monitoring and assessment at a variety of scales, from
establishing local baseline conditions and management responses to evaluating regional status
and trends.
Acknowledgements
This work was supported, in part, by funding from USGS National Park Monitoring Program. In
addition, GRG and JGB acknowledge support from the USGS Climate and Land-Use Research
and Development Program. We are grateful to Holly Plaisted for assistance with data
preparation, to Jennifer Olker for analytical guidance, and to the many NPS professional staff
and summer research assistants who helped with vegetation and nekton sampling. We extend
sincere appreciation to Nick Danz and two anonymous reviewers for helpful comments that
improved the manuscript. All data used in this paper are publicly available through USGS at
https://dx.doi.org/10.5066/F7ZP449D. The use of trade, product, or firm names in this
publication is for descriptive purposes only and does not imply endorsement by the US
Government.
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Table 1 Location and size (area in hectares) of marsh study units monitored from 2008 – 2013 in NCBN parks, availability of vegetation (V) and nekton (N) data, and number of years sampled
NPS Park State Marsh Study Unit (MSU) Area (ha)
2008-13
Data # YearsAssateague Virginia ASIS_1 5.1 V,N 3Island NS ASIS_2 7.5 V,N 3
ASIS_3 6.7 V,N 3ASIS_4 8.3 V,N 3ASIS_5 8 V,N 3ASIS_6 5.1 V,N 3ASIS_7 7.4 V,N 3ASIS_8 5.4 V,N 3ASIS_9 6.6 V,N 3
Cape Cod NS Massachusetts CACO_1a 55.7 V,N 2CACO_2a 34.6 V,N 2CACO_3a 78.8 V,N 2CACO_4 72.2 V 2CACO_5 26.1 V 2CACO_6 47 V 2CACO_7 25.9 V 2CACO_8 5.6 V 2CACO_9 19.8 V 2CACO_10 112.6 V,N 2CACO_11 167.8 V 2
Fire Island NS New York FIIS_1 5.2 V,N 3FIIS_2 5.2 V,N 3FIIS_3 4.7 V,N 3FIIS_4 6.2 V 2FIIS_5 5 V,N 3FIIS_6 6 V,N 3FIIS_7 5.2 V,N 3FIIS_8 5.4 V,N 3FIIS_9 7.1 V,N 3
Gateway NRA, Sandy Hook Unit New Jersey GSH_1a 7 V,N 2
GSH_2 15.1 V 2GSH_3 6.5 V,N 2
Sagamore Hill NHS New York SAHI_1a 2.7 V,N 3a denotes units that were also sampled from 2003-7 as part of a pilot monitoring program
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Table 2 Candidate vegetation metrics from NCBN parks for incorporation into salt marsh MMIs
Vegetation CommunitiesPercent cover of low marsh speciesPercent cover of high marsh speciesPercent cover of brackish border speciesPercent cover of salt marsh border speciesPercent cover of panne, pool and creek speciesPercent cover of native speciesPercent cover of high salinity-tolerant speciesSpecies richness (total no. spp. within each sample averaged over MSU)Native species richness (total no. native spp. within each sample averaged over MSU)Native species richness (total no. native spp. within MSU)Introduced species richness (total no. introduced spp. within each sample averaged over MSU)a
Introduced species richness (total no. introduced spp. within MSU)a
Percent native species richness (no. native spp. in MSU /total no. spp. in MSU)Percent introduced species richness (no. introduced spp. in MSU /total no. spp. in MSU)b
High salinity-tolerant species richness (no. high salinity-tolerant spp. within MSU)High salinity-tolerant species richness (no. high salinity-tolerant spp.in sample /total no. spp. in sample)Percent high salinity-tolerant species (no. high salinity-tolerant spp. in MSU /total no. spp. in MSU)Percent high salinity-tolerant species (no. high salinity-tolerant spp. in sample /total no. spp. in sample)
Vegetation speciesPercent frequency Spartina alternifloraPercent cover Spartina patensPercent frequency S. patensPercent cover Distichlis spicataPercent frequency D. spicataPercent cover Salicornia spp.Percent frequency Salicornia spp.Percent frequency Iva frutescensPercent cover Scirpus and Schoenoplectus sppb
