HomeMy WebLinkAbout20080868 Ver 2_Appendix A History and Methods_20220605APPENDIX A
HISTORY AND METHODS BY PARAMETER
APPENDIX A. HISTORY AND METHODS BY PARAMETER
Watershed reduction impacts are determined on an annual acreage basis. Some monitoring
parameters are collected once a year only and others are collected throughout the year. Data
collection for a parameter in a specific watershed may occur prior to the mine progress through
that watershed for that year or, in the case of parameters collected throughout the year, mine
progress through the watershed may have affected only the data collected in the last portion of
the calendar year. The NCDWQ agreed during the previous study that "significant measurable
results" were not likely to be determined "until 10 percent or more of the basin was impacted".
The 10 percent threshold was developed specifically for Alt E impacts in Tooley Creek yet remains
an important factor in the determination of the first post -Mod Alt L year in a given creek.
To determine the percentage of basin impact and to increase accuracy of analysis, drainage basin
quantification is recalculated annually at the end of each mine advance year. Over the course of
the creeks study, basin acreage calculations have been refined as past activities were uncovered
and digital tools improved (e.g., LiDAR). In calculation of the percent impacts to assign 2013 as
pre- or post -Mod Alt L for a given creek (10 percent reduction threshold on current basin), it was
discovered that the drainage basins of the NCPC creeks had actually been reduced prior to Alt E
by the construction of a canal inside the Alt E boundary dug in the late 1970s or early 1980s. This
canal cut off the NCPC creeks from portions of their historic drainage basin and resulted in several
corrections. It reduced the historic basin acreage, the Alt E baseline acreage, and the basin
remaining after Alt E for each NCPC creek shown in previous annual reports. These reductions
affected the pre -Alt E and pre -Mod Alt L basins and subsequent percent reductions shown for
some creeks in previous annual reports. Boundaries of drainage basins have not been revised
since 2013.
A. Low Flow Stream Monitoring
1. History
With the concurrence of regulatory agencies and subsequently, the Science Panel
(committee chosen to review this report), flow monitoring with weirs was discontinued at the end
of 2009 in Huddles Cut and the end of 2010 in Tooley Creek. While no monitoring has occurred
in Jacks Creek since 2005, the weir in Jacks Creek was removed in 2010 when the weirs in Tooley
and Huddles Cut were removed. However, PCS elected to install a new device developed
specifically to monitor low flow events in upper headwater stream mitigation sites in the coastal
plain in appropriate creeks monitored for this study. Two flow gauges were installed in Porter
Creek in April 2010 and four were installed in Duck Creek in March 2011. The most downstream
gauge in Porter Creek was within the Mod Alt L boundary and did not collect any post -Mod Alt L
data; and, therefore, not included in the 2012 creeks report.
The upstream Porter Creek segment monitored by the other low flow gauge is
estimated to have a 117-acre drainage basin. One Duck Creek segment with two of the four
gauges is in the headwaters of the northwest prong (-124-acre basin) and the other segment is
in the south fork of the east prong (-151-acre basin).
Beta models of the low flow gauges were produced by Flowline Products and
installed in some PCS stream and wetland mitigation sites in 2010. Refinement of the beta model
and larger scale production by Remote Data Systems, Inc (RDS) stalled and use of these devices
ceased in 2013. The gauges were used to document the duration and relative magnitude of flow
events occurring in shallow perennial, intermittent, and zero order stream segments. The device
was based on the principles of a variable area flow measurement device and consists of a vertical
baffle mounted on an axis inside a protective housing. Flow passing through the housing caused
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the baffle to tilt on its axis, and the degree of tilt was recorded by the internal electronics and
logging device. The gauge was capable of reliably recording flows as low as 0.5 gal/min. Once
a month biologists downloaded the gauge and visually determined the relative degree of flow as
well as water depth at each device location. Once downloaded from the flow gauge, the voltage
setting for each gauge was calibrated during analysis such that dry conditions and times of no
flow (from observer data) were graphically depicted as a "flatline" or zero. A flow event was
defined as flow >_0.5 gallons per minute (gpm) unless noted otherwise. Both rainfall in the
watershed and hydrology of adjacent wells were also used to double check the accuracy of the
flow gauge data.
By late 2013, the low flow gauges became more prone to error and it was
increasingly evident that production of additional units was not likely; therefore, PCS decided to
cease monitoring flow with these units as their inclusion in the creeks monitoring program was
completely voluntary.
On December 10, 2020, the Corps and NCDWR met with PCS and CZR on site to
visit upper headwater areas and discuss flow monitoring. NCDWR (formerly NCDWQ) forms
were informally used to compare pre- conditions vs. post- conditions during the 2020 visit. Ratings
were relatively similar, with no concerns or suggestions for a change in flow methodology.
