\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[greek,ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Assessing the Utility of Shellfish Sanitation Monitoring Data
for Long-Term Estuarine Water Quality Analysis}



\author[1]{Natalie Chazal}%
\author[1]{Megan Carr}%
\author[1]{Andrew Haines}%
\author[1]{Andrew K. Leight}%
\author[1]{Natalie Nelson}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup








\sloppy


\textbf{ABSTRACT}

Regular testing of coastal waters for fecal coliform bacteria by
shellfish sanitation programs could provide data to fill large gaps in
existing coastal water quality monitoring, but research is needed to
understand the opportunities and limitations of using these data for
inference of long-term trends. In this study, we analyzed spatiotemporal
trends from multidecadal fecal coliform concentration observations
collected by a shellfish sanitation program, and assessed the
feasibility of using these monitoring data to infer long-term water
quality dynamics. We evaluated trends in fecal coliform concentrations
for a 20-year period (1999-2021) using data collected from spatially
fixed sampling sites (n = 466) in North Carolina (USA). Findings
indicated that shellfish sanitation data can be used for long-term water
quality inference under relatively stationary management conditions, and
that salinity trends can be used to measure the extent of
management-driven bias in fecal coliform observations collected in a
particular area.

\textbf{1. INTRODUCTION}

Healthy estuarine environments are critical for maintaining ecological
stability, coastal economies, and human health standards. In order to
maintain and even improve these habitats, metrics of current and past
conditions must be evaluated to inform proper management. Water quality
measurements can be used to indicate overall estuarine health and can
aid in understanding increasing coastal threats such as rising sea
levels, increased salinities, and urbanization. Long-term water quality
analysis is key for developing target thresholds for future management
action as well as assessing the efficacy of past management measures
(Cloern et al., 2016). The value of historical observations in advancing
understanding of estuarine water quality has been demonstrated by
multi-decadal studies of several systems, including the San Francisco
Bay area (Beck et al., 2018; Cloern et al., 2016), May River, South
Carolina (Souedan et al., 2021), Texas's coastline (Bugica et al.,
2020), and the Chesapeake Bay area (Zhang et al., 2018; Harding et al.,
2019). Most notably, long-term water quality monitoring in the
Chesapeake Bay has led to the identification of climatic and
anthropogenic drivers for certain water quality parameters and
subsequent evaluation of the effectiveness of past management and
restoration efforts (Kemp et al., 2005; Leight et al., 2011; Zhang et
al., 2018; Harding et al., 2019).

Datasets used for prior longitudinal water quality studies are commonly
a product of governmental agencies developing localized programs, like
the Chesapeake Bay Program (Chesapeake Bay Monitoring Program, 2022), in
response to increasing population and significant degradation of vital
estuarine ecosystems. While national and regional efforts have attempted
to provide unbiased, sustained monitoring, these programs currently lack
the spatial extent needed to capture coastwide water quality trends. The
National Estuarine Research Reserve System (NERRS) is one of the few
organizations with dedicated coastal water quality monitoring stations,
which are included as part of the NERRS System Wide Monitoring Program
(SWMP) that maintains 355 coastal water quality monitoring stations
across 29 designated coastal reserves along the USA coastline (National
Estuarine Research Reserve System, 2022). Compared to the over 13,500
freshwater monitoring stations maintained by the United States
Geological Survey (USGS, 2022), the relatively small number of water
quality monitoring stations across coastal and estuarine waters (NOAA
Tides \& Currents, 2022; US EPA, 2022) are likely not representative of
the variations in environmental conditions that we observe across the
tens of thousands of miles of shoreline along the United States.

Because of the limited number of unbiased monitoring programs, the
ability to use water quality data from regulatory operations presents a
potentially valuable resource for assessing long-term estuarine
conditions. Regulatory programs differ from monitoring programs by
collecting water quality samples to meet regulatory requirements and
inform short-term decision-making. For example, in North Carolina (NC),
there are four NERRS SWMP monitoring stations and eight coastal stations
with water quality data available through the USGS (South Atlantic Water
Science Center, North Carolina Office, 2022) and fifty stations from the
NC Ambient Monitoring System (Water Quality Portal, 2021), but the NC
Division of Marine Fisheries (NCDMF) shellfish sanitation program
maintains 1,924 water quality monitoring stations. In fact, state
shellfish sanitation programs across the USA collect an abundance of
water quality observations, and often have for decades. Shellfish
mariculture is highly dependent on water quality monitoring due to the
direct influence that ambient conditions have on the safety of shellfish
meat consumption. The U.S. Food and Drug Administration's National
Shellfish Sanitation Program (NSSP) was developed in 1925 to maintain
public safety and human health standards in relation to the consumption
of shellfish grown in potentially polluted waters (NSSP, 2019). The
implementation of the NSSP has resulted in systematic sampling of water
quality for day-to-day fisheries regulation, specifically for Fecal
Indicator Bacteria (FIB), a group of bacteria that are commonly used as
a proxy measure for harmful pathogen loads in the waterway that could
potentially be incorporated into shellfish meat through filter feeding.
Thus, fecal coliforms (FC), a type of FIB, and other environmental
factors that contribute to FC load and water quality, are regularly
measured in shellfish growing waters due to the food safety
implications. As a product of this regular testing, fisheries operations
have accumulated decades of data with the potential to provide insights
on historical trends with wide spatial extents, potentially filling gaps
in long-term water quality monitoring capacity.

However, because of the limited resources and industry specific
priorities, regulatory data can maintain underlying biases as a result
of the sampling methodology used to collect the water quality sample.
Often, the collection of a sample can be motivated by day-to-day
operational decisions, such as weather, the availability of field
technicians, and ease of collection. These operational decisions lead to
non-random sampling that provides observations that are not always
representative of the system's true dynamics. Engaging regulatory
personnel to understand their fisheries management and sampling
decisions is necessary to properly analyze the observations collected by
shellfish sanitation programs.

For example, the NSSP permits states to employ one of two sampling
strategies when collecting regulatory water quality data in shellfish
growing waters: adverse pollution condition sampling and systematic
random sampling. The adverse pollution condition sampling strategy
describes sampling in periods when known contamination events (commonly
due to point-source pollution events or rainfall events) have degraded
the water quality, and data collected under these conditions capture
peak contamination. States must collect ``a minimum of five
samples\ldots{} annually under adverse pollution conditions from each
sample station in the growing area'' (NSSP, 2019) to meet NSSP sampling
requirements. In contrast, the systematic random sampling strategy
describes the collection of data across ``a statistically representative
cross section of all meteorological, hydrographic, and/or other
pollution events'' (NSSP, 2019), resulting in the data collection under
varied environment and climactic conditions. For state programs that use
systematic random sampling, the NSSP requires samples be collected at
least 6 times throughout the year (NSSP, 2019). As a result of the
requirements for the conditions under which the two systems of sampling
can take place, the resulting data may be biased and impact their
utility for use in long-term water quality assessments. With our growing
reliance on aquaculture and the expanding value of shellfish production
driving the development of fisheries management infrastructure (Azra et
al., 2021), long-term datasets available through shellfish sanitation
programs will become increasingly valuable. Realizing the potential of
regulatory datasets to inform long-term water quality trends is a vital
next step for assessing the health of our coastal ecosystems, but
research is needed to determine the utility of these data for water
quality analyses.

The goal of this study was to utilize shellfish management data to infer
long-term spatiotemporal trends in water quality parameters, including
FC and salinity, while accounting for variation in routine sampling
conditions and environmental landscapes. Study objectives included (1)
analyzing spatiotemporal trends from multidecadal fecal coliform
concentration observations collected by a shellfish sanitation program,
(2) identifying possible management and environmental drivers of fecal
coliform trends, and (3) assessing the feasibility of using these
monitoring data to infer long-term water quality dynamics. We focused on
North Carolina's shellfish waters as a representative study system due
to the availability of public, digitized multidecadal data, and the
region's rapidly growing population, wide variety of land use
characteristics along the coast, presence of the second largest
estuarine system in the contiguous USA, and growing shellfish industry.
Ultimately, this study demonstrates the application of shellfish
management data for long-term water quality trend analysis in estuaries,
informs future resource management strategies, and reveals new insights
into the functioning of coastal systems.

\textbf{2. METHODS}

\textbf{2.1 Study Area Description: Shellfish Waters in North Carolina,
USA}

The study area spanned all marine and estuarine waters in coastal North
Carolina, which are subdivided into shellfish growing areas (SGAs)
(Figure 1a,b). SGAs are subdivisions of waterways used to support
shellfish harvest through delineating administrative boundaries for
regulatory purposes. SGAs spatially cover North Carolina's shoreline
from Currituck Sound in the north to Brunswick County in the south.
These SGAs are named with alphabetic letters (e.g. ``A'', ``B'', etc.;
Figure 1b), and further categorized through a letter-number system (ex.
``A01''). There are 9 SGA letter groups along North Carolina's coast and
these groups of SGAs have~similar ecological features and approximately
correspond to County jurisdictions. This study excludes the northernmost
SGA (``I'') due to the lack of open shellfish growing areas and
discontinuous water quality data. SGAs vary in environmental and
managerial conditions across the NC coast. These variations manifest as
differences in estuarine type which can be defined using physical
measures of area, depth, volume, freshwater flow, and salinity within
the estuary (Engle et al., 2007).

SGAs can contain multiple classifications including approved,
conditionally approved, restricted, and prohibited areas. Of the 9,208
km\textsuperscript{2} of shellfish waters, 5,910
km\textsuperscript{2}(64.18\%) are approved or conditionally approved.
Observations collected through routine monitoring programs are used to
help establish the classifications within SGAs. The NCDMF Shellfish
Sanitation and Recreational Water Quality Section has jurisdiction over
the classification of coastal waters for shellfish harvest, and also
regulates closures and openings of conditionally approved SGAs. A
majority of the NCDMF sampling stations included in this analysis are
within the approved and conditionally managed SGAs (Figure 1a).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} . Leftmost map (a) illustrates NCDMF conditional
management units with water quality sampling stations. Blue waters
correspond to waters that are not approved for shellfish growing and
harvesting, while the green areas correspond to the conditionally
managed shellfish growing waters. The black points indicate the exact
locations of the NCDMF water quality sampling stations and the orange
crosses indicate the locations of the weather stations used to gather
the precipitation time series data. The middle map (b) illustrates the
NCDMF SGAs colored by the first letter of their SGA name. The rightmost
map (c) illustrates rainfall thresholds (as of July 2021) for NCDMF
conditionally managed areas measured in inches. Dark red indicates a
rainfall closure threshold of 1 inch where the lightest white indicates
areas with a rainfall closure threshold of 4 inches. Study area maps
were created in ArcGIS Pro version 2.8.

\emph{2.1.1 Sampling strategies}

In accordance with the NSSP, NCDMF routinely samples all shellfish
growing areas on a regular basis (6 times annually) using the systematic
random sampling strategy, meaning samples for each station are collected
at randomly scheduled timepoints throughout the year; however, there are
some constraints as to when sample collection is permissible.
Specifically, samples are only collected during conditions when the SGAs
are open for harvest or assumed to be unimpacted by unsafe FC levels,
resulting in the subsequent data not capturing peak FC concentrations.
Because freshwater input and runoff are tied to increased FC
concentrations in estuarine waters, precipitation intensity is used as a
management indicator for closures of conditionally approved waterways
(NSSP, 2019; Leight et al., 2016). In North Carolina, rainfall closure
thresholds in conditionally approved waters range from 1-3 inches of
rainfall within a 24-hour period (Figure 1c) and dictate if a managed
shellfish area will be closed for harvesting after a meteorological
event. The ``emergency closure'' of additional areas can occur after
higher rainfall amounts are noted and, for the purposes of this
analysis, those areas will be represented by a 4 inch threshold.
Rainfall thresholds are assigned to conditionally managed areas. To
reopen closed areas that have been temporarily closed following
exceedances of those rainfall thresholds, NCDMF tests the water, and
reopening will only occur after samples confirm safe harvest conditions,
which is defined by waters that do not exceed a FC density of 14 MPN per
100 mL of sample (NSSP, 2019); sampling for reopening is hereafter
referred to as ``conditional sampling''. NCDMF balances its limited
resources with speed of reopening by only conditionally sampling when
the organization suspects the FC concentrations will be low enough to
support reopening.~SGAs A, C, D, E, and the northern half of B are
characterized by moderate to low rainfall thresholds (i.e., 1-3 inches)
whereas SGAs F, G, H, I, and the southern half of B have high rainfall
thresholds (i.e., 4 inches; Figure 1c).

Under systematic random sampling, routine sampling in conditionally
approved waters must occur when the SGA is open and not during a
temporary closure. Accordingly, this stipulation creates bias in the
routine monitoring data. As such, areas with a 1 inch rainfall threshold
will close more often than areas with 4 inch thresholds, resulting in
more restrictive sampling conditions and more homogeneous water quality
samples in the long-term. Furthermore, NCDMF will change rainfall
thresholds or exclude stations for areas with persistently poor water
quality, resulting in the longest-standing monitoring stations typically
being located in growing areas that are known to be productive with
relatively good water quality, contributing another source of bias to
the data.

\textbf{2.2 Data Description}

To analyze shellfish sanitation program data, water quality observations
were compiled for the study area along with additional environmental
variables believed to be potential descriptors of FC trends including
salinity, precipitation, land use, and sampling station distance to
closest shoreline.~These variables have been established as relevant to
FC concentrations in estuarine waters (Chigbu et al., 2004; Chigbu et
al., 2005; Campos et al., 2013; Leight et al., 2016)

\emph{2.2.1 Water quality}

During each sampling event, a grab sample is collected for FC analysis.
To enumerate FC, NCDMF uses a five-tube decimal dilution, method SM 9221
(NSSP, 2019). Data records also include monitoring type to indicate the
type of sampling (either conditional or routine), coded SGA designation
(i.e., names listed in Figure 1b), station name, date of the sample,
tide at the time of sample, salinity, and water temperature. The data
are publicly available through NCDMF, and have been reorganized into a
normal form database called ``ShellBase'', which is freely available
through the Southeast Coastal Ocean Observing Regional Association's
Data Portal (SECOORA, 2022).