Percent frequency Scirpus and Schoenoplectus sppb
Percent cover Phragmites australisPercent frequency P. australis
a Metric used only in the dataset consisting of vegetation data alone from 33 MSUs (V33)b Metric used only in the dataset from the 24-MSU subset including both vegetation and nekton data (V24, VN24)
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Table 3 Candidate nekton metrics from NCBN parks for incorporation into salt marsh MMIs
Nekton communitiesRelative abundance of resident fish speciesRelative abundance of resident shrimp speciesRelative abundance transient fish speciesTotal density (no. m-2)Fish density (no. fish m-2)Decapod density (no. decapods m-2)a
Species richness (total no. spp. within each sample averaged over MSU)Native species richness (total no. native spp. within MSU) SiteIntroduced species richness (total no. introduced spp. within MSU)Resident fish species richness (total no. resident fish spp. within MSU)Resident fish species richness (total no. resident fish spp. within each sample averaged over MSU)Percent native species richness (no. native spp. in MSU /total no. spp. in MSU)Percent native species richness (no. native spp. in sample /total no. spp. in sample)Percent resident fish species richness (no. resident fish spp.in MSU /total no. spp. in MSU)
Nekton speciesRelative abundance of Fundulus spp.Relative abundance of Cyprinodon variegatusRelative abundance of Menidia spp.Length of Fundulus heteroclitus
aDecapod = Crabs and Shrimp
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Table 4 Land use metrics used to calculate the Human Disturbance IndexMetric Classes/Rank Definition
Ditch density no (0), low (2), moderate (4), severe (6)
Extent of ditching and draining of the marsh unit
Tidal restriction no (0), yes (6) Potential restriction of normal flow and tidal range by features such as under-sized culverts or bridges, causeways, dikes, etc.
Tidal flushing well flushed (0), moderately flushed (3),poorly flushed (6)
Degree of hydrologic connection with marine waters
Fill/fragmentation no (0), low (2), moderate (4), severe (6)
Extent of salt marsh areas filled or fragmented (i.e. were once whole systems).
Relativized %Disturbed_150m
continuous converted to ranked variable
% disturbed landa in 150-m buffer * (area of buffer/area of MSU)
Relativized %Disturbed_1km
continuous converted to ranked variable
% disturbed landa in 1-km buffer * (area of buffer/area of MSU)
Point source discharges of pollutants
no (0), low (2), moderate (4), severe (6)
Outfalls, drains emptying into marsh
aAggregated NLCD classes: Developed Open Space, Developed Low Intensity, Developed Medium Intensity, Developed High Intensity, Barren Land and Agricultural
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Table 5 Principle Component loadings (PC1 – PC7) for each disturbance metric incorporated in the HDIs. Eigenvalue size for each Principle Component is also shown
HDI33
Disturbance Metrics PC1 PC2 PC3 PC4 PC5 PC6 PC7Ditch Density 0.25 -0.50 -0.29 0.67 0.34 0.19 -0.08Fill / Fragmentation -0.52 -0.18 -0.08 0.00 -0.39 0.60 -0.41Tidal Flushing -0.50 -0.07 -0.03 -0.20 0.69 -0.28 -0.39Tidal Restriction -0.43 -0.43 -0.32 -0.15 -0.07 -0.11 0.70Point Source Discharges of Pollutants -0.40 0.36 -0.12 0.64 -0.28 -0.46 -0.04Relativized % Disturbed_150m -0.26 0.08 0.73 0.30 0.26 0.31 0.36Relativized % Disturbed_1km 0.05 -0.62 0.50 0.00 -0.32 -0.45 -0.23
Eigenvalue Size 2.68 1.46 1.31 0.67 0.47 0.28 0.13
HDI24