2. Methods
Since March 2012 in all creeks at the intended locations of the low flow gauges,
biologists make qualitative observations of flow (low, medium, high) and measure water depth at
designated flow poles in the creek during the monthly download of adjacent wetland wells as
indicated by the red arrow shown on the monitoring locations figure for each creek. Evidence of
recent flow is noted and photographs or video are taken as appropriate. The observations are
presented in table form on an annual basis and photographs or video are retained at CZR and
made available upon request. With no further factory production anticipated and cessation of
monitoring of the low flow gauges, these observations will continue and will serve to document
flow events. At the Science Panel meeting in August 2015, flow observations were requested to
be made at the three Huddles Cut Aqua TROLLs when the meters were downloaded (twice a
month) despite their locations in the bidirectional portion of the creek system. These observations
began in September 2015. Some wind conditions can move water into the creek upstream of the
meters and when the wind direction/speed changes, this water will mix with runoff from the
watershed itself and can contribute to the duration and speed of flow observed as well as the
water depth measured: therefore, wind direction and estimated speed are noted with the Huddles
Cut observations. The data form was modified further in 2020 at the suggestion of the Science
Panel in August 2019 to include flow direction since direction may be difficult to determine from
videos. These data are found in Appendix B, Table B-2 on the flash drive. The evaluation of flow
using well data began in 2020; observation data have been used with well data to establish well
water levels with documented observed flow to estimate of the number of days with flow for a
year.
B. Salinity Monitoring
1. History
A CAMA permit (number 66-11) was received by PCS in May 2011 for construction
and/or rehabilitation of 23 piers located in the monitored creeks. Stainless steel boxes were
mounted on these piers to house the water level and salinity monitoring equipment. Equipment
was installed as the piers were completed. By July 2011 all salinity monitoring equipment was in
place per the plan and salinity monitoring began in 12 of the 15 creeks (six of which were controls)
identified in the plan for the first time in 2011. During 2013, salinity was monitored at seven creeks
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that have had or will have their watershed disturbed by Mod Alt L mine activities and at eight
control creeks/locations. In 2018, a new CAMA General permit covered new piers for the 15th
creek identified in the plan (Broomfield Swamp Creek) and its nearby control creek (an unnamed
tributary to South Creek, SCUT1). With the exception of the control stations in South Creek,
Pamlico River, PA2, Durham Creek, the two unnamed tributaries to Durham Creek, the three
stations on Huddles Cut (two upstream and one downstream), Broomfield Swamp Creek, and
SCUT1 which each have three salinity stations (see comments below regarding Huddles Cut), all
other creeks monitored for salinity have an upstream (#1) and a downstream (#2) salinity station.
Control locations, or control creeks, aid in the determination of whether changes
that may occur between pre- and post -Mod Alt L monitoring are also seen regionally. Control
locations/creeks for salinity are listed below:
• one in the Pamlico River established in 1998 (PS1),
• one in South Creek established in 1998 (SS1),
• two in Long Creek (LOCS1; LOCS2) established in 2011,
• two in Little Creek (LCS1; LCS2) established in 2011,
• one in PA2 (PA2S1) established in 2011,
• two in Duck Creek (DKCS1; DKCS2) established in 2011,
• one in DCUT19 (DC19S1) established in 2013,
• one in Durham Creek (DCS1) established in 2013, and
• three in a tributary to South Creek (SCUT1) established in 2019.
Muddy Creek is another control creek; however, only fish, sediment, and benthos
data are collected. Fish sampling has occurred weekly April through June every year since 1999
except for 2006, when no monitoring occurred for any parameter. A handheld YS1Pro Plus
Quatro is used to collect water quality data, including salinity, at each visit; therefore, salinity data
for those three months are available for evaluation of changes over time in Muddy Creek.
Issues with failing Aqua Trolls at upstream Huddles Cut associated with low or no
water conditions were presented and discussed with the Science Panel in 2020. The panel
agreed with the proposed shift in monitoring equipment at HS1 and HS2 from Aqua Trolls to
Level Trolls. Level Trolls are tolerant of dry periods and still allow collection of temperature and
water levels every 1.5 hours, but salinity data would be reduced to twice per month in
association with water quality collections. Formal request for this change was made after the
meeting via a letter dated November 12, 2020 from PCS to the Corps and NCDWR. Approval
of the change was received via email 15 January 2021 and conversion of equipment occurred
during February 2021.