In our study, the data were filtered to only include routine samples
from stations that had 20 years of continuous water quality sampling
data from 1999 to 2021. 466 stations met these conditions. We chose the
time period of 1999 to 2021 due to changes in management procedures in
the NCDMF in the 1990's. Throughout the 1990's, NCDMF progressively
introduced conditional management plans to many of the growing areas.
Therefore, by confining the dataset from 1999 to 2021, we are safely
ensuring that the conditional management plans have been introduced and
that the sampling would be reflective of that. Observations collected
through conditional sampling were not considered in the analysis because
the varied conditions under which conditional sampling occurs results in
skewed FC concentrations and is not as representative of the system as
the data collected through routine sampling design.

\emph{2.2.2 Salinity}

Because data collected through the NCDMF monitoring program may have
preferential sampling bias, we sought to analyze additional salinity
data collected from an unbiased monitoring program in order to
understand how the NCDMF observations compared to an independent
dataset. We accessed the North Carolina Division of Water Resources
(NCDWR) Water Quality Portal (Water Quality Portal, 2021). The NCDWR
dataset consists of 17 monitoring stations that had at least 10 years of
salinity data between the years 1999 and 2021 and were within proximity
to the NCDMF water quality sampling stations (Figure 2); stations with
fewer than 10 years of data or in portions of the estuary that did not
overlap with NCDMF stations (e.g., fresh headwaters) were not considered
in the analysis. However, data from NCDWR sites in SGAs D and E were
still considered despite being relatively distant from NCDMF monitoring
locations as these were the only NCDWR sites present in SGAs D and E.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2} . Map of NCDWR water quality sampling stations along
the coast of North Carolina. Numbers 1 -- 4 are added to annotate
stations referenced in Figure 5. Stations are colored by their use in
the analysis. Study area maps were created in ArcGIS Pro version 2.8

\emph{2.2.3 Precipitation}

The precipitation data was gathered through the North Carolina State
Climate Office for weather stations (n = 70) that contained continuous
data for 1999 to 2021 (Figure 1a). Precipitation data were related to
water quality sampling locations as a function of proximity.

\emph{2.2.4 Geographic context}

Land Use and Land Cover (LULC) data for North Carolina in 2001 and 2019
were compiled from the National Land Cover Database created by the
Multi-Resolution Land Characteristics Consortium (MRLC). LULC categories
were consolidated into ``Developed'', ``Barren'', ``Cultivated'',
``Vegetated'', and ``Wetlands'' classes (Table S1) and summarized for
each coastal watershed, defined by USGS 12 digit Hydrologic Unit Codes
(HUC12; Figure 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3}. Map of coastal North Carolina HUC12 watershed areas
that were used to summarize land use change between 2019 and 2001. The
watersheds in this map are colored by the SGA name that the watershed is
closest to and therefore associated with. Study area map was created in
R version 4.1.0 with the `sf' version 0.9 package.

Percent changes within each consolidated LULC class for all coastal
watershed areas from 2001 to 2019 were calculated in R version 4.1.0.
Water quality stations were then related to watersheds based on
distance, with stations being assigned to the nearest watershed. Because
of the variation in flow volumes of contributing tributaries to these
estuaries it is difficult to generalize the exact transport distance of
non-point sources of FCs in coastal systems. It is known that FCs are
generally sourced from surrounding watersheds and their survivability in
the water is dependent on a wide range of environmental factors (Weiskel
et al., 1996; Cho et al., 2016; Korajkic et al., 2019). For context,
Weiskel et al. (1996) demonstrated that even point source discharges of
FC being diluted to near-background levels within 15 meters of the
source (Weiskel et al. 1996). Each station's distance to shoreline was
also calculated, which was done through nearest feature geoprocessing in
R using the estuarine shoreline data layer from the NCDMF Estuarine
Shoreline Mapping Project (NC Division of Coastal Management, 2007).

\textbf{2.3 Data Analysis}

The 20-year trends in water quality variables were analyzed to allow for
(1) comparison against different environmental variables to understand
possible drivers of change and (2) evaluation of spatial variability in
water quality trends. Mann-Kendall (MK) testing and Sen Slope Estimators
were calculated and applied to annual average FC concentrations and
annual average salinity values for the water quality sampling stations,
and total annual precipitation for the terrestrial weather stations.
Fecal indicator bacteria concentrations are highly variable in space and
time, with samples collected consecutively over the span of a few
minutes sometimes varying by an order of magnitude over a few minutes
(Boehm, 2007). Accordingly, the annual arithmetic mean was selected to
represent the central tendency in long-term FC data, with the annual
time step selected to average out the effects of seasonal variation.
Similarly, using total annual precipitation informed us of the overall
freshwater load over the years and allowed us to explore long-term
drivers of baseflow FC concentrations. MK testing and Sen Slope
Estimation analysis have been used in numerous environmental,
hydrological, and water quality studies (Hirsch et al., 1982; Cailas et
al., 1986; Hipel et al., 1988; Zetterqvist, 1991; Burn et al., 2002;
Meals et al., 2011; Mustapha, 2013) due to their robustness against
non-normal data with missing values. Statistical analyses and mapping
were conducted in R with the `trend' package version 1.1.4 (Pohlert,
2020) and the `sf' package version 0.9 (Pebesma et al., 2022).

\emph{2.3.1 Mann-Kendall Trend Test~}

The MK trend test is a nonparametric test for monotonicity of trends in
time series data (Mann, 1945; Kendall, 1975). By ranking the time series
observations and measuring the later observations (\emph{j} ) against
earlier observations (\emph{i} ), MK testing allows us to understand the
monotonicity of an upward or downward trend in a time series. A
perfectly monotonic trend consistently increases or decreases. For
example, a perfectly increasing monotonic function is never decreasing
at any point along the function. Using pairwise comparison of ranked
values from all data points, the test statistic (\emph{S} ) is
calculated through either adding or subtracting 1 for every value that
is larger or smaller than the later value (Equation 1). This results in
a test statistic (\emph{S} ) that characterizes the directionality and
monotonicity of a trend in a given time series. \emph{S} is then used to
calculate the \selectlanguage{greek}τ \selectlanguage{english}test statistic (Equation 2), which is a measure of
correlation that ranges from -1 to +1 with the sign indicating the
direction of the value's change over time. This test determines whether
there is a significant, monotonic trend in a value over time in either a
positive or negative direction.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\(S=\sum_{i=1}^{n-1}{\sum_{j=i+1}^{n}{\text{sign}\left(y_{j}-y_{i}\right)}}\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ sign}\left(y_{j}-y_{i}\right)=\left\{\begin{matrix}1;\mathrm{\text{if\ }}y_{j}>y_{i}\\
0;\mathrm{\text{if\ }}y_{j}=y_{i}\\
-1;\mathrm{\text{if\ }}y_{j}<y_{i}\\
\end{matrix}\right.\ \) & \emph{(Equation 1)}\tabularnewline
\midrule
\endhead
\(\tau=\frac{S}{n\left(n-1\right)/2}\) & \emph{(Equation 2)}\tabularnewline
\bottomrule
\end{longtable}

In Equations 1 and 2, \emph{n} is the number of observations in the time
series, \emph{i} is the earlier value in the time series
sequence,\emph{j} is later value in the time series sequence, and
\emph{y}represents the measurements at times \emph{i} or \emph{j} .

\emph{2.3.2 Sen Slope Estimator}

While the \selectlanguage{greek}τ \selectlanguage{english}value from the MK test denotes direction and how well a time
series fits a monotonic model, the Sen slope estimator (\selectlanguage{greek}β\selectlanguage{english}) determines
the rate of change for the resulting MK trends. The resulting \selectlanguage{greek}β \selectlanguage{english}is based
on a median of all possible slopes calculated (Equation 3).\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\(\beta=median\left(\frac{y_{j}-y_{i}}{x_{j}-x_{i}}\right)\) & \emph{(Equation 3)}\tabularnewline
\bottomrule
\end{longtable}

The Mann-Kendall trend test and Sen slope estimators provided us with FC
\selectlanguage{greek}τ \selectlanguage{english}(\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC}), FC \selectlanguage{greek}β \selectlanguage{english}(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC}), salinity \selectlanguage{greek}τ
\selectlanguage{english}(\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Sal}), salinity \selectlanguage{greek}β \selectlanguage{english}(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal}), precipitation
\selectlanguage{greek}τ \selectlanguage{english}(\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Precip}), and precipitation \selectlanguage{greek}β
\selectlanguage{english}(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip}) for each water quality sampling station.

\emph{2.3.3 Correlations}

Pearson's correlation coefficients were calculated to evaluate the
relationships between the FC, salinity, and precipitation trends as well
as the LULC class percent change for the consolidated classes and
distance to shore.

\textbf{3. RESULTS}

\textbf{3.1 Coastwide Trends}

The results of the MK testing and Sen slope estimation of FC
concentrations were mapped alongside the developed land use change for
each coastal watershed (Figure 4). Results for all stations including \selectlanguage{greek}τ
\selectlanguage{english}values, p-values, and \selectlanguage{greek}β \selectlanguage{english}slopes are documented in Table S1. The spatial
distribution of \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} varied along the coast with defined
areas of similar \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values. From the most southern
coastal area, we observe higher, positive \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values for
stations down estuary of areas with very high proportions of developed
change. Within the south-central portion of the coast, we can see more
negative \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values. Farther north, in the area
characterized by the large, Albemarle-Pamlico sound which spans SGAs F,
G, H, and I, there is a mixture of lower positive and lower negative
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values. The developed land use change is fairly
uniform along a majority of the state's coastline, with higher
proportions of developed land change being located in the watersheds
associated with SGA C and E and the most intense increase in proportion
of developed land being in central watersheds associated with SGA B.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} . Map of the 20-year FC trends for all 466 water
quality stations (points) along North Carolina's coast alongside the
developed change percentage of the HUC12 watershed areas (purple fill).
The points representing the water quality station FC trends are colored
by the \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} value with the red and orange colors
illustrating the positive trends in FC concentrations while the green
and blue colors indicate a negative trend in FC concentrations. These
points are also sized by the magnitude of their
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC}values (i.e., larger points represent steeper slopes
in the FC trends and smaller points represent more gently sloping FC
trends). The light purple watershed areas represent smaller proportions
of developed land increase whereas dark purple areas represent a more
intense increase in developed land proportions from 2001 to 2019; white,
unfilled areas between watersheds correspond to watersheds that do not
directly connect to a waterway. This map was created in R version 4.1.0
with `sf' version 0.9 package.

The Pearson correlation coefficients for the water quality and
environmental trends along the full coastal area are summarized in Table
1. These correlations include the relationships between
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC}, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC}, \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Sal},
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal}, \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Precip}, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip},
developed land percentage, barren land percentage, wetlands land
percentage, vegetated land percentage, cultivated land percentage, and
distance to shore. We observed a clear negative relationship between
both \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} \& \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Sal}\& \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} (r values from -0.281 to
-0.304). There was a weak positive relationship between
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Precip} \&
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip}. The positive relationship between
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{Sal} \& \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} and all land use
classifications with wetlands had the strongest positive coefficients (r
= 0.393 and r = 0.401, respectively). Distance to shore was weakly
correlated with overall FC trends.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
& Wetlands Change & Cultivated Change & Vegetated Change & Barren Change
& Developed Change & Shore Distance & Salinity \selectlanguage{greek}β & Salinity \selectlanguage{greek}τ &
Precipitation \selectlanguage{greek}β & Precipitation \selectlanguage{greek}τ & FC \selectlanguage{greek}β & FC \selectlanguage{greek}τ\selectlanguage{english}\tabularnewline
\midrule
\endhead
FC \selectlanguage{greek}τ & 0.006 & -0.029 & 0.087 & 0.062 & 0.127 & 0.057 & -0.304 & -0.304
& 0.095 & 0.108 & 0.751 & 1.000\tabularnewline
FC \selectlanguage{greek}β & -0.072 & -0.047 & 0.017 & 0.021 & 0.044 & 0.013 & -0.281 & -0.260
& -0.023 & -0.012 & 1.000 &\tabularnewline
Precipitation \selectlanguage{greek}τ & -0.192 & -0.070 & -0.023 & 0.020 & 0.067 & -0.033 &
-0.304 & -0.371 & 0.920 & 1.000 & &\tabularnewline
Precipitation \selectlanguage{greek}β & -0.315 & -0.149 & -0.048 & -0.024 & 0.004 & -0.103 &
-0.368 & -0.397 & 1.000 & & &\tabularnewline
Salinity \selectlanguage{greek}τ & 0.393 & 0.174 & 0.100 & 0.119 & 0.111 & 0.144 & 0.908 &
1.000 & & & &\tabularnewline
Salinity \selectlanguage{greek}β & 0.401 & 0.189 & 0.078 & 0.120 & 0.116 & 0.117 & 1.000 & & &
& &\tabularnewline
Shore Distance & 0.187 & 0.017 & 0.174 & 0.087 & 0.318 & 1.000 & & & & &
&\tabularnewline
Developed Change & 0.282 & -0.005 & 0.193 & 0.099 & 1.000 & & & & & &
&\tabularnewline
Barren Change & 0.299 & -0.070 & 0.179 & 1.000 & & & & & & &
&\tabularnewline
Vegetated Change & 0.198 & -0.004 & 1.000 & & & & & & & &
&\tabularnewline
Cultivated Change & -0.082 & 1.000 & & & & & & & & & &\tabularnewline
Wetlands Change & 1.000 & & & & & & & & & & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1.} Pearson's correlation matrix representing
relationships between water quality trends (FC concentration and
salinity) and environmental parameters (LULC change, precipitation,
shore distance) for all stations along North Carolina's coast included
in the MK trend testing.