Disturbance Metrics PC1 PC2 PC3 PC4 PC5 PC6 PC7Ditch Density -0.13 0.64 0.14 0.6 -0.43 0.13 0.03Fill / Fragmentation 0.51 0.22 0.12 -0.27 0.07 0.78 -0.01Tidal Flushing 0.49 -0.29 0.11 -0.03 -0.6 -0.21 0.51Tidal Restriction 0.45 0.35 0.34 -0.21 0.03 -0.52 -0.5Point Source Discharges of Pollutants 0.36 -0.23 0.27 0.65 0.55 -0.03 0.14Relativized % Disturbed_150m 0.32 -0.23 -0.64 0.31 -0.23 0.08 -0.53Relativized % Disturbed_1km 0.23 0.48 -0.6 -0.07 0.31 -0.25 0.44
Eigenvalue Size 2.90 1.40 1.23 0.80 0.38 0.18 0.10
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Table 6 Metrics included in the MMIs and correlations of individual metrics with HDI scores for datasets including vegetation metrics only. Single-letter subscripts refer to candidate MMIs with ΔAIC < 2; wt refers to the model-weighted MMI. Dashes indicate metrics that were not included in the MMI for that dataset
Correlation with HDI
Metrics included in MMIMMI V33
MMI V24a
MMI V24b
MMI V24wt
Frequency of Distichlis spicata -0.52 -0.59 -0.59 -0.59Frequency of Phragmites australis 0.65 0.67 - 0.67Frequency of Spartina patens -0.56 - - -Percent cover of brackish border spp. - 0.64 - 0.64Percent cover of Phragmites australis - - 0.73 0.73Percent cover of salt marsh border spp. 0.12 - 0.12 0.12Percent of high salinity spp. at sample level -0.80 - -0.75 -0.75
Correlation of MMI with HDI -0.93 -0.89 -0.90 -0.91
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Table 7 Metrics included in the MMIs and correlations of individual metrics with HDI scores for the dataset including nekton metrics only. Single-letter subscripts refer to candidate MMIs with ΔAIC < 2; wt refers to the model-weighted MMI. Dashes indicate metrics that were not included in the MMI
Correlation with HDI
Metrics included in MMIMMI N24a
MMI N24b
MMI N24c
MMI N24d
MMI N24e
MMI N24wt
Relative abundance of resident shrimp - 0.49 0.49 - - 0.49Relative abundance of resident fish -0.79 -0.79 - - -0.79 -0.79Total density - - - 0.30 - 0.30Introduced spp. at site level - - 0.59 - - 0.59Resident fish spp. at site level - - -0.58 - -0.58 -0.58Resident fish spp. at sample level - - - -0.64 - -0.64Percent of native spp. at sample level - -0.44 - -0.44 - -0.44
Correlation of MMI with HDI -0.80 -0.84 -0.84 -0.83 -0.80 -0.85
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Figure Captions
Fig. 1 Location of parks sampled within the Northeast Coastal and Barrier Network. Black
triangles = MSUs with vegetation and nekton data; white circles = MSUs with vegetation data
only
Fig. 2 MSU scores for the 24- (HDI 24) and 33-MSU (HDI 33) Human Disturbance Indices
(HDI). MSUs are listed along the horizontal axis in latitudinal order from north to south. CACO
= Cape Cod National Seashore, SAHI = Sagamore Hill National Historic Site, FIIS = Fire Island
National Seashore, GSH = Gateway National Recreation Area – Sandy Hook Unit, and ASIS =
Assateague Island National Seashore
Fig. 3 MMI scores as a function of Human Disturbance Index for the (A) MMIVN(24), (B) model-
weighted MMIV(24), (C) model-weighted MMIN(24), and (D) MMIV(33) datasets. Correlations (r-
values) are also shown
Fig. 4 MMI scores by MSU for the V33, V24, N24, and VN24 datasets. MSUs are listed along
the horizontal axis in latitudinal order from north to south. CACO = Cape Cod National
Seashore, SAHI = Sagamore Hill National Historic Site, FIIS = Fire Island National Seashore,
GSH = Gateway National Recreation Area – Sandy Hook Unit, and ASIS = Assateague Island
National Seashore
Fig. 5 Change in MMI scores over time at 5 MSUs using the (A) MMI VN24 and (B) MMI V24
formulations. CACO = Cape Cod National Seashore, SAHI = Sagamore Hill National Historic
Site, and GSH = Gateway National Recreation Area – Sandy Hook Unit
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