2. Methods
A YSI 600XLM multi -parameter water quality sonde (YSI) was utilized to record
salinity and water depth at each monitoring location during earlier monitoring (1999-2005) and
from January 2007 to August 2008. Salinity monitoring after August 2008 was performed with In -
Situ AquaTROLL 200 CTD loggers. The YSI sondes automatically calculated salinity readings
from conductivity and temperature. The salinity sensor range for each YSI was 0-70 parts per
thousand (ppt), with an accuracy of +/- 1.0 percent of the reading, or 0.1 ppt, whichever was
greater. The resolution was 0.01 ppt. The depth sensor was a stainless steel strain gauge
pressure sensor with a range of 0-30 feet, an accuracy of +/- 0.7 inch, and a resolution of 0.01
inch.
The Aqua TROLL 200 CTD logger is manufactured by In -Situ, Inc. Like the YSI
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sondes, the Aqua TROLLs generate salinity readings from temperature and conductivity. The
salinity sensor range is 0-42 practical salinity units (psu), with an accuracy of +/- 0.5 percent of
the reading. The resolution is 0.001 psu. Practical salinity units (psu) are essentially equivalent
to parts per thousand (ppt); however, psu is considered a more appropriate descriptor since it
refers to the practical salinity scale that is used to calculate salinity (Reid 2006). The depth sensor
is a titanium/silicon strain gauge pressure sensor with a range of 0-35 feet, an accuracy of +/-
0.003 inch, and a resolution of 0.001 inch.
The Aqua TROLLs are programmed to record a salinity and depth reading every
1.5 hours (16 readings per day). Each Aqua TROLL is downloaded and calibrated for conductivity
every two weeks and depth is calibrated as needed. The probes are also cleaned and batteries
checked and replaced as necessary. Sensors are located near the bottom of the stream to ensure
continuous data collection during most low water conditions. Depth readings are compensated
for the distance from the sensor to the creek bottom. Occasional gaps in the continuous data in
some years exist due to dead batteries, equipment malfunctions, and low water levels not allowing
sensors to be fully submerged. At the time of download, the handheld YSI Pro Plus is used to
determine the accuracy of the Aqua TROLL readings.
To aid in the interpretation of factors that may influence salinity fluctuations,
continuous salinity data from each Aqua TROLL are displayed on graphs along with the
continuous water level data from that monitor, daily rainfall from the nearest rain gauge (PCS
Aurora NOAA Station), and data from the Tar River U.S. Geological Survey flow gauge at
Greenville, NC (http://waterdata.usgs.gov/nwis/uv?02084000) (Appendix C). The Tar River
becomes the Pamlico River at the US Hwy 17 Bridge in Washington. These graphs were used
for a qualitative assessment of the relative effects of wind tides, local drainage basin input, and
Tar River input on salinity fluctuations in the monitored creeks. Wind data, either from the PCS
NOAA or Craven County Regional Airport, New Bern weather station, were compared with the
graphs for the 2012 monitoring year report to determine if there was any effect from wind.
Spearman Rank Order correlation tests were conducted to test the correlation among rainfall,
discharge, and salinity at the monitors. This wind analysis may be conducted in subsequent years
as applicable.
T-tests and ANOVAs were utilized to attempt to find differences between pre -Mod
Alt L salinity and post -Mod Alt L salinity but normality failed in the t-test using either Shapiro -Wilk
or Kolmogorov-Smirov, so a Mann -Whitney Rank Sum or Kruskal-Wallis One -Way ANOVA on
Ranks was used at the recommendation of the SigmaPlot® software.
C. Wetland Hydrology Monitoring
1. History
Monitoring of wetland hydrology occurred in Jacks Creek, Tooley Creek, and
Huddles Cut under the 1998 plan and was expanded to include seven new creeks under the 2011
plan, two of which (DCUT11 and DCUT19) were first monitored in 2013. In 2019, Broomfield
Swamp Creek (one of the three named creeks in the S33 Tract permitted to be impacted by Mod
Alt L) was included in the study along with SCUT1, a nearby unnamed tributary to South Creek,
as a control.
The location of wetland wells did not change from previous years for the three
creeks monitored under the 1998 creeks study, with the exception of the loss of three well
locations at Huddles Cut due to activities associated with the permitted mine advance, and the
addition of four more wells at Jacks Creek. The three wells lost at Huddles were in the most
upstream reaches of the west prong (HWW1, HWW10, and HWW11). Three of the new wells
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added in Jacks Creek (JWS, JW7, JW9) were installed in close proximity to an original well
location so that a broader area of the floodplain could be monitored semi -continuously, and a
fourth one (JW2B) was installed a short distance upstream of what was previously the most
upstream well.