When aggregated to annual averages, the NCDWR salinity data is
insufficiently short (n = {[}10, 14{]} for each station) and limits our
ability to apply trend testing. However, the NCDWR salinity time series
(Figure 5) largely follow the same trends as the NCDMF salinity data;
exceptions include sites in growing areas E and D, where several NCDWR
stations were located in the upstream, fresher portions of the
estuaries. While we generally selected stations in locations similar to
NCDMF stations, these are presented due to lack of stations in the more
brackish and marine waters within these areas. Given that the NCDWR data
is collected through an unbiased monitoring program, the agreement
between the NCDWR and NCDMF salinity observations indicates the NCDMF
observations may not be severely affected by sampling bias.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Time series of NCDMF salinity results (grey lines)
with the NCDWR salinity results (red lines) from the chosen
representative stations along North Carolina's coast. Line graphs are
grouped by their SGA area. Locations of NCDWR stations labeled as 1-4
are annotated in Figure 2.

\textbf{3.2 SGA Specific Trends}

To further understand spatial relationships within the results, the data
for stations within SGA name groups were pooled. There are 8 total SGA
groups (A, B, C, D, E, F, G, H) that were used to aggregate the station
results.~Correlations between all considered variables were calculated
for each SGA (Table 2). From the correlations describing FC trends with
LULC changes, we saw strong negative correlations between FC trends and
developed land use change in SGA letters C and A despite the weak
positive correlation represented by the coefficient calculated from all
data (i.e., full coast). Negative correlations between FC trends and
wetlands land use change were also observed, being especially strong in
southern SGAs (A, B, C, D). \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip} and
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} had a weak positive relationship for most SGAs, with
the exception of SGA C. The correlation between
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip}and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} was broken down further
for SGA C (Figure S1), which revealed a strong correlation attributed to
negative \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} values occurring in conjunction with high
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip} values. Shore distance had a fairly positive to
fairly negative relationship with \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} moving from the
southern SGAs (A, B, C, D) to the northern SGAs (F, G, H). The
relationship between shore distance and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} is shown in
Figure S2. Salinity trends were negatively correlated with
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} along each SGA with the exception of SGA E (Table
2). Due to the ecological importance of salinity when it comes to water
quality and predicting FC concentrations (Souedan et al., 2021; Florini
et al., 2020; Liu et al., 2012), our understanding of salinity
integrating the effects of both land use and precipitation, and the
consistent directionality of the correlation coefficients, we analyzed
this SGA-specific relationship further (Figure 6).\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
& & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and LULC Classes & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and LULC
Classes & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and LULC Classes & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and
LULC Classes & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and LULC Classes & &
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and Environmental Parameters & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC}
and Environmental Parameters & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and Environmental
Parameters & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and Environmental Parameters
&\tabularnewline
\midrule
\endhead
& Barren & Barren & Cultivated & Developed & Vegetated & Wetlands &
Wetlands & & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{sal} & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip} & Shore
Distance & Shore Distance\tabularnewline
H & 0.311 & 0.311 & -0.043 & 0.278 & 0.255 & 0.158 & 0.158 & & -0.04 &
-0.032 & -0.105 & -0.105\tabularnewline
G & -0.141 & -0.141 & 0.014 & -0.006 & -0.066 & 0.234 & 0.234 & & -0.147
& -0.345 & -0.094 & -0.094\tabularnewline
F & 0.188 & 0.188 & -0.056 & -0.219 & -0.105 & -0.178 & -0.178 & &
-0.363 & -0.031 & -0.099 & -0.099\tabularnewline
E & 0.003 & 0.003 & -0.207 & 0.052 & 0.015 & 0.123 & 0.123 & & 0.161 &
-0.151 & 0.045 & 0.045\tabularnewline
D & 0.254 & 0.254 & -0.324 & -0.180 & -0.500 & -0.267 & -0.267 & &
-0.088 & NA & 0.244 & 0.244\tabularnewline
C & 0.040 & 0.040 & 0.252 & -0.497 & -0.006 & -0.514 & -0.514 & & -0.561
& -0.827 & 0.335 & 0.335\tabularnewline
B & -0.042 & -0.042 & 0.034 & 0.128 & 0.167 & -0.253 & -0.253 & & -0.055
& 0.094 & 0.099 & 0.099\tabularnewline
A & -0.223 & -0.223 & 0.394 & -0.355 & 0.31 & -0.386 & -0.386 & & -0.510
& 0.071 & 0.417 & 0.417\tabularnewline
Full Coast & 0.021 & 0.021 & -0.047 & 0.044 & 0.017 & -0.072 & -0.072 &
& -0.281 & -0.023 & 0.013 & 0.013\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Pearson's correlation coefficients broken down by SGA
letter (rows) for the relationships between \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and LULC
class change, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal}, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Precip}, and shore
distance. SGAs are listed in geographic order across the rows from H to
A, with H being the northernmost SGA and A being the southernmost.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6.} Leftmost scatterplot (a) illustrates the
relationships between \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} for
each station colored by the station's associated SGA letter. The top
right scatterplots (b) illustrate \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} for each station within the SGA. The lower right
histograms (c) show the distributions of \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values
broken down by SGA letter. Both (b) and (c) further demonstrate the
spatial variation in salinity trends and relationships between
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} across the coast.

There were weakly correlated, somewhat grouped relationships between
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} along the full coast
(Figure 6a), with strongly negative correlations in SGA B and somewhat
in C, weakly correlated relationships in SGAs D, F, G, and H, and a
positive correlation in SGA E (Figure 6b). When broken out by SGA
(Figure 6c), the \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} distribution varied from lower
negative values with higher spread in the southern SGAs A, B, and C, to
higher positive \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values with more narrow spread in
the central SGAs D, E, and F. SGAs G and H had lower
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values than the more central areas, but higher than
the more southern areas (Figure 6c).

\textbf{3.3 Representative Focal Areas}

To explain some of the variation seen within the North Carolina coastal
system, we detailed the trends seen under locally specific management
and environmental conditions in SGAs B, E, and G (Figure 7).~SGAs B, E,
and G were selected because they captured different modes of estuarine
and management variation, described in more detail in the following
sections. More specifically, they represent different classes of
estuarine drainage areas as defined by Engle et al. (2007), different
shellfish lease distribution, and different levels of developed land
change within the surrounding watersheds. Estuary drainage area
classifications were originally created by NOAA's Coastal Assessment
Framework to incorporate tidal influence into watershed delineation
(NOAA, 2003). Engle et al. (2007) uses these areas to reclassify areas
through a system that includes area, depth, volume, freshwater flow, and
salinity to define an estuary type. This results in a coded class system
ranging from 1 to 9 (Engle et al., 2007). By using a variety of
different estuarine drainage area classes, we are effectively capturing
a variety of physical and hydrological conditions, which enriches the
interpretation of our results.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7.} Full map of coastal North Carolina depicting the
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values sized by the magnitude of their
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} alongside HUC 12 land areas and their respective
percent change in developed land use. SGAs B (a), E (b), and G (c) are
used as focal areas to understand how management planning, increase in
developed land, and estuary type affect the variation in FC trends. This
map was created in R version 4.1.0 with `sf' version 0.9 package.

\emph{Focal Area B}

SGA B (Figure 7c) is associated with waters at the mouth of the major
river cutting through this region, the Cape Fear River, and lagoonal
estuaries. SGA B is a class 6 estuary (Engle et al. 2007) characterized
by large area, moderate volume, high freshwater flow, and moderate depth
and salinity. This area contains higher rainfall thresholds ranging from
2 to 4 inches indicating that coastal FC concentrations within
conditionally approved or approved portions of these growing areas do
not respond as intensely to rainfall as compared to other areas of the
coast with lower rainfall thresholds. Prior studies have reported poor
water quality in SGA B (Alford et al., 2016; NCDEQ, 2022), likely
correlated with a high increase in the developed land up-river. Based on
the changes in land use observed over the study period, watersheds
adjacent to SGA B were associated with increased urbanization along the
NC coast (Figure 4). SGA B is also characterized by consistently
negative \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values indicating decreasing salinity
values for the samples that have been taken over the past 20 years
(Figure 6c).

FC trends support our prior understanding regarding declining water
quality in this region (Figure 7c), as demonstrated by high
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} values across most of the
study sites. However, some spatial variation in FC trends were observed.
The more southern areas within this SGA, associated with the mouth of
the Cape Fear River, have higher \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values than the
lagoonal areas located in the northern portion of the region, which are
not generally directly affected by the Cape Fear River. The southern
Cape Fear River can experience diverted flow through a man-made waterway
under some high tide conditions, resulting in river flows discharging
directly into the lagoonal estuaries. This tidal overflow effect might
explain the similar trends in \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} from the southern Cape
Fear River area to the southern lagoonal estuary area despite being very
separate geographies. While there are negative \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC}values
in the lagoonal areas, the \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} is higher in the areas
where \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} is positive, meaning that the FC
concentrations towards the mouth of the Cape Fear River are increasing
at a faster rate than the decreasing, lagoonal FC concentrations.

\emph{Focal Area E}SGA E (Figure 7b) represents a trunk and tributary
estuarine system surrounded by moderate urban development in the past 20
years. SGA E contains systems classified as class 2 estuaries (Engle et
al., 2007) indicating moderate area with low volume, moderate freshwater
flow, and high salinity. SGA E supports a large number of up-estuary
shellfish leases within the tributary systems. This area responds
strongly to rainfall events in terms of FC load, as demonstrated by the
low rainfall thresholds (i.e., 1 inch to 4 inches), with the lowest
rainfall thresholds located up-estuary. This watershed, similar to SGA
B, experienced a moderate increase in developed land use within the past
20 years (Figure 4). However, the \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values in this
region were generally negative with a few very positive
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values within the river systems, suggesting an
improvement in baseflow water quality. This area has also shown more
positive \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values (Figure 6c), indicating an increase
in salinity measured for the samples taken over the past 20 years.

\emph{Focal Area G}

SGA G (Figure 7c) contains the Pamlico and Pungo Rivers. SGA G is a
class 4 estuary (Engle et al., 2007), which is characterized by moderate
area, moderate depth, low volume, and high freshwater flow. This area is
associated with the back-barrier Albemarle-Pamlico Estuary to the east,
which is a class 9 estuary (Engle et al., 2007) indicating very large
area, deep bathymetry, high volume, and high salinity environment. This
area is also characterized by low increase in surrounding development
and relatively fewer shellfish leases, demonstrating that this system is
relatively unimpacted by human activity as compared to focal areas B and
E. Accordingly, rainfall thresholds within SGA G are all at the highest
limit of 4 inches, indicating FC concentrations in these waters are not
highly sensitive to precipitation and stormwater runoff. There was a
variety of negative and positive \selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC} values in this area
with a majority of the stations exhibiting negative
\selectlanguage{greek}τ\selectlanguage{english}\textsubscript{FC}values. The \selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values in this area
are also variable, exhibiting a range of both positive and negative
trends.

\textbf{4. DISCUSSION}

We found that shellfish sanitation data collected routinely through a
systematic random sampling strategy as defined by the National Shellfish
Sanitation Program (NSSP) could cautiously be used for long-term water
quality trend analysis. By comparing salinity measurements collected by
the NCDWR, which maintains an unbiased monitoring program, and NCDMF,
which only samples when shellfish waters are open for harvest, we were
able to assess whether the sampling constraints imposed on the NCDMF
measurements influenced the trend testing results. We found that the
NCDMF and NCDWR salinity time series behaved similarly across all SGAs
(Figure 5). However, the NCDWR data only spanned 10 years while the
NCDMF data spanned 20, and the difference in time series length limits
our ability to fully corroborate the NCDMF data using NCDWR
observations. Additionally, though not strongly evident in the salinity
data analyzed here, the risk for sampling bias to affect routine
monitoring data collected by shellfish sanitation programs exists and
should always be considered when analyzing their measurements.

We expect sampling bias risk to be greatest in conditionally approved
waters with low rainfall thresholds (i.e., 1 to 2 inches), such as SGA
E. In contrast, in areas with relatively high rainfall thresholds (e.g.,
4 inches), routine FC samples can typically be collected at any time
during the year since these waters remain open unless an exceptional
event, such as a hurricane or major frontal storm, has occurred. Because
waters with high rainfall thresholds largely remain open, the six annual
samples are collected under a wider range of environmental conditions,
and there is less risk of sampling bias potentially affecting FC trends
quantified from the routine monitoring data. For example, SGA G
represents an area with high rainfall thresholds (4 inches). These high
rainfall thresholds create less restrictive conditions for routine
sampling, effectively increasing the variety of conditions captured in
the sampling. Accordingly, FC trends determined from shellfish
sanitation data from these stations are likely representative of the
true improvement or degradation in water quality observed in the system,
which also helps to explain why the FC trend results we reported
corroborate findings from other studies that have evaluated water
quality in this region. In contrast, areas that are more restricted in
the time and conditions that routine sampling is able to occur (i.e.,
areas that are conditionally managed with low rainfall thresholds), such
as SGA E, are associated with routine observations that have higher risk
of being biased, and there is increased complexity in terms of
interpreting these data to infer general water quality trends. Low
rainfall thresholds dictate higher rates of closures for even mild
meteorological events, which effectively restricts the open times
available for routine sampling. However, we demonstrated that the use of
an external water quality dataset, in this case for salinity, can be
used to assess how sampling bias may have affected measurements
collected by shellfish sanitation programs.

Nonpoint source runoff is considered a major contributor to FC loads in
estuaries located near developed landscapes (Mallin et al., 2000;
Coulliette et al., 2009; Kirby-Smith et al., 2006; Campos et al., 2013).
Therefore, the increasing trends we documented in FC concentrations in
SGAs B, E, and H align with the known relationship between FC and
development. Specifically, the positive correlative relationship between
change in developed land cover across a watershed and increasing FC
trends was seen in SGAs B, E, and H, while A, C, D, F, and G were
associated with negative correlations. Relationships between developed
land use change and FC trends could potentially be clarified further by
using population density change over watersheds, stormwater management,
or differentiating impervious surfaces (Mallin et al., 2000; Carle et
al., 2005; Cahoon et al., 2016; Freeman et al., 2019).