2. Methods
Both manual wells and semi -continuous recorders are used to monitor the water
table. Each manual well consists of a 54-inch length of 1 1/4-inch diameter PVC well screen
(0.010-inch slots) and an 18-inch long riser made of solid -walled 1 1/4-inch diameter PVC pipe.
The well screen and riser are connected by a PVC coupler. The manual wells were installed to a
depth of 60 inches, with 12 inches of the riser extending above the ground. The top of the riser
is covered by a PVC cap, and a hole in the side of the riser provides air exchange during water
level fluctuations. Manual wells were installed with the original wells in Jacks, Tooley, and
Huddles to serve as controls to cross reference with the new electronic recording devices (WLs
— at the time). Manual measurements are now collected from the same well screen harboring the
electronic recorder.
WL/Ecotones (RDS products) were originally used to collect semi -continuous
water level data. In 2011, all new creek monitoring locations and all but two existing Ecotones
were replaced with Level TROLL 500 water level loggers manufactured by In -Situ. The Level
TROLL is housed inside a 3-inch diameter PVC well screen (0.010-inch slots) installed to a depth
of approximately 50 inches. The current configuration of cable and Level TROLL record water
levels over a range of about five feet, and installation varies according to site conditions, but in
general most wells record water within 2 feet above and below the ground surface. The TROLLs
record the water level every 1.5 hours (16 times per day). To prevent damage by bears, the
aboveground portions of the well screens were surrounded by a fence enclosure made of metal
T-posts to encase the well with strands of barbed wire.
All monitoring wells were checked and downloaded once a month. Wetland
hydroperiods were calculated for each monitoring well during the growing season. A hydroperiod
is defined as consecutive days during the growing season that the water table is within 12 inches
of the surface or the surface is inundated, and is expressed as a percentage of the growing
season. For this project, the growing season is defined by the Beaufort County soil survey (Kirby
1995) as 14 March through 24 November (256 days). Growing season dates have recently been
adjusted by the Regional Supplement to the Corps of Engineers Wetland Delineation Manual:
Atlantic and Gulf Coastal Plain Region (Version 2.0) (USACE ERDC 2010) to match the Natural
Resources Conservation Services' (NRCS) WETs tables; however, the previously established
soil survey growing season dates and Corps 1987 wetland definitions will continue to be used for
this report in order to maintain consistency with baseline years in hydroperiod calculations.
Hydroperiods were statistically compared to explore pre- and post -disturbance differences and
between creek variation. All statistical analyses were performed with Sigma Plot 11.0 using a
simple t-test or a Mann -Whitney Rank sum, depending on results of normality tests and what was
being compared.
Rainfall was recorded by tipping buckets at seven locations in the creek study area,
usually within a study creek basin itself, or at least in near proximity (e.g., Jacobs Creek, PA2,
and Drinkwater Creek use the same rain gauge).
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D. Water Quality Monitoring. Section III. D. was prepared by Dr. Suelen Tullio, a
faculty member of East Carolina University (ECU).
1. History
Water quality monitoring sites on three creek systems were initially established at
the beginning of the creeks monitoring in 1998 as follows: (1) two locations in Jacks Creek, (2)
three locations on Tooley Creek, and (3) four locations on Huddles. These stations were
monitored in accordance with the 1998 plan and continued under the final 2011 plan. By
December 2011, two stations each in seven additional creeks (four control creeks and three
creeks to be impacted) had been added such that ten creeks designated for water quality
monitoring were part of the regular program (no water quality samples have ever been collected
in Muddy Creek). Once the salinity locations were established, collection/submission of water
quality samples was gradual in order for the ECU laboratory to ramp up their analysis and
throughput.
Water quality stations at two locations were added in the following creeks in 2011:
Jacobs Creek- JCBWQ1 near the upstream salinity station and JCBWQ2 near the old railroad
trestle ; Project Area 2 (PA2) - PA2WQ1 at the upstream end of the main channel and PA2WQ2
at the midstream salinity station; Drinkwater Creek- DWWQ1 at the upstream well array and
DWWQ2 near the upstream salinity station; Little Creek- LCWQ1 and LCWQ2, one at each of the
salinity stations; Long Creek- LOCWQ1 and LOCWQ2, one at each of the salinity stations; Porter
Creek- one downstream of the most upstream well array (PCWQ1) and PCWQ2 at the upstream
salinity station; and Duck Creek- DKCWQ1 at the upstream salinity monitor and DKCWQ2 at the
downstream salinity monitor. With the addition of two stations in 2013 on two small unnamed
tributaries (UTs) to Durham Creek (DC11WQ1 and DC19WQ1), all water quality stations north of
NC 33 designated in the study plan are in place. The headwaters of the Durham Creek tributary
DCUT11 will be impacted by the mine continuation and DCUT19 serves as the control. After
consultation with USACE and NCDWR in 2017, one of the S33 Tract creeks permitted to be
impacted was chosen to be monitored (Broomfield Swamp Creek); subsequently a nearby creek
was also included as control (SCUT1). In 2018, limited pre -Mod Alt L data collection occurred at
some locations in Broomfield Swamp Creek and SCUT1; however, as the piers were not
completed and only partial water quality samples were collected, 2019 will be considered the first
pre -Mod Alt L year with complete data for later comparison.
2. Methods
Water quality data were collected from study creeks throughout the year with 26
potential sampling periods; however, insufficient water depth resulted in some sites not being
sampled every period in various years. An annual summary list of samples collected from all
monitoring stations is made each year and included in an appendix in the annual report.
At the 2018 agency/Science Panel meeting to discuss the 2017 report, there was
discussion about consistency of water sample collection locations for analysis. Samples will
continue to be collected as shallow as 1 inch water depths, but with the same precautions
previously followed to avoid substrate collection along with the aqueous sample. In past dry
conditions at the normal collection location, samples were sometimes collected from the closest
pool or puddle in the stream channel and so noted on the data form. Going forward, when the
actual station location is dry, the sample can be collected from up to 20 feet upstream or
downstream, only if that sampled water in the channel is directly connected to receiving waters;
no puddles or pools will be sampled.
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CZR biologists collected field measurements in conjunction with water sample
collections. Field measurements included water depth, Secchi disk depth, temperature, salinity,
conductivity, turbidity, dissolved oxygen, and pH. Water depth was measured to the nearest 0.25
inch in close proximity to the site where water samples were collected and all other measurements
taken. Temperature, salinity, conductivity, dissolved oxygen, and pH were measured with an YSI
85 (early years) and a YSI Pro Plus Quatro (recent years) multi -parameter water quality
instrument. These measurements were made in the middle of the water column when possible.
Turbidity was measured with a Hazco DRT-15 Portable Turbidimeter (early years) and a LaMotte
2020 turbidimeter (recent years). Turbidity water samples were collected in the field and turbidity
was measured at the time of collection. Care was taken to exclude detrital particles from the
substrate and surface in turbidity samples.
The creek water quality samples were collected in polyethylene bottles and driven
directly to the ECU lab in iced coolers at the end of the collection day. At the lab, subsamples
were taken for the various analyses. Pre-combusted Whatman 934-AH (glass fiber) filters were
used to separate particulate and dissolved fractions. The filtrate was stored frozen in a
polyethylene bottle for later analyses of total dissolved phosphorus (TDP), dissolved
orthophosphate (PO4-P), ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), and dissolved
Kjeldahl nitrogen (DKN). The filter pads were also stored frozen for particulate nitrogen (PN),
particulate phosphorus (PP), and chlorophyll a determinations. Fluoride data, although not a
required monitoring parameter for the Creeks study, were included in reports and evaluations
through 2011. Parameters added to the study included dissolved organic carbon (DOC mg L-1)
and total dissolved nitrogen (TDN mg L-1), with preliminary data in 2012 and with the first full year
of data beginning in 2013. Techniques used for these analyses are summarized in Table-D1.
These methods are identical to those used for the PCS Phosphate Pamlico River estuary water -
quality monitoring program (see Stanley 1997 for example).
In order to reduce the amount of information presented in both graphical and table
format in this section, multivariate analysis approach was initiated in 2012 and has continued
since with some modifications. The addition of data from all years sampled - was first analyzed
for the 2013 report and divided into three distinct subsections: temporal variability for all water
quality stations analyzed separately across all years, spatial variability for all water quality stations
across all years, and temporal variability across pre -Mod Alt L and post -Mod Alt L years (CZR
2014 annual report on data through 2013). In the 2013 report (on 2012 data), only the calendar
year data were analyzed and presented with multivariate techniques. In all reports prior to 2013,
data were summarized in up to five standard graphical formats and depicted with box/whisker
plots and standard deviations.
Temporal variability at water quality stations across years was analyzed by
employing a Principal Components Analysis (PCA) to recombine all water quality variables into
principal components that capture the intercorrelation between variables over time. PCA has two
primary uses: 1) to describe interrelationships between a matrix of intercorrelated variables and
2) data reduction, i.e. to reduce a large matrix of intercorrelated variables into linear
recombinations of the original variables. PCA plots all original variables in multidimensional space
(one dimension for each variable), and then fits a regression line through the multiple dimensions.