The negative correlation between FC and salinity along all SGAs was
consistent with established water quality relationships except for a few
contradictory results. The inverse relationship between FC and salinity
could be a result of the coupled effect of increased freshwater input
that comes with increased precipitation (Campos et al., 2013; Coulliette
et al., 2009). It is known that FC concentrations increase following
runoff after rainfall events, especially in more developed areas (Mallin
et al., 2000; Carle et al., 2005; Cahoon et al., 2016; Freeman et al.,
2019). These same rainfall events that increase the FC concentrations
also decrease salinity, which is illustrated in the inverse
relationships reported in this study across each SGA, with the exception
of SGA E (Table 2, Figure 6). However, the inverse relationship between
FC concentration and salinity trends was often noisy (Figure 6b), with
the correlation coefficient between FC concentration and salinity trends
being in the range of {[}-0.147, 0.161{]} for 5 out of 8 SGAs (Table 2).
In the case of SGA E, where a positive correlation between salinity and
FC trends was observed, the correlation appears to have been influenced
by outlying values (Figure 6b), particularly since most of the
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Sal} values reflected increases in salinity (Figure 6c)
while the \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} values showed there were FC concentrations
decreases across most sampling locations (Figure 6a, 6b).

The noisy relationships between FC concentration and salinity trends in
our results could be explained by our dataset not capturing short-term
FC concentration increases following storm events and instead capturing
FC during baseflow conditions. Because the data analyzed in this study
was produced from routine systematic random sampling, which is collected
when waters are open for harvest to capture baseline fecal coliform
loading, the observations will not capture changes in storm-driven FC
concentrations. Instead, the measurements may reveal if there is chronic
loading in an area (e.g., due to continuously failing septic systems or
poorly performing wastewater treatment plants). Therefore, the trends
from this analysis are representative of baseflow conditions.
Accordingly, had the routine sampling data captured post-storm
conditions, we expect stronger correlations between FC and salinity
trends would have been observed. Instead, we believe that factors such
as increases in tidal flushing (e.g., due to inlet dredging) and changes
in baseflow FC loads in these systems play a larger role in explaining
the negative relationship between FC and salinity than changes in rain
and runoff.

In addition to providing insights on long-term water quality trends,
shellfish sanitation data can also be used to assess the efficacy of
current management practices. For example, a conditionally managed
waterbody with low rainfall thresholds that is still showing a trend
towards increasing FC concentrations could indicate a decline in water
quality that has not been met with intense enough action by the current
management plan. As a result, trends in fecal coliform observations
could be used as an ``early warning system'' to help pinpoint areas
where more intense management measures need to be taken. For example,
the way in which these data could be used as an ``early warning system''
is demonstrated by focal SGA B (Figure 7c), where the mouth of the Cape
Fear River likely shows increasing FC concentrations due to degradation
of water quality that may need to be met with new management actions.

\textbf{5. CONCLUSIONS}

In this study, we assessed the feasibility of utilizing estuarine
monitoring data from a representative regulatory program (i.e.,
shellfish sanitation) to infer long term water quality trends. We used
these data to look specifically at the spatial and temporal trends in FC
concentrations and identified possible management and environmental
drivers of these trends. Our study system, coastal North Carolina,
exhibited a variety of trends in both the 20-year FC concentrations and
the considered environmental drivers. While the resulting water quality
trends and their relationships with environmental factors were complex,
there were emergent patterns that we found to offer key insights. In
particular, we concluded that shellfish sanitation data collected
routinely through a systematic random sampling strategy as defined by
the National Shellfish Sanitation Program (NSSP) could be used for
long-term water quality trend analysis, and to fill extensive gaps in
existing coastal water quality monitoring programs.

Although our results demonstrated opportunities of using shellfish
sanitation data for inferring long-term water quality trends, our study
was limited by several factors. Firstly, we did not account for tidal
circulation due to the major modeling effort that would be required to
include tidal circulation and flow patterns at this spatial and temporal
scale. Future research should improve upon the methods presented here by
including factors that capture the marine flushing of an area such as
inlet maintenance or distance to the nearest intracoastal waterway.
Secondly, there was a lack of unbiased FC concentration datasets for
trend validation, and we relied on findings from prior published studies
to ``ground truth'' FC trends calculated from monitoring data. Regions
outside of our study area may not have access to the type of information
used to help diagnose the reliability of shellfish sanitation monitoring
data for water quality inference. As new monitoring programs are
introduced to track changes in marine systems, opportunities to pair
sites with existing shellfish sanitation program monitoring locations
could help to create data needed to characterize potential sampling bias
effects and increase the ability for long-term shellfish sanitation data
to be used for water quality analyses. Finally, because of variation in
sampling protocols across state programs, shellfish sanitation data are
nuanced and challenging to interpret. This study offers context and an
approach for confronting nuance in the data. However, directly engaging
with shellfish sanitation program managers is essential to accurately
interpreting trend results like those presented here, as local expertise
provides invaluable insight into the state and function of these
estuarine systems and their management.

\textbf{6. ACKNOWLEDGMENTS}

The authors have no conflicts of interest. This work was supported by
North Carolina Sea Grant R/20-SFA-4 and USDA National Institute of Food
and Agriculture (NIFA) Hatch project 1016068. The datasets analyzed in
this study are all publicly available, with sources summarized in the
methods section.

\textbf{REFERENCES}

Alford, J. B., K. G. Debbage, M. A. Mallin, and Z.-J. Liu. 2016. Surface
Water Quality and Landscape Gradients in the North Carolina Cape Fear
River Basin: The Key Role of Fecal Coliform. \emph{Southeastern
Geographer} 56. University of North Carolina Press: 428--453.

Azra, M. N., V. T. Okomoda, M. Tabatabaei, M. Hassan, and M.
Ikhwanuddin. 2021. The Contributions of Shellfish Aquaculture to Global
Food Security: Assessing Its Characteristics From a Future Food
Perspective. \emph{Frontiers in Marine Science} 8: 365.
https://doi.org/10.3389/fmars.2021.654897.

Beck, M. W., T. W. Jabusch, P. R. Trowbridge, and D. B. Senn. 2018. Four
decades of water quality change in the upper San Francisco
Estuary.\emph{Estuarine, Coastal and Shelf Science} 212: 11--22.
https://doi.org/10.1016/j.ecss.2018.06.021.

Boehm, A. B. 2007. Enterococci Concentrations in Diverse Coastal
Environments Exhibit Extreme Variability. \emph{Environmental Science \&
Technology} 41. American Chemical Society: 8227--8232.
https://doi.org/10.1021/es071807v.

Bourel, M., A. M. Segura, C. Crisci, G. L\selectlanguage{ngerman}ópez, L. Sampognaro, V. Vidal,
C. Kruk, C. Piccini, and G. Perera. 2021. Machine learning methods for
imbalanced data set for prediction of faecal contamination in beach
waters. \emph{Water Research} 202: 117450.
https://doi.org/10.1016/j.watres.2021.117450.

Bugica, K., B. Sterba-Boatwright, and M. S. Wetz. 2020. Water quality
trends in Texas estuaries. \emph{Marine Pollution Bulletin} 152: 110903.
https://doi.org/10.1016/j.marpolbul.2020.110903.

Burn, D. H., and M. A. Hag Elnur. 2002. Detection of hydrologic trends
and variability. \emph{Journal of Hydrology} 255: 107--122.
https://doi.org/10.1016/S0022-1694(01)00514-5.

Cahoon, L. B., J. C. Hales, E. S. Carey, S. Loucaides, K. R. Rowland,
and B. R. Toothman. 2016. Multiple modes of water quality impairment by
fecal contamination in a rapidly developing coastal area: southwest
Brunswick County, North Carolina. \emph{Environmental Monitoring and
Assessment} 188: 89. https://doi.org/10.1007/s10661-015-5081-6.

Cailas, M. D., G. Cavadias, and R. Gehr. 1986. Application of a
Nonparametric Approach for Monitoring and Detecting Trends in Water
Quality Data for the St. \emph{Water Quality Research Journal} 21:
153--167. https://doi.org/10.2166/wqrj.1986.012.

Campos, C. J. A., S. R. Kershaw, and R. J. Lee. 2013. Environmental
Influences on Faecal Indicator Organisms in Coastal Waters and Their
Accumulation in Bivalve Shellfish. \emph{Estuaries and Coasts} 36:
834--853. https://doi.org/10.1007/s12237-013-9599-y.

Carle, M. V., P. N. Halpin, and C. A. Stow. 2005. Patterns of Watershed
Urbanization and Impacts on Water Quality1. \emph{JAWRA Journal of the
American Water Resources Association} 41: 693--708.
https://doi.org/10.1111/j.1752-1688.2005.tb03764.x.

Chesapeake Bay Monitoring Program. 2022.
https://www.chesapeakebay.net/what/programs/monitoring. Accessed
February 9.

Chigbu, P., S. Gordon, and T. Strange. 2004. Influence of inter-annual
variations in climatic factors on fecal coliform levels in Mississippi
Sound. \emph{Water Research} 38: 4341--4352.
https://doi.org/10.1016/j.watres.2004.08.019.

Chigbu, P., S. Gordon, and P. B. Tchounwou. 2005. The Seasonality of
Fecal Coliform Bacteria Pollution and its Influence on Closures of
Shellfish Harvesting Areas in Mississippi Sound. \emph{International
Journal of Environmental Research and Public Health} 2. Molecular
Diversity Preservation International: 362--373.
https://doi.org/10.3390/ijerph2005020023.

Cho, K. H., Y. A. Pachepsky, D. M. Oliver, R. W. Muirhead, Y. Park, R.
S. Quilliam, and D. R. Shelton. 2016. Modeling fate and transport of
fecally-derived microorganisms at the watershed scale: State of the
science and future opportunities. \emph{Water Research} 100: 38--56.
https://doi.org/10.1016/j.watres.2016.04.064.

Cloern, J. E., P. C. Abreu, J. Carstensen, L. Chauvaud, R. Elmgren, J.
Grall, H. Greening, et al. 2016. Human activities and climate
variability drive fast-paced change across the world's
estuarine--coastal ecosystems. \emph{Global Change Biology} 22:
513--529. https://doi.org/10.1111/gcb.13059.

Coulliette, A. D., E. S. Money, M. L. Serre, and R. T. Noble. 2009.
Space/Time Analysis of Fecal Pollution and Rainfall in an Eastern North
Carolina Estuary. \emph{Environmental Science \& Technology} 43.
American Chemical Society: 3728--3735.
https://doi.org/10.1021/es803183f.

Engle, V. D., J. C. Kurtz, L. M. Smith, C. Chancy, and P. Bourgeois.
2007. A classification of U.S. estuaries based on physical and
hydrologic attributes. \emph{Environmental Monitoring and Assessment} .
https://doi.org/10.1007/s10661-006-9372-9.

Florini, S., E. Shahsavari, T. Ngo, A. Aburto-Medina, D. J. Smith, and
A. S. Ball. 2020. Factors Influencing the Concentration of Fecal
Coliforms in Oysters in the River Blackwater Estuary, UK.
\emph{Water}12. Multidisciplinary Digital Publishing Institute: 1086.
https://doi.org/10.3390/w12041086.

Freeman, L. A., D. R. Corbett, A. M. Fitzgerald, D. A. Lemley, A. Quigg,
and C. N. Steppe. 2019. Impacts of Urbanization and Development on
Estuarine Ecosystems and Water Quality. \emph{Estuaries and Coasts} 42:
1821--1838. https://doi.org/10.1007/s12237-019-00597-z.

Fries, J. S., G. W. Characklis, and R. T. Noble. 2008. Sediment--water
exchange of Vibrio sp. and fecal indicator bacteria: Implications for
persistence and transport in the Neuse River Estuary, North Carolina,
USA. \emph{Water Research} 42: 941--950.
https://doi.org/10.1016/j.watres.2007.09.006.

Harding, L. W., M. E. Mallonee, E. S. Perry, W. D. Miller, J. E. Adolf,
C. L. Gallegos, and H. W. Paerl. 2019. Long-term trends, current status,
and transitions of water quality in Chesapeake Bay. \emph{Scientific
Reports} 9: 6709. https://doi.org/10.1038/s41598-019-43036-6.

Hipel, K. W., A. J. McLeod, and R. R. Weller. 1988. Data Analysis of
Water Quality Time Series in Lake Erie1. \emph{JAWRA Journal of the
American Water Resources Association} 24: 533--544.
https://doi.org/10.1111/j.1752-1688.1988.tb00903.x.

Hirsch, R. M., J. R. Slack, and R. A. Smith. 1982. Techniques of trend
analysis for monthly water quality data. \emph{Water Resources
Research}18: 107--121. https://doi.org/10.1029/WR018i001p00107.

Kemp, W. M., W. R. Boynton, J. E. Adolf, D. F. Boesch, W. C. Boicourt,
G. Brush, J. C. Cornwell, et al. 2005. Eutrophication of Chesapeake Bay:
historical trends and ecological interactions. \emph{Marine Ecology
Progress Series} 303: 1--29. https://doi.org/10.3354/meps303001.

Kendall, M. G. 1975. \emph{Rank correlation methods} . 4th ed. London:
Griffin.

Kirby-Smith, W. w., and N. m. White. 2006. Bacterial contamination
associated with estuarine shoreline development. \emph{Journal of
Applied Microbiology} 100: 648--657.
https://doi.org/10.1111/j.1365-2672.2005.02797.x.

Korajkic, A., P. Wanjugi, L. Brooks, Y. Cao, and V. J. Harwood. 2019.
Persistence and Decay of Fecal Microbiota in Aquatic
Habitats.\emph{Microbiology and Molecular Biology Reviews} 83. American
Society for Microbiology: e00005-19.
https://doi.org/10.1128/MMBR.00005-19.

Leight, A. K., R. Hood, R. Wood, and K. Brohawn. 2016. Climate
relationships to fecal bacterial densities in Maryland shellfish harvest
waters. \emph{Water Research} 89: 270--281.
https://doi.org/10.1016/j.watres.2015.11.055.