The principle of least squares is used to fit the regression line and the resulting variance explained
is recombined into a principal component. This principal component (PC) is a combination of the
original variables and explains a fraction of the total variables. This procedure is repeated, i.e. a
new regression line is fitted to the remaining data, and a new principal component is calculated
and generated, until all variability is explained. The PCs themselves are uncorrelated to each
other and therefore may be used in further modeling without violation of regression assumptions.
The PCs can be related to the original variables by examining the loadings on to each PC and
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these values represent the degree of correlation of each original variable to the new PC. In order
to examine how the PCs are related to one another, a biplot is often generated that shows how
the original variables are related to the first two PCs. The final result of PCA is a set of PC values
that represent new variables, made from the original variables, but fewer in number and
uncorrelated to each other. Plotting the PC scores over the course of the years shows the
temporal variability of multiple variables without the need to generate numerous plots. A PCA
analysis was run for each water quality station and the biplot, loadings, and PC time -series are
presented.
Spatial variability among stations across all years was analyzed by comparing the
mean water quality parameters of each water quality monitoring station using a cluster analysis
approach. The approach groups water quality monitoring stations with similar conditions together,
demonstrating the relationship between each station. The benefit of this over multiple years is to
demonstrate how relationships between stations may change over time. Water quality conditions
for each group of stations were then summarized graphically.
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Table D1. Summary of techniques used for chemical and physical measurements.
Parameter
Technique
Reference
Total Dissolved Phosphorus
(TDP)
Ascorbic Acid method
(persulfate digestion
procedure)
EPA (1979); Standard
Methods 4500-P-E (2012);
SmartChem 200 Method 410-
200B (2008)
Particulate Kjeldahl
Phosphorus (PP)
Kjeldahl method
EPA (1979); Standard
Methods 4500-Norg. Nitrogen
(Organic) (2012); SmartChem
200 Method 390-200E and
420-200E (2008)
Dissolved Orthophosphate
(PO4)
Ascorbic Acid method
EPA (1979); Standard
Methods 4500-P-E (2012);
SmartChem 200 Method 410-
200B (2008)
Total Ammonia Nitrogen
(NH4/TAN)
Berthelot method
Solorzano (1969); Standard
Methods 4500-NH3-H (2012);
SmartChem 200 Method 210-
201B (2008)
NO2 + NO3 (N0)
Cadmium Reduction method
Standard Methods 4500-NO3-
E (2012); SmartChem 200
Method 375-100E-1 (2008)
Dissolved Kjeldahl Nitrogen
(DKN)
Kjeldahl method
EPA (1979); Standard
Methods 4500-Norg. Nitrogen
(Organic) (2012); SmartChem
200 Method 390-200E and
420-200E (2008)
Particulate Kjeldahl Nitrogen
(PN)
Kjeldahl method
EPA (1979); Standard
Methods 4500-Norg. Nitrogen
(Organic) (2012); SmartChem
200 Method 390-200E and
420-200E (2008)
Chlorophyll a
Welschmeyer method
(fluorometric analysis)
Welschmeyer (1994); Arar &
Collins (1997); Standard
Methods for the Examination
of Water and Wastewater
(2012); Strickland & Parsons
(1968); Parsons et al. (1984)
Dissolved Organic Carbon
High temperature catalytic
oxidation with non- dispersive
infrared detector
Sugimura and Suzuki (1998)
Total Dissolved Nitrogen
High temperature catalytic
oxidation with
chemiluminescence detector
Walsh (1989)
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Cluster analysis is a multivariate technique that analyzes similarity or dissimilarity
computed from a data matrix. For example, a single water quality monitoring station may be
characterized by multiple water quality measurements. Thus, one might ask: how similar are two
water quality stations from two different creeks based on all of the water quality measurements?
While it is straightforward to compare salinity values between the two creeks, the addition of more
parameters makes the comparison difficult. In order to accomplish this comparison the data
matrix of water quality values has been grouped by station and a dissimilarity matrix was
calculated. This is done by plotting the water quality values for each parameter in multivariate
space. Instead of fitting a regression line, as with PCA, the Euclidean distance is calculated
between the water quality values for each station. Values that are close to each other in space
have low dissimilarity and values that are for apart in space have high dissimilarity. Once the
dissimilarity matrix has been computed, the dissimilarity values for each station may be clustered
and displayed using a dendrogram (tree diagram). Height is typically used as the y-axis for such
a graph and the higher the height of a branch split, the more dissimilar the stations that follow
such a split.
Finally, the interannual variability in water quality variables for those creeks with
pre- and post -Mod Alt L data was compared. Pre- and post -mod Alt-L conditions were compared
using a one-way ANOVA and t scores and p-values are presented. Differences were considered
significant if p-values were < 0.05.
For all statistical analyses of laboratory results, when the limits of detection for a
specific method returned a "< the method detection limit", the "<" was removed and the limit
entered as a value in the spreadsheet. This value is the limit of 100 percent confidence in
detection; while the value may actually be less, if it were present, it would not be higher than this
limit. This practice prevents many "gaps" in the graphical depictions and increases the value of
the statistical analysis.
E. Metals Sampling
1. History
From 1998 to 2010, the PCS sediment samples were analyzed by Dr. John Trefry
at the Florida Institute of Technology (FIT). In 2011 Dr. Trefry informed CZR and PCS that his
workload and research interests would prevent his involvement with future PCS sediment
analyses. To date, a search for an alternate university or commercial laboratory which is both
interested in the work and can duplicate the FIT laboratory report and perform total metals
digestion with either hydrofluoric or perchloric acid has been futile. Commercial facilities
contacted do not digest samples with these acids due to special health, safety, and
instrumentation requirements; university facilities contacted either lacked the ability to commit to
the project or lacked the necessary equipment. An alternate laboratory, SGS North America, Inc.,
now SGS Analytical Perspectives (SGS), in Wilmington NC has performed the metals analyses
since 2011 for the water column and since 2012 for the sediments. In 2016, SGS Analytical
Perspectives acquired the assets of Accutest Laboratories USA and became SGS Accutest-
Orlando. In January 2018, it became SGS North America Inc. —Orlando. The PCS metals sample
analysis was transferred to the Orlando laboratory in 2017. Sediment samples analyzed by SGS
follow sample preparatory Method 3050B under the USEPA SW-846 series, followed by
inductively coupled plasma (ICP) or ICP/mass spectrometry analysis (ICP/MS) by Method 6010C
or Method 6020A. Without the use of the stronger acid(s) used by FIT, the 3050B method is not
a total digestion technique but one that dissolves "elements that could become environmentally
available." The remaining residue after digestion by this method is referred to as "environmentally
inert material"; however, without the stronger acids, the results are not likely to be total metal
concentrations, although the concentration measured is the total amount recoverable by the
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method used. In previous creeks annual reports, the FIT lab provided a summary report with
tables of results and environmental interpretation but no raw data. By their governing standards,
commercial facilities provide a "report" of results with no environmental interpretation or opinion.
From 2013 - 2018, CZR contracted Dr. Jamie DeWitt of ECU Pharmacology and Toxicology to
provide environmental interpretation of the metals data and to serve as reviewer and advisor for
this parameter. Due to other commitments, Dr. DeWitt declined to continue in the role; however,
CZR repeated the analysis and graphics for 2019 as Dr. DeWitt had done previously and CZR
will continue to do so. Expert review or interpretation of any future results will be sought if
necessary.
Elevated metal concentrations in sediments can be evaluated within the context of
potentially harmful biological effects using the guidelines developed by Long et al. (1995). In
these guidelines, two assessment values are listed for several metals. An effects -range -low value
(ERL) and an effects -range -median value (ERM) are defined as the 10th and 50th percentile,
respectively, from an ordered list of concentration of substances in sediments that are linked to a
biological effect. Several authors have noted that sediment quality guidelines should be used
cautiously with an appropriate understanding of their limitations. For example, Field et al. (2002)
noted that the ERL is not a concentration threshold for a chemical in sediment above which toxicity
is possible and below which toxicity is impossible. Instead, according to O'Connor (2004), the
ERL is a concentration "at the low end of a continuum roughly relating bulk chemistry with toxicity."
The ERL and ERM values are applied to the sediments from this study with the caveats listed
above.
2. Methods
Prior to the collection of sediment samples, the water column sample is collected.
Leaning over the bow of the boat while it is slowly underway, using a sample bottle provided by
the laboratory which contains HNO3 preservative, a gloved biologist fills the 1 L sample bottle with
creek water using a separate container which has been cleaned with deionized water and
alconox. The sample bottle is then sealed, labeled, and double bagged. As a back-up sample,
the biologist also collects a 0.5L bottle using the same process. Samples are kept in the dark in
iced coolers and delivered/shipped for receipt at the laboratory using the chain of custody form
provided by the laboratory. The samples are analyzed for concentrations of silver (Ag), arsenic
(As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), molybdenum (Mo), selenium (Se),
and zinc (Zn) (aluminum is not commonly analyzed in marine/estuarine water samples). In 2013,
the sediments samples were also analyzed for total organic carbon (TOC) for the first time.
Standard laboratory protocols and procedures for these analyses are strictly followed.