Leight, A., H. Slacum, E. Wirth, and M. Fulton. 2011. An assessment of
benthic condition in several small watersheds of the Chesapeake Bay,
USA. \emph{Environmental monitoring and assessment} 176: 483--500.
https://doi.org/10.1007/s10661-010-1599-9.

Liu, W.-C., and W.-C. Huang. 2012. Modeling the transport and
distribution of fecal coliform in a tidal estuary. \emph{Science of The
Total Environment} 431: 1--8.
https://doi.org/10.1016/j.scitotenv.2012.05.016.

Mallin, M. A., K. E. Williams, E. C. Esham, and R. P. Lowe. 2000. Effect
of Human Development on Bacteriological Water Quality in Coastal
Watersheds. \emph{Ecological Applications} 10: 1047--1056.
https://doi.org/10.1890/1051-0761(2000)010{[}1047:EOHDOB{]}2.0.CO;2.

Mann, H. B. 1945. Nonparametric Tests Against Trend.
\emph{Econometrica}13. {[}Wiley, Econometric Society{]}: 245--259.
https://doi.org/10.2307/1907187.

Meals, D., J. Spooner, S. Dressing, and J. Harcum. 2011. Statistical
Analysis for Monotonic Trends. EPA.

Mustapha, A. 2013. Detecting Surface Water Quality Trends Using
Mann-Kendall Tests and Sen's Slope Estimates.

National Estuarine Research Reserve System. 2022.
https://coast.noaa.gov/nerrs/about/. Accessed February 19, 2022.

NC Division of Coastal Management. 2007. Estuarine Shoreline Mapping
Project. Accessed February 19, 2022.

NCDEQ. 2022. 303(d).
https://edocs.deq.nc.gov/WaterResources/DocView.aspx?dbid=0\&id=2161668\&cr=1.
Accessed February 8, 2022.

NOAA Tides \& Currents. 2022. Center for Operational Oceanographic
Products and Services (CO-OPS).
https://tidesandcurrents.noaa.gov/about.html. Accessed April 30, 2022.

NSSP. 2019. National Shellfish Sanitation Program (NSSP) Guide for the
Control of Molluscan Shellfish: 2019 Revision. Accessed February 8,
2022.

Park, Y., M. Kim, Y. Pachepsky, S.-H. Choi, J.-G. Cho, J. Jeon, and K.
H. Cho. 2018. Development of a Nowcasting System Using Machine Learning
Approaches to Predict Fecal Contamination Levels at Recreational Beaches
in Korea. \emph{Journal of Environmental Quality} 47: 1094--1102.
https://doi.org/10.2134/jeq2017.11.0425.

Pebesma, E., R. Bivand, E. Racine, M. Sumner, I. Cook, T. Keitt, R.
Lovelace, et al. 2022. \emph{sf: Simple Features for R} (version 1.0-6).

Pohlert, T. 2020. \emph{trend: Non-Parametric Trend Tests and
Change-Point Detection} (version 1.1.4).

SECOORA, n.d. SECOORA Data Portal. 2022. https://portal.secoora.org/
{[}Accessed 1 March 2022{]}.

\selectlanguage{polish}Š\selectlanguage{english}oli\selectlanguage{polish}ć, \selectlanguage{english}M., and N. Krstulovi\selectlanguage{polish}ć. \selectlanguage{english}1992. Separate and combined effects of
solar radiation, temperature, salinity, and pH on the survival of faecal
coliforms in seawater. \emph{Marine Pollution Bulletin} 24: 411--416.
https://doi.org/10.1016/0025-326X(92)90503-X.

Souedian, J., A. Warren, M. Pearson, and E. Montie. 2021. A changing
estuary: Understanding historical patterns in salinity and fecal
coliform levels in the May River, SC. \emph{Marine Pollution
Bulletin}168. Pergamon: 112384.
https://doi.org/10.1016/j.marpolbul.2021.112384.

Stanley, D. 1992. Historical trends, water quality and fisheries,
Albemarle-Pamlico Sounds : with emphasis on the Pamlico River
Estuary.\emph{Sea Grant Publication} .

US EPA. 2022. Environmental Monitoring \& Assessment Program (EMAP)
\textbar{} US EPA.
https://archive.epa.gov/emap/archive-emap/web/html/index.html. Accessed
April 30.

USGS. 2022. National Water Information System: Web Interface. \emph{USGS
Current Water Data for the Nation} . https://waterdata.usgs.gov/nwis/rt.
Accessed February 19'' https://waterdata.usgs.gov/nwis/rt. Accessed
February 19.

Water Quality Portal. 2021. Washington (DC): National Water Quality
Monitoring Council, United States Geological Survey
(USGS).\emph{Environmental Protection Agency (EPA)} .
https://doi.org/10.5066/P9QRKUVJ'' https://doi.org/10.5066/P9QRKUVJ.

Weiskel, P. K., B. L. Howes, and G. R. Heufelder. 1996. Coliform
Contamination of a Coastal Embayment:~ Sources and Transport
Pathways.\emph{Environmental Science \& Technology} 30. American
Chemical Society: 1872--1881. https://doi.org/10.1021/es950466v.

Zetterqvist, L. 1991. Statistical estimation and interpretation of
trends in water quality time series. \emph{Water Resources Research} 27:
1637--1648. https://doi.org/10.1029/91WR00478.

Zhang, Q., R. R. Murphy, R. Tian, M. K. Forsyth, E. M. Trentacoste, J.
Keisman, and P. J. Tango. 2018. Chesapeake Bay's water quality condition
has been recovering: Insights from a multimetric indicator assessment of
thirty years of tidal monitoring data. \emph{Science of The Total
Environment} 637--638: 1617--1625.
\url{https://doi.org/10.1016/j.scitotenv.2018.05.025}.

\par\null

\textbf{Supplementary Material}

\textbf{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figs1/figs1}
\caption{{This is a caption
{\label{492027}}%
}}
\end{center}
\end{figure}

\textbf{Figure S1.} Leftmost scatterplot (a) illustrates the
relationships between \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{precipitation} for each station colored by the station's
associated SGA letter. The top right scatterplots (b) illustrate
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and \selectlanguage{greek}β\selectlanguage{english}\textsubscript{precipitation} for each station
within the SGA. The lower right histograms (c) show the distributions of
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{precipitation} values broken down by SGA letter. Both
(b) and (c) further demonstrate the spatial variation in precipitation
trends and relationships between \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{precipitation} across the coast.~{}

{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figs2/figs2}
\caption{{This is a caption
{\label{490838}}%
}}
\end{center}
\end{figure}

\textbf{Figure S2.} Leftmost scatterplot (a) illustrates the
relationships between \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and distance to shore in
meters for each station colored by the station's associated SGA letter.
The top right scatterplots (b) illustrate \selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and
distance to shore for each station within the SGA. The lower right
histograms (c) show the distributions of distance to shore values broken
down by SGA letter. Both (b) and (c) further demonstrate the spatial
variation in distance to shore and relationships between
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{FC} and distance to shore across the coast.~{}

\par\null

\textbf{Table S1.} Full results from the Mann-Kendall trend testing and
the Sen Slope estimation for each of the NCDMF water quality sampling
stations that have 20 years of continuous data between 1999 to 2021.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{station} & \textbf{SGA} & \textbf{t}\textsubscript{\textbf{FC}}
& \textbf{b}\textsubscript{\textbf{FC}\textbf{{}}} & \textbf{FC
p-value}\tabularnewline
\textbf{A1-9} & A01 & 0.108225 & 0.208824 & 0.498564\tabularnewline
\textbf{A1-36} & A01 & 0.138829 & 0.542222 & 0.381855\tabularnewline
\textbf{A1-8} & A01 & 0.125541 & 0.553333 & 0.429795\tabularnewline
\textbf{A1-12} & A01 & 0.203463 & 0.35 & 0.194595\tabularnewline
\textbf{A1-12} & A01 & 0.203463 & 0.35 & 0.194595\tabularnewline
\textbf{A1-35} & A01 & -0.19957 & -0.13889 & 0.204294\tabularnewline
\textbf{A1-15} & A01 & -0.09091 & -0.07333 & 0.572782\tabularnewline
\textbf{E4-16-A} & E04 & 0.099567 & 0.15 & 0.535024\tabularnewline
\textbf{E7-23} & E07 & -0.30266 & -0.0463 & 0.054535\tabularnewline
\textbf{D3-55} & D03 & -0.08225 & -0.16952 & 0.61176\tabularnewline
\textbf{D3-57} & D03 & -0.2381 & -0.19337 & 0.127837\tabularnewline
\textbf{E7-20} & E07 & -0.29259 & -0.025 & 0.062317\tabularnewline
\textbf{D3-25} & D03 & -0.09091 & -0.05667 & 0.572782\tabularnewline
\textbf{D3-26} & D03 & 0.069414 & 0.050926 & 0.672195\tabularnewline
\textbf{D3-46} & D03 & -0.19481 & -0.16443 & 0.214713\tabularnewline
\textbf{E7-21} & E07 & 0.084091 & 0.004167 & 0.609859\tabularnewline
\textbf{E7-22} & E07 & -0.30636 & -0.04844 & 0.051231\tabularnewline
\textbf{D3-45} & D03 & -0.05206 & -0.04333 & 0.756332\tabularnewline
\textbf{D1-5} & D01 & -0.29935 & -0.4463 & 0.055083\tabularnewline
\textbf{G1-7} & G01 & 0.134199 & 0.097778 & 0.397587\tabularnewline
\textbf{H4-31} & H04 & 0.121154 & 0.005 & 0.45891\tabularnewline
\textbf{G1-6} & G01 & 0.090909 & 0.083333 & 0.572782\tabularnewline
\textbf{H4-26} & H04 & 0.125541 & 0.118519 & 0.429795\tabularnewline
\textbf{G8-12-A} & G08 & 0.047619 & 0.068421 & 0.777959\tabularnewline
\textbf{G8-6-A} & G08 & 0.367965 & 0.115556 & 0.017854\tabularnewline
\textbf{G8-12-B} & G08 & -0.02165 & -0.03519 & 0.910196\tabularnewline
\textbf{A2-7} & A02 & 0.038961 & 0.063725 & 0.821525\tabularnewline
\textbf{H3-16} & H03 & -0.05652 & -0.008 & 0.734878\tabularnewline
\textbf{E1-2} & E01 & 0.056277 & 0.07381 & 0.73508\tabularnewline
\textbf{E3-35} & E03 & -0.06941 & -0.03277 & 0.672195\tabularnewline
\textbf{E2-10} & E02 & -0.21212 & -0.20034 & 0.175896\tabularnewline
\textbf{D3-36} & D03 & -0.05206 & -0.0827 & 0.756332\tabularnewline
\textbf{E2-11} & E02 & -0.24783 & -0.06042 & 0.114029\tabularnewline
\textbf{E1-3} & E01 & -0.02614 & -0.01667 & 0.887715\tabularnewline
\textbf{E6-23} & E06 & -0.15687 & -0.05 & 0.322972\tabularnewline
\textbf{F6-23} & F06 & 0.056522 & 0.016667 & 0.734878\tabularnewline
\textbf{E1-11} & E01 & -0.16856 & -0.03704 & 0.293383\tabularnewline
\textbf{B2-5} & B02 & 0.286335 & 0.17037 & 0.066714\tabularnewline
\textbf{B4-12} & B04 & -0.02165 & -0.01667 & 0.910196\tabularnewline
\textbf{C1-23-A} & C01 & -0.07809 & -0.045 & 0.631543\tabularnewline
\textbf{C1-9} & C01 & -0.15152 & -0.09167 & 0.337695\tabularnewline
\textbf{C1-16} & C01 & -0.16883 & -0.24958 & 0.283935\tabularnewline
\textbf{E4-14-A} & E04 & -0.17749 & -0.05628 & 0.259355\tabularnewline
\textbf{E4-11} & E04 & -0.41649 & -0.22727 & 0.007365\tabularnewline
\textbf{E6-7-C} & E06 & -0.27273 & -0.32 & 0.080417\tabularnewline
\textbf{E6-8} & E06 & -0.26898 & -0.10198 & 0.085295\tabularnewline
\textbf{E4-10} & E04 & -0.14348 & -0.04143 & 0.366498\tabularnewline
\textbf{E6-34} & E06 & -0.39913 & -0.1244 & 0.010257\tabularnewline
\textbf{E6-17} & E06 & 0.017354 & 0.007843 & 0.932558\tabularnewline
\textbf{E6-15} & E06 & 0.038961 & 0.114286 & 0.821525\tabularnewline
\textbf{E6-32} & E06 & -0.31602 & -0.20556 & 0.042331\tabularnewline
\textbf{H3-1-C} & H03 & 0.283854 & 0.075714 & 0.070603\tabularnewline
\textbf{H3-7} & H03 & 0.078091 & 0.033333 & 0.631543\tabularnewline
\textbf{H3-10} & H03 & 0.108225 & 0.088095 & 0.498564\tabularnewline
\textbf{H5-16} & H05 & 0.048037 & 0.021905 & 0.777557\tabularnewline
\textbf{H3-18} & H03 & 0.057536 & 0.002083 & 0.733719\tabularnewline
\textbf{H3-1} & H03 & 0.404352 & 0.490476 & 0.009424\tabularnewline
\textbf{G1-15} & G01 & 0.013101 & 0 & 0.954955\tabularnewline
\textbf{E8-52} & E08 & 0.073593 & 0.036905 & 0.651869\tabularnewline
\textbf{G5-26-B} & G05 & -0.21398 & -0.08778 & 0.175093\tabularnewline
\textbf{G5-18-A} & G05 & -0.11843 & -0.03039 & 0.46228\tabularnewline
\textbf{E6-25} & E06 & -0.28639 & -0.05417 & 0.070079\tabularnewline
\textbf{E3-37} & E03 & -0.26522 & -0.11 & 0.090413\tabularnewline
\textbf{E3-2} & E03 & -0.22078 & -0.09964 & 0.15857\tabularnewline
\textbf{E3-36} & E03 & -0.39394 & -0.19167 & 0.011155\tabularnewline
\textbf{D3-60} & D03 & 0.194805 & 0.245 & 0.214713\tabularnewline
\textbf{E1-9} & E01 & 0.064935 & 0.102083 & 0.693012\tabularnewline
\textbf{E3-8} & E03 & -0.16883 & -0.23439 & 0.283935\tabularnewline
\textbf{G1-17} & G01 & -0.10412 & -0.35278 & 0.516461\tabularnewline
\textbf{G8-9-B} & G08 & 0.333333 & 0.433333 & 0.03211\tabularnewline
\textbf{C2-3} & C02 & 0.168831 & 0.065 & 0.283935\tabularnewline
\textbf{D1-9} & D01 & -0.22078 & -0.06771 & 0.15857\tabularnewline
\textbf{H2-2} & H02 & 0.074568 & 0.002976 & 0.651004\tabularnewline
\textbf{G4-7} & G04 & 0.100441 & 0.006548 & 0.534374\tabularnewline
\textbf{E8-54} & E08 & 0.194805 & 0.396875 & 0.214713\tabularnewline
\textbf{E8-55} & E08 & -0.06941 & -0.02361 & 0.672195\tabularnewline
\textbf{E8-2} & E08 & -0.38528 & -0.09444 & 0.013086\tabularnewline
\textbf{D4-1} & D04 & 0.338396 & 0.205556 & 0.029848\tabularnewline
\textbf{D4-3} & D04 & 0.108225 & 0.162619 & 0.498564\tabularnewline
\textbf{D4-5} & D04 & -0.02165 & -0.01667 & 0.910196\tabularnewline
\textbf{D4-8-A} & D04 & 0.18778 & 0.057143 & 0.235418\tabularnewline
\textbf{D4-6} & D04 & 0.073593 & 0.057576 & 0.651869\tabularnewline
\textbf{E6-7} & E06 & -0.4026 & -0.39037 & 0.009481\tabularnewline
\textbf{E4-24} & E04 & -0.46855 & -0.17619 & 0.002541\tabularnewline
\textbf{E4-83} & E04 & -0.41126 & -0.28819 & 0.008035\tabularnewline
\textbf{E6-16} & E06 & -0.0303 & -0.02778 & 0.865649\tabularnewline
\textbf{E2-3} & E02 & -0.45653 & -0.21021 & 0.003336\tabularnewline
\textbf{E4-84} & E04 & -0.15152 & -0.09074 & 0.337695\tabularnewline
\textbf{E3-51} & E03 & -0.18615 & -0.07889 & 0.236289\tabularnewline
\textbf{E2-1} & E02 & -0.22078 & -0.09643 & 0.15857\tabularnewline
\textbf{E6-29} & E06 & -0.33333 & -0.18778 & 0.03211\tabularnewline
\textbf{E4-4} & E04 & 0.056277 & 0.063725 & 0.73508\tabularnewline
\textbf{E4-55} & E04 & 0.030303 & 0.034416 & 0.865649\tabularnewline
\textbf{E6-48} & E06 & 0.012987 & 0.011111 & 0.955026\tabularnewline
\textbf{E6-38} & E06 & -0.0131 & 0 & 0.954943\tabularnewline
\textbf{G8-3} & G08 & 0.099567 & 0.040741 & 0.535024\tabularnewline
\textbf{E4-4-A} & E04 & -0.18615 & -0.24074 & 0.236289\tabularnewline
\textbf{G8-4} & G08 & 0.116883 & 0.168182 & 0.463469\tabularnewline
\textbf{G8-9} & G08 & 0.173536 & 0.093333 & 0.271264\tabularnewline
\textbf{E6-9} & E06 & -0.09544 & -0.10521 & 0.553588\tabularnewline
\textbf{E4-35} & E04 & -0.34199 & -0.15824 & 0.027847\tabularnewline
\textbf{A2-20-A} & A02 & 0.220779 & 0.325 & 0.15857\tabularnewline
\textbf{H5-2-A} & H05 & -0.04396 & -0.01333 & 0.798907\tabularnewline
\textbf{H1-27-B} & H01 & -0.26407 & -0.12273 & 0.09067\tabularnewline
\textbf{H1-27} & H01 & -0.2987 & -0.25208 & 0.05518\tabularnewline
\textbf{H2-27} & H02 & -0.36797 & -0.22083 & 0.017854\tabularnewline
\textbf{H1-24} & H01 & -0.22078 & -0.23733 & 0.15857\tabularnewline
\textbf{G1-9} & G01 & 0.130722 & 0.023148 & 0.412948\tabularnewline
\textbf{H5-3} & H05 & 0.082251 & 0.031944 & 0.61176\tabularnewline
\textbf{H5-4} & H05 & 0.086768 & 0.092333 & 0.591977\tabularnewline
\textbf{H5-6} & H05 & -0.21212 & -0.42039 & 0.175896\tabularnewline
\textbf{G1-4} & G01 & 0.078091 & 0.089583 & 0.631543\tabularnewline
\textbf{H5-1} & H05 & -0.13883 & -0.1246 & 0.381855\tabularnewline
\textbf{H4-29} & H04 & 0.134199 & 0.203333 & 0.397587\tabularnewline
\textbf{G1-3} & G01 & -0.07359 & -0.07208 & 0.651869\tabularnewline
\textbf{H4-27} & H04 & 0.194805 & 0.229167 & 0.214713\tabularnewline
\textbf{H4-9-B} & H04 & -0.40435 & -0.43788 & 0.009424\tabularnewline
\textbf{H4-30} & H04 & 0.087148 & 0.05625 & 0.591583\tabularnewline
\textbf{F6-25} & F06 & -0.0303 & -0.0599 & 0.865649\tabularnewline
\textbf{F6-22} & F06 & -0.02165 & -0.00556 & 0.910196\tabularnewline
\textbf{H1-4} & H01 & 0.082973 & 0.046667 & 0.611098\tabularnewline
\textbf{H2-28} & H02 & -0.1743 & -0.00918 & 0.270882\tabularnewline
\textbf{H2-1} & H02 & -0.03471 & -0.00667 & 0.843464\tabularnewline
\textbf{H1-7} & H01 & 0.177489 & 0.117714 & 0.259355\tabularnewline
\textbf{H1-22-A} & H01 & -0.21305 & -0.03156 & 0.175552\tabularnewline
\textbf{H1-8} & H01 & 0.125541 & 0.09381 & 0.429795\tabularnewline
\textbf{H2-11} & H02 & -0.15152 & -0.06111 & 0.337695\tabularnewline
\textbf{H2-16} & H02 & -0.08677 & -0.02517 & 0.591977\tabularnewline
\textbf{H1-10} & H01 & 0.021645 & 0.040476 & 0.910196\tabularnewline
\textbf{H2-24} & H02 & 0.261443 & 0.125 & 0.095692\tabularnewline
\textbf{H1-11} & H01 & -0.00433 & -0.00556 & 1\tabularnewline
\textbf{H1-12-E} & H01 & 0.104122 & 0.113636 & 0.516461\tabularnewline
\textbf{H2-20} & H02 & 0.052061 & 0.031296 & 0.756332\tabularnewline
\textbf{H1-33} & H01 & -0.16883 & -0.30635 & 0.283935\tabularnewline
\textbf{H2-21} & H02 & 0.212121 & 0.225238 & 0.175896\tabularnewline
\textbf{H2-5-B} & H02 & 0.182213 & 0.112121 & 0.247446\tabularnewline
\textbf{H3-9} & H03 & 0.004329 & 0.003704 & 1\tabularnewline
\textbf{H1-16} & H01 & 0.060738 & 0.025556 & 0.713829\tabularnewline
\textbf{H2-33} & H02 & -0.20346 & -0.25 & 0.194595\tabularnewline
\textbf{H2-9} & H02 & 0.013044 & 0.001667 & 0.954991\tabularnewline
\textbf{G1-19} & G01 & 0.026377 & 0 & 0.887507\tabularnewline
\textbf{H5-11-A} & H05 & 0.047827 & 0.035417 & 0.777787\tabularnewline
\textbf{G1-18} & G01 & -0.36927 & -0.05208 & 0.018895\tabularnewline
\textbf{H5-9} & H05 & -0.24675 & -0.3629 & 0.114317\tabularnewline
\textbf{G1-10} & G01 & 0.109174 & 0.016667 & 0.497766\tabularnewline
\textbf{G1-11} & G01 & 0.130152 & 0.047115 & 0.41332\tabularnewline
\textbf{H5-10-A} & H05 & 0.043384 & 0.016667 & 0.799586\tabularnewline
\textbf{G1-7-A} & G01 & -0.17749 & -0.15952 & 0.259355\tabularnewline
\textbf{H5-8} & H05 & 0.100441 & 0.022222 & 0.534265\tabularnewline
\textbf{G1-8} & G01 & 0.108225 & 0.156667 & 0.498564\tabularnewline
\textbf{A2-10} & A02 & 0.116883 & 0.203824 & 0.463469\tabularnewline
\textbf{A3-10} & A03 & 0.056277 & 0.087879 & 0.73508\tabularnewline
\textbf{A3-13} & A03 & 0.064935 & 0.064583 & 0.693012\tabularnewline
\textbf{A2-9} & A02 & -0.05628 & -0.06458 & 0.73508\tabularnewline
\textbf{E4-7} & E04 & -0.12554 & -0.27976 & 0.429795\tabularnewline
\textbf{E4-8-A} & E04 & 0.264069 & 0.50625 & 0.09067\tabularnewline
\textbf{E6-39} & E06 & -0.04338 & -0.01083 & 0.799586\tabularnewline
\textbf{E4-28} & E04 & 0.056277 & 0.057407 & 0.73508\tabularnewline
\textbf{E4-29} & E04 & 0.142857 & 0.433333 & 0.36688\tabularnewline
\textbf{E4-8} & E04 & -0.07359 & -0.08095 & 0.651869\tabularnewline
\textbf{E6-5} & E06 & -0.16017 & -0.17308 & 0.310046\tabularnewline
\textbf{G8-11} & G08 & 0.112799 & 0.035 & 0.480668\tabularnewline
\textbf{E4-3} & E04 & -0.09957 & -0.08526 & 0.535024\tabularnewline
\textbf{E3-52} & E03 & -0.11688 & -0.09921 & 0.463469\tabularnewline
\textbf{E2-6} & E02 & -0.31143 & -0.06429 & 0.047626\tabularnewline
\textbf{E1-1} & E01 & -0.02603 & -0.00476 & 0.887834\tabularnewline
\textbf{E6-2-C} & E06 & 0.028847 & 0.001185 & 0.879734\tabularnewline
\textbf{E6-4} & E06 & -0.04825 & -0.00714 & 0.777211\tabularnewline
\textbf{E4-56} & E04 & -0.12554 & -0.08294 & 0.429795\tabularnewline
\textbf{E6-12} & E06 & -0.15152 & -0.17879 & 0.337695\tabularnewline
\textbf{E6-43} & E06 & -0.2381 & -0.12286 & 0.127837\tabularnewline
\textbf{C2-2} & C02 & -0.09586 & -0.03533 & 0.553167\tabularnewline
\textbf{B3-1} & B03 & 0.203463 & 0.172222 & 0.194595\tabularnewline
\textbf{B4-11} & B04 & 0.082251 & 0.083333 & 0.61176\tabularnewline
\textbf{B3-5} & B03 & 0.186147 & 0.129167 & 0.236289\tabularnewline
\textbf{B2-1} & B02 & 0.134199 & 0.093333 & 0.397587\tabularnewline
\textbf{B7-65} & B07 & 0.012987 & 0.037037 & 0.955026\tabularnewline
\textbf{B7-19} & B07 & 0.078091 & 0.044444 & 0.631543\tabularnewline
\textbf{G3-3-C} & G03 & 0.208243 & 0.198333 & 0.184896\tabularnewline
\textbf{G3-24} & G03 & 0.100001 & 0.039744 & 0.534699\tabularnewline
\textbf{G3-24} & G03 & 0.100001 & 0.039744 & 0.534699\tabularnewline
\textbf{G2-4-B} & G02 & 0.043384 & 0.030556 & 0.799586\tabularnewline
\textbf{D2-22} & D02 & -0.20346 & -0.11176 & 0.194595\tabularnewline
\textbf{B9-21} & B09 & 0.168831 & 0.259524 & 0.283935\tabularnewline
\textbf{B9-20-A} & B09 & 0.012987 & 0.05 & 0.955026\tabularnewline
\textbf{B9-22} & B09 & 0.004329 & 0.023333 & 1\tabularnewline
\textbf{B9-23} & B09 & 0.186958 & 0.405556 & 0.235916\tabularnewline
\textbf{B9-19} & B09 & 0.238095 & 0.27037 & 0.127837\tabularnewline
\textbf{B9-18} & B09 & 0.147506 & 0.201042 & 0.351904\tabularnewline
\textbf{B9-20} & B09 & 0.142857 & 0.15 & 0.36688\tabularnewline
\textbf{B9-15} & B09 & 0.021739 & 0.004444 & 0.910124\tabularnewline
\textbf{B9-14} & B09 & 0.126088 & 0.161111 & 0.429428\tabularnewline
\textbf{B9-29} & B09 & 0.242951 & 0.266667 & 0.120781\tabularnewline
\textbf{B9-27} & B09 & 0.194805 & 0.178333 & 0.214713\tabularnewline
\textbf{B9-28} & B09 & 0.203463 & 0.171429 & 0.194595\tabularnewline
\textbf{B9-35} & B09 & -0.13478 & -0.19167 & 0.397212\tabularnewline
\textbf{B7-33} & B07 & 0 & 0 & 1\tabularnewline
\textbf{B7-22} & B07 & -0.04762 & -0.09333 & 0.777959\tabularnewline
\textbf{A2-6} & A02 & 0.186147 & 0.294792 & 0.236289\tabularnewline
\textbf{A3-7} & A03 & -0.03471 & -0.04167 & 0.843464\tabularnewline
\textbf{E2-7} & E02 & -0.25995 & -0.03472 & 0.100495\tabularnewline
\textbf{E3-21} & E03 & -0.44252 & -0.35926 & 0.004384\tabularnewline
\textbf{E7-11} & E07 & -0.38696 & -0.14115 & 0.013014\tabularnewline
\textbf{E7-13} & E07 & -0.00871 & 0 & 0.977477\tabularnewline
\textbf{E5-24} & E05 & -0.09171 & -0.00455 & 0.572068\tabularnewline
\textbf{E5-14} & E05 & -0.07878 & -0.00414 & 0.630909\tabularnewline
\textbf{E7-10} & E07 & -0.37826 & -0.11 & 0.015226\tabularnewline
\textbf{B7-4} & B07 & -0.17749 & -0.175 & 0.259355\tabularnewline
\textbf{B7-6} & B07 & -0.04762 & -0.02778 & 0.777959\tabularnewline
\textbf{G6-15} & G06 & -0.19089 & -0.05167 & 0.225132\tabularnewline
\textbf{G6-19} & G06 & -0.20132 & -0.03611 & 0.203453\tabularnewline
\textbf{G6-18-B} & G06 & -0.12609 & -0.08939 & 0.429428\tabularnewline
\textbf{G6-10} & G06 & 0.138829 & 0.042063 & 0.381855\tabularnewline
\textbf{E7-26} & E07 & -0.15826 & -0.005 & 0.322199\tabularnewline
\textbf{E7-1} & E07 & -0.13478 & -0.02381 & 0.397212\tabularnewline
\textbf{E1-13} & E01 & -0.30317 & -0.00625 & 0.063299\tabularnewline
\textbf{E1-12} & E01 & -0.09423 & -0.00357 & 0.568235\tabularnewline
\textbf{D3-19} & D03 & 0.134199 & 0.43098 & 0.397587\tabularnewline
\textbf{E5-23} & E05 & -0.07196 & 0 & 0.668532\tabularnewline
\textbf{D3-20} & D03 & -0.10823 & -0.17778 & 0.498564\tabularnewline
\textbf{D3-47} & D03 & -0.13015 & -0.08011 & 0.41332\tabularnewline
\textbf{D1-13} & D01 & -0.17354 & -0.05312 & 0.271264\tabularnewline
\textbf{D4-11} & D04 & -0.00868 & -0.00159 & 0.977495\tabularnewline
\textbf{D2-36} & D02 & 0.246753 & 0.298776 & 0.114317\tabularnewline
\textbf{D2-13} & D02 & 0.264069 & 0.392593 & 0.09067\tabularnewline
\textbf{G5-19} & G05 & -0.24401 & -0.06538 & 0.120387\tabularnewline
\textbf{G4-9} & G04 & -0.15618 & -0.06952 & 0.323486\tabularnewline
\textbf{G4-8-B} & G04 & 0.147506 & 0.118095 & 0.351904\tabularnewline
\textbf{G3-1-C} & G03 & 0.100001 & 0.047778 & 0.534699\tabularnewline
\textbf{D2-32} & D02 & -0.00435 & 0 & 1\tabularnewline
\textbf{D2-20} & D02 & -0.35065 & -0.16272 & 0.024081\tabularnewline
\textbf{D2-26} & D02 & -0.2987 & -0.17361 & 0.05518\tabularnewline
\textbf{D2-18} & D02 & 0.116883 & 0.05 & 0.463469\tabularnewline
\textbf{D2-23} & D02 & -0.06074 & -0.01246 & 0.713829\tabularnewline
\textbf{D2-21} & D02 & -0.12554 & -0.08839 & 0.429795\tabularnewline
\textbf{D1-12} & D01 & -0.04338 & -0.01394 & 0.799586\tabularnewline
\textbf{D1-15} & D01 & -0.33988 & -0.25 & 0.029674\tabularnewline
\textbf{C4-11} & C04 & -0.09091 & -0.06976 & 0.572782\tabularnewline
\textbf{C1-10-A} & C01 & -0.00868 & -0.00278 & 0.977495\tabularnewline
\textbf{C1-2-A} & C01 & -0.02165 & -0.02444 & 0.910196\tabularnewline
\textbf{C4-5} & C04 & -0.35065 & -0.09375 & 0.024081\tabularnewline
\textbf{C2-11} & C02 & 0.035013 & 0.016667 & 0.843177\tabularnewline
\textbf{G3-3-A} & G03 & 0.125541 & 0.214583 & 0.429795\tabularnewline
\textbf{G3-2} & G03 & 0.092525 & 0.01 & 0.571145\tabularnewline
\textbf{G3-18} & G03 & 0.168831 & 0.238611 & 0.283935\tabularnewline
\textbf{B6-20} & B06 & 0.047619 & 0.039286 & 0.777959\tabularnewline
\textbf{B6-13} & B06 & 0.038961 & 0.019444 & 0.821525\tabularnewline
\textbf{G2-7} & G02 & -0.03896 & -0.03611 & 0.821525\tabularnewline
\textbf{G4-11} & G04 & 0.109174 & 0.016667 & 0.497766\tabularnewline
\textbf{G2-6} & G02 & 0.081339 & 0 & 0.627123\tabularnewline
\textbf{G3-16} & G03 & 0.012987 & 0.053333 & 0.955026\tabularnewline
\textbf{C1-12} & C01 & -0.23427 & -0.15833 & 0.134892\tabularnewline
\textbf{B5-19} & B05 & 0.212121 & 0.448148 & 0.175896\tabularnewline
\textbf{B5-20} & B05 & 0.281385 & 0.440278 & 0.071127\tabularnewline
\textbf{C1-20} & C01 & -0.07809 & -0.08333 & 0.631543\tabularnewline
\textbf{C1-13} & C01 & -0.14751 & -0.16389 & 0.351904\tabularnewline
\textbf{B5-14} & B05 & 0.393939 & 0.369444 & 0.011155\tabularnewline
\textbf{B5-14} & B05 & 0.393939 & 0.369444 & 0.011155\tabularnewline
\textbf{B5-16} & B05 & 0.264069 & 0.407018 & 0.09067\tabularnewline
\textbf{B5-17} & B05 & 0.238095 & 0.241667 & 0.127837\tabularnewline
\textbf{B5-22} & B05 & 0.186147 & 0.156667 & 0.236289\tabularnewline
\textbf{B5-23} & B05 & 0.385281 & 0.54359 & 0.013086\tabularnewline
\textbf{B5-40} & B05 & 0.246753 & 0.6125 & 0.114317\tabularnewline
\textbf{B5-30} & B05 & 0.341991 & 0.575 & 0.027847\tabularnewline
\textbf{B5-6} & B05 & 0.324675 & 0.32381 & 0.03692\tabularnewline
\textbf{B5-7} & B05 & 0.316017 & 0.278307 & 0.042331\tabularnewline
\textbf{C2-44} & C02 & 0.139437 & 0.033333 & 0.381476\tabularnewline
\textbf{C4-6} & C04 & -0.1342 & -0.06973 & 0.397587\tabularnewline
\textbf{C2-9} & C02 & 0.239133 & 0.144444 & 0.127534\tabularnewline
\textbf{C2-22} & C02 & 0.100001 & 0.021212 & 0.534699\tabularnewline
\textbf{C4-14} & C04 & -0.29004 & -0.24762 & 0.062735\tabularnewline
\textbf{C2-19} & C02 & 0.069414 & 0.032222 & 0.672195\tabularnewline
\textbf{C2-4} & C02 & 0.099567 & 0.045614 & 0.535024\tabularnewline
\textbf{B6-10} & B06 & -0.12554 & -0.0875 & 0.429795\tabularnewline
\textbf{B6-10} & B06 & -0.12554 & -0.0875 & 0.429795\tabularnewline
\textbf{G2-5} & G02 & 0.105039 & 0.011905 & 0.51568\tabularnewline
\textbf{G3-22} & G03 & 0.225597 & 0.086111 & 0.150245\tabularnewline
\textbf{G2-4-A} & G02 & 0.069414 & 0.032143 & 0.672195\tabularnewline
\textbf{G4-6} & G04 & 0.236338 & 0.019048 & 0.134167\tabularnewline
\textbf{G3-10} & G03 & 0.352177 & 0.145238 & 0.023969\tabularnewline
\textbf{G2-9} & G02 & -0.29788 & -0.04074 & 0.060789\tabularnewline
\textbf{G4-1} & G04 & 0.312365 & 0.154167 & 0.045193\tabularnewline
\textbf{G4-1-D} & G04 & 0.290043 & 0.312088 & 0.062735\tabularnewline
\textbf{G3-11} & G03 & 0.136585 & 0.00625 & 0.395829\tabularnewline
\textbf{G4-3} & G04 & 0.333835 & 0.00625 & 0.039028\tabularnewline
\textbf{G4-14} & G04 & -0.32456 & -0.05 & 0.040807\tabularnewline
\textbf{G4-12} & G04 & 0.054484 & 0 & 0.753142\tabularnewline
\textbf{G2-3} & G02 & 0.30706 & 0.02 & 0.060245\tabularnewline
\textbf{G2-2} & G02 & -0.00433 & -8.33E-04 & 1\tabularnewline
\textbf{G2-1-B} & G02 & -0.12554 & -0.1039 & 0.429795\tabularnewline
\textbf{G2-1-A} & G02 & -0.05628 & -0.08333 & 0.73508\tabularnewline
\textbf{G2-1} & G02 & 0 & 0 & 1\tabularnewline
\textbf{A2-12} & A02 & -0.03896 & -0.03667 & 0.821525\tabularnewline
\textbf{B2-22} & B02 & 0.252729 & 0.205128 & 0.107479\tabularnewline
\textbf{A3-16} & A03 & -0.1342 & -0.08944 & 0.397587\tabularnewline
\textbf{D1-16} & D01 & -0.24675 & -0.09697 & 0.114317\tabularnewline
\textbf{F6-26} & F06 & 0.069414 & 0.204464 & 0.672195\tabularnewline
\textbf{F6-11} & F06 & -0.21212 & -0.25175 & 0.175896\tabularnewline
\textbf{E8-49} & E08 & 0.064935 & 0.056122 & 0.693012\tabularnewline
\textbf{B7-35} & B07 & -0.08715 & -0.03056 & 0.591583\tabularnewline
\textbf{B7-28-A} & B07 & -0.20346 & -0.12222 & 0.194595\tabularnewline
\textbf{G6-18-A} & G06 & 0.069414 & 0.019913 & 0.672195\tabularnewline
\textbf{F4-9} & F04 & 0.195654 & 0.061667 & 0.214349\tabularnewline
\textbf{F2-8} & F02 & 0.362102 & 0.045667 & 0.021715\tabularnewline
\textbf{F4-8-A} & F04 & -0.17208 & -0.05 & 0.311793\tabularnewline
\textbf{F4-12} & F04 & 0.213904 & 0.023958 & 0.187786\tabularnewline
\textbf{F2-7} & F02 & 0.29277 & 0.051852 & 0.06491\tabularnewline
\textbf{E9-63} & E09 & -0.20824 & -0.08148 & 0.184896\tabularnewline
\textbf{E9-23-A} & E09 & 0.121475 & 0.098148 & 0.446271\tabularnewline
\textbf{E9-13-A} & E09 & -0.09091 & -0.06278 & 0.572782\tabularnewline
\textbf{E9-14-A} & E09 & 0.039131 & 0.004762 & 0.821386\tabularnewline
\textbf{E9-20} & E09 & 0.142857 & 0.094444 & 0.36688\tabularnewline
\textbf{E3-44-A} & E03 & -0.27273 & -0.14259 & 0.080417\tabularnewline
\textbf{E1-18} & E01 & 0.082251 & 0.052778 & 0.61176\tabularnewline
\textbf{E5-11} & E05 & -0.21787 & -0.02341 & 0.166445\tabularnewline
\textbf{E3-20} & E03 & -0.18615 & -0.2641 & 0.236289\tabularnewline
\textbf{E6-21} & E06 & 0.043384 & 0.015556 & 0.799586\tabularnewline
\textbf{E3-41} & E03 & -0.18615 & -0.16806 & 0.236289\tabularnewline
\textbf{E3-42} & E03 & -0.21212 & -0.24325 & 0.175896\tabularnewline
\textbf{D3-18} & D03 & -0.17749 & -0.07679 & 0.259355\tabularnewline
\textbf{E7-6} & E07 & -0.30736 & -0.16 & 0.048398\tabularnewline
\textbf{E7-8} & E07 & -0.3731 & -0.34861 & 0.016495\tabularnewline
\textbf{D3-21-B} & D03 & -0.04762 & -0.0197 & 0.777959\tabularnewline
\textbf{E5-22} & E05 & -0.44423 & -0.03733 & 0.005244\tabularnewline
\textbf{E7-4} & E07 & -0.32972 & -0.15926 & 0.034372\tabularnewline
\textbf{E7-17} & E07 & -0.38501 & -0.0369 & 0.016337\tabularnewline
\textbf{E2-27} & E02 & -0.20986 & -0.00714 & 0.196029\tabularnewline
\textbf{E9-16} & E09 & -0.05229 & -0.00357 & 0.756144\tabularnewline
\textbf{E9-43} & E09 & 0.052061 & 0.009091 & 0.756332\tabularnewline
\textbf{E9-11} & E09 & 0.012987 & 0.007407 & 0.955026\tabularnewline
\textbf{E9-44} & E09 & -0.07809 & -0.08622 & 0.631543\tabularnewline
\textbf{E8-17} & E08 & -0.49458 & -0.63878 & 0.001434\tabularnewline
\textbf{E8-48-A} & E08 & 0.333333 & 0.54881 & 0.03211\tabularnewline
\textbf{E8-46} & E08 & -0.33626 & -0.11667 & 0.031791\tabularnewline
\textbf{E8-48} & E08 & 0.082251 & 0.142857 & 0.61176\tabularnewline
\textbf{E8-48-B} & E08 & 0.393939 & 0.650595 & 0.011155\tabularnewline
\textbf{C4-10} & C04 & -0.16883 & -0.08333 & 0.283935\tabularnewline
\textbf{B2-7} & B02 & 0.286335 & 0.211905 & 0.066714\tabularnewline
\textbf{B7-8} & B07 & -0.06494 & -0.03733 & 0.693012\tabularnewline
\textbf{B7-20} & B07 & 0.229437 & 0.357692 & 0.142569\tabularnewline
\textbf{B7-45} & B07 & -0.21692 & -0.05333 & 0.166896\tabularnewline
\textbf{B7-49} & B07 & -0.25273 & -0.07889 & 0.107572\tabularnewline
\textbf{D1-6-A} & D01 & -0.07359 & -0.07381 & 0.651869\tabularnewline
\textbf{E7-24} & E07 & -0.09715 & -0.00833 & 0.552108\tabularnewline
\textbf{D1-7} & D01 & -0.20346 & -0.15625 & 0.194595\tabularnewline
\textbf{D1-6} & D01 & -0.19481 & -0.2 & 0.214713\tabularnewline
\textbf{D1-17} & D01 & -0.1 & -0.04444 & 0.534699\tabularnewline
\textbf{D1-8} & D01 & -0.21692 & -0.10417 & 0.166896\tabularnewline
\textbf{D1-11} & D01 & -0.01735 & -0.00833 & 0.932558\tabularnewline
\textbf{D1-8-A} & D01 & -0.22944 & -0.17361 & 0.142569\tabularnewline
\textbf{F6-8-A} & F06 & -0.27273 & -0.31923 & 0.080417\tabularnewline
\textbf{F6-9-A} & F06 & -0.20346 & -0.16667 & 0.194595\tabularnewline
\textbf{F6-31} & F06 & 0.008677 & 0.011458 & 0.977495\tabularnewline
\textbf{F6-16} & F06 & -0.1342 & -0.15833 & 0.397587\tabularnewline
\textbf{F6-17} & F06 & -0.07809 & -0.01845 & 0.631543\tabularnewline
\textbf{F6-18} & F06 & -0.01735 & -0.01667 & 0.932558\tabularnewline
\textbf{F5-17-B} & F05 & -0.09091 & -0.13631 & 0.572782\tabularnewline
\textbf{F5-19} & F05 & 0.116883 & 0.436364 & 0.463469\tabularnewline
\textbf{F5-15} & F05 & 0.350649 & 0.472222 & 0.024081\tabularnewline
\textbf{F5-15} & F05 & 0.350649 & 0.472222 & 0.024081\tabularnewline
\textbf{F5-17-A} & F05 & 0.212121 & 0.316667 & 0.175896\tabularnewline
\textbf{F3-4} & F03 & 0.125935 & 0.004545 & 0.441878\tabularnewline
\textbf{F3-3} & F03 & 0.207848 & 0.002857 & 0.207672\tabularnewline
\textbf{F5-7} & F05 & -0.21692 & -0.13095 & 0.166896\tabularnewline
\textbf{F3-5} & F03 & 0.091305 & 0.028571 & 0.572477\tabularnewline
\textbf{F3-2-B} & F03 & -0.17749 & -0.1748 & 0.259355\tabularnewline
\textbf{F3-1} & F03 & -0.01751 & 0 & 0.93245\tabularnewline
\textbf{F3-2} & F03 & -0.31373 & -0.10758 & 0.044965\tabularnewline
\textbf{F3-6} & F03 & -0.21196 & -0.04167 & 0.183027\tabularnewline
\textbf{E6-3} & E06 & -0.2256 & -0.15 & 0.150245\tabularnewline
\textbf{E6-2} & E06 & 0.030303 & 0.058333 & 0.865649\tabularnewline
\textbf{E3-50} & E03 & -0.02603 & -0.02158 & 0.887834\tabularnewline
\textbf{E3-48} & E03 & -0.28139 & -0.18188 & 0.071127\tabularnewline
\textbf{E5-25} & E05 & -0.24401 & -0.07048 & 0.120387\tabularnewline
\textbf{E3-7} & E03 & -0.1342 & -0.13214 & 0.397587\tabularnewline
\textbf{E7-16} & E07 & -0.24401 & -0.09667 & 0.120387\tabularnewline
\textbf{E6-20} & E06 & -0.01735 & -0.00267 & 0.932558\tabularnewline
\textbf{E5-19} & E05 & -0.15218 & -0.02941 & 0.337311\tabularnewline
\textbf{E3-19} & E03 & 0.043384 & 0.006667 & 0.799586\tabularnewline
\textbf{E5-12} & E05 & -0.38514 & -0.05463 & 0.013942\tabularnewline
\textbf{E7-29} & E07 & -0.14475 & -0.01667 & 0.365606\tabularnewline
\textbf{E7-2} & E07 & -0.55844 & -0.34902 & 3.07E-04\tabularnewline
\textbf{E7-18} & E07 & -0.25384 & -0.02745 & 0.107292\tabularnewline
\textbf{E1-14} & E01 & -0.27883 & -0.05741 & 0.078813\tabularnewline
\textbf{D3-56} & D03 & 0.021645 & 0.053846 & 0.910196\tabularnewline
\textbf{C4-12} & C04 & -0.04338 & -0.07434 & 0.799586\tabularnewline
\textbf{D1-10} & D01 & -0.0303 & -0.0049 & 0.865649\tabularnewline
\textbf{F4-10} & F04 & 0.168565 & 0.009091 & 0.293383\tabularnewline
\textbf{F2-21} & F02 & -0.18615 & -0.24722 & 0.236289\tabularnewline
\textbf{F2-8-A} & F02 & 0.127204 & 0.074026 & 0.428199\tabularnewline
\textbf{F4-11} & F04 & -0.09989 & -0.00119 & 0.548234\tabularnewline
\textbf{F2-6} & F02 & 0.048037 & 0.005882 & 0.777557\tabularnewline
\textbf{F2-1-B} & F02 & 0.116883 & 0.09127 & 0.463469\tabularnewline
\textbf{F2-13} & F02 & 0.100001 & 0.057143 & 0.534699\tabularnewline
\textbf{F4-5} & F04 & -0.20824 & -0.16125 & 0.184896\tabularnewline
\textbf{F2-1-C} & F02 & 0.177489 & 0.152183 & 0.259355\tabularnewline
\textbf{F4-4} & F04 & -0.16576 & 0 & 0.322077\tabularnewline
\textbf{F2-1-A} & F02 & 0.229437 & 0.198333 & 0.142569\tabularnewline
\textbf{D1-14} & D01 & -0.49458 & -0.21515 & 0.001434\tabularnewline
\textbf{C4-9-B} & C04 & -0.48052 & -1.18182 & 0.001924\tabularnewline
\textbf{C4-13} & C04 & -0.32468 & -0.1631 & 0.03692\tabularnewline
\textbf{B8-10-A} & B08 & -0.12554 & -0.17143 & 0.429795\tabularnewline
\textbf{G4-7-A} & G04 & 0.295012 & 0.153333 & 0.058756\tabularnewline
\textbf{G5-12-C} & G05 & -0.1342 & -0.06889 & 0.397587\tabularnewline
\textbf{G5-13-A} & G05 & -0.09091 & -0.08 & 0.572782\tabularnewline
\textbf{G5-12-D} & G05 & 0.234274 & 0.114815 & 0.134892\tabularnewline
\textbf{G5-10-A} & G05 & 0.303688 & 0.180532 & 0.051603\tabularnewline
\textbf{B7-18} & B07 & -0.06941 & -0.04306 & 0.672195\tabularnewline
\textbf{B2-19} & B02 & 0.257652 & 0.0725 & 0.101313\tabularnewline
\textbf{B2-25} & B02 & -0.01735 & -0.0037 & 0.932558\tabularnewline
\textbf{G5-26-A} & G05 & -0.12148 & -0.15238 & 0.446271\tabularnewline
\textbf{C4-4} & C04 & -0.2381 & -0.09286 & 0.127837\tabularnewline
\textbf{C2-10} & C02 & 0.226584 & 0.045455 & 0.149812\tabularnewline
\textbf{C4-1} & C04 & -0.17354 & -0.07007 & 0.271264\tabularnewline
\textbf{C2-13} & C02 & -0.18615 & -0.11389 & 0.236289\tabularnewline
\textbf{C2-12} & C02 & 0.134784 & 0.044872 & 0.397212\tabularnewline
\textbf{C2-14} & C02 & 0.043384 & 0.02 & 0.799586\tabularnewline
\textbf{C1-7} & C01 & -0.21212 & -0.09881 & 0.175896\tabularnewline
\textbf{C1-8} & C01 & -0.16017 & -0.07821 & 0.310046\tabularnewline
\textbf{A3-9} & A03 & 0.082251 & 0.083333 & 0.61176\tabularnewline
\textbf{A3-9} & A03 & 0.082251 & 0.083333 & 0.61176\tabularnewline
\textbf{B2-9} & B02 & 0.359307 & 0.183333 & 0.020765\tabularnewline
\textbf{A2-21} & A02 & 0.090909 & 0.112963 & 0.572782\tabularnewline
\textbf{B4-29} & B04 & -0.16087 & -0.08889 & 0.309661\tabularnewline
\textbf{B2-10} & B02 & 0.378264 & 0.241667 & 0.015226\tabularnewline
\textbf{A2-18} & A02 & 0.064935 & 0.068125 & 0.693012\tabularnewline
\textbf{A2-16} & A02 & -0.31602 & -1.33205 & 0.042331\tabularnewline
\textbf{B9-3} & B09 & 0.008677 & 0.002564 & 0.977495\tabularnewline
\textbf{B9-6} & B09 & 0.073593 & 0.068627 & 0.651869\tabularnewline
\textbf{B7-17} & B07 & -0.22944 & -0.10476 & 0.142569\tabularnewline
\textbf{B8-2-A} & B08 & -0.15618 & -0.12949 & 0.323486\tabularnewline
\textbf{D2-17} & D02 & 0.090909 & 0.054762 & 0.572782\tabularnewline
\textbf{D2-16} & D02 & -0.12148 & -0.07222 & 0.446271\tabularnewline
\textbf{D2-24} & D02 & -0.02614 & -0.00222 & 0.887715\tabularnewline
\textbf{B9-4} & B09 & 0.168831 & 0.107556 & 0.283935\tabularnewline
\textbf{B9-5} & B09 & -0.06494 & -0.18 & 0.693012\tabularnewline
\textbf{F4-3-C} & F04 & 0.147506 & 0.075 & 0.351904\tabularnewline
\textbf{F4-3-B} & F04 & 0.138829 & 0.052381 & 0.381855\tabularnewline
\textbf{F4-2} & F04 & 0.147122 & 0 & 0.38704\tabularnewline
\textbf{C4-9-A} & C04 & -0.39394 & -0.78472 & 0.011155\tabularnewline
\textbf{C4-8} & C04 & -0.48052 & -0.31921 & 0.001924\tabularnewline
\textbf{C4-9-C} & C04 & -0.47186 & -1.9 & 0.002324\tabularnewline
\textbf{C2-7} & C02 & 0.378264 & 0.189815 & 0.015226\tabularnewline
\textbf{C4-7} & C04 & -0.20346 & -0.07959 & 0.194595\tabularnewline
\textbf{C4-7} & C04 & -0.20346 & -0.07959 & 0.194595\tabularnewline
\textbf{C4-9} & C04 & -0.49784 & -0.52308 & 0.001306\tabularnewline
\textbf{C4-2} & C04 & -0.14348 & -0.05424 & 0.366498\tabularnewline
\textbf{C4-3} & C04 & -0.25218 & -0.20833 & 0.107712\tabularnewline
\textbf{B2-20} & B02 & -0.07391 & -0.00625 & 0.65161\tabularnewline
\textbf{B2-17} & B02 & -0.15618 & -0.04444 & 0.323486\tabularnewline
\textbf{B8-38} & B08 & -0.02165 & -0.00476 & 0.910196\tabularnewline
\textbf{B8-5} & B08 & 0.01743 & 2.98E-04 & 0.932504\tabularnewline
\textbf{B8-8} & B08 & 0.260304 & 0.246667 & 0.096045\tabularnewline
\textbf{B8-9} & B08 & 0.104122 & 0.061224 & 0.516461\tabularnewline
\textbf{G5-11} & G05 & 0.095445 & 0.048333 & 0.553588\tabularnewline
\textbf{G5-13} & G05 & 0.286388 & 0.044857 & 0.069929\tabularnewline
\textbf{F4-1} & F04 & -0.13665 & 0 & 0.429494\tabularnewline
\textbf{E9-22} & E09 & -0.14751 & -0.03542 & 0.351904\tabularnewline
\textbf{E9-23} & E09 & -0.08261 & -0.03848 & 0.611477\tabularnewline
\textbf{E9-25} & E09 & 0.060738 & 0.025 & 0.713829\tabularnewline
\textbf{E9-36} & E09 & -0.1128 & -0.25714 & 0.480668\tabularnewline
\textbf{D4-7} & D04 & 0.298701 & 0.115686 & 0.05518\tabularnewline
\textbf{D4-27} & D04 & 0.257652 & 0.085 & 0.101313\tabularnewline
\textbf{D4-9-A} & D04 & 0.125541 & 0.126667 & 0.429795\tabularnewline
\textbf{D2-19} & D02 & 0.160173 & 0.083651 & 0.310046\tabularnewline
\textbf{D2-5} & D02 & 0.047619 & 0.103571 & 0.777959\tabularnewline
\textbf{D2-7} & D02 & 0.090909 & 0.120635 & 0.572782\tabularnewline
\textbf{D2-11} & D02 & 0.281385 & 0.475397 & 0.071127\tabularnewline
\textbf{G8-8-B} & G08 & 0.272077 & 0.637222 & 0.090684\tabularnewline
\textbf{G1-1} & G01 & 0.273684 & 0.454167 & 0.097994\tabularnewline
\textbf{G3-19} & G03 & -0.03846 & -0.00486 & 0.83224\tabularnewline
\textbf{B2-16} & B02 & 0.017507 & 0 & 0.932432\tabularnewline
\textbf{E4-5-A} & E04 & -0.04211 & -0.16066 & 0.820338\tabularnewline
\bottomrule
\end{longtable}

~

\selectlanguage{english}
\FloatBarrier
\end{document}