For sediments, the ponar device is deployed and retrieved from the boat and the
collected sediment is dumped into a plastic tray from which —0.5 gallon of sediment is scooped
from the sample into a Ziploc bag using a plastic or stainless steel scoop. As the sediment sample
is scooped, sediment that may have touched the metal of the ponar is avoided. Each bag is
labeled with creek name and date and, to minimize the potential for leaks, each sample is double
bagged. The ponar itself, plastic tray, and scoop/spoon are thoroughly rinsed with deionized
water between each sample to avoid cross -contamination. A second sample is collected from
each station in case there is a problem with the shipment to the laboratory or a problem
encountered by the laboratory during analyses. The backup samples are kept at CZR until results
of the analyses are completed at which time the samples are discarded. The sediment samples
are delivered chilled to the laboratory. The samples are analyzed for concentrations of aluminum
(Al), silver (Ag), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), molybdenum
(Mo), selenium (Se), and zinc (Zn).
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Since 2016, per 2015 EPA Publication SW-846, certified laboratories replaced
Method 6010C with Method 6010D and Method 6020A with Method 6020B for the ICP or ICP/MS
portion of the analysis. Bulk density of the sediments is measured by ASTM C 29/C 29M by a
subcontractor to the SGS laboratory (CATLIN Engineers and Scientists, Wilmington NC). Total
organic carbon (TOC) measurement by Method 9060A was added to the SGS sediment analysis
in 2013.
For statistical analysis of data, a two-way ANOVA is not possible when only one
sample is collected per creek (no replicate samples) and when a year is not represented with
more than two creeks. While it is possible to treat individual creeks as pseudoreplicates, for some
years there are not a sufficient number of samples from the post -Mod Alt L creeks to perform a
two-way ANOVA with a repeated factor. In lieu of two-way ANOVA, pairwise comparisons (i.e.,
Student's t-tests) were run for individual sediment metals and water column metals; however, due
to the lack of replicates per creek, creeks are treated as pseudoreplicates, which decrease the
quality of the statistical analysis.
For all statistical analyses of laboratory results, when the limits of detection for a
specific method returned a "< the method detection limit", the "<" was removed and the limit
entered as a value in the spreadsheet. This value is the limit of 100 percent confidence in
detection; while the value may actually be less, if it were present, it would not be higher than this
limit. This practice prevents many "gaps" in the graphical depictions and increases the value of
the statistical analysis.
F. Vegetation Monitoring
1. History
Under the 2009 permit conditions, annual vegetation monitoring was no longer
necessary and the Corps suggested a wider interval (approximately every three to five years)
appropriate to each creek after baseline data were collected. In addition to the wider interval that
began in 2010, Hurricane Irene damage in 2011 severely limited access in Tooley Creek and the
main prong of Huddles Cut, both of which were on the calendar to be monitored that year. The
storm damage and high amount of standing water (in the main prong of Huddles Cut) precluded
any monitoring in 2011 in these areas of Tooley Creek and Huddles Cut main prong.
Additionally, the Science Panel agreed at the 2013 annual meeting that of all the
study parameters, vegetation is likely to have the longest "lag effect", the period of time between
impact and the response to that impact; therefore, with the concurrence of the Science Panel, it
was determined that from 2013 forward, vegetation also does not need to be monitored during
the "lag effect", or transition years. A transition year is considered to be the year(s) when the
mine actually moves through a creek basin and also includes the first year after the mine has
completed all impacts to a basin.
The transition years vary depending on the size and shape of the creek basin within
the permitted mine area. While Drinkwater Creek is the first creek tracked with the transition year
approach as described above, because of proximity to the mine advance, baseline data for
Drinkwater Creek were only collected for one year (2011) before 2012 became the first transition
year. After the 2011 survey, it was agreed by the Science Panel that monitoring in Drinkwater
Creek, Tooley Creek, and Huddles Cut would not occur in 2012.
Wetland vegetation data are collected from all non -transition creeks each year that
have had or will have their watershed disturbed by mining activities as well as from four control
creeks-SCUT1, Long Creek, DCUT19, and Duck Creek. The Science Panel and/or agencies will
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determine how many years of post -Mod Alt L data are sufficient in a specific creek or whether
additional gaps in monitoring can occur before all vegetation monitoring ceases in that creek. At
the August 2017 Science Panel meeting (2016 data report review) further clarifications were
discussed about the interval between post -Mod Alt L vegetation surveys. It was agreed that as
originally planned, five years of pre -Mod Alt L vegetation data would be collected when possible,
that four years of post -Mod Alt L data would be collected after the transition year(s), and that after
four years of post -Mod Alt L vegetation data collection the fifth year would be skipped. In a
certified letter sent to USACE and NCDWR on 12 September 2017, PCS made further
clarifications on the frequency and proposed that after the fifth year post -Mod Alt L (the first
skipped year), that vegetation be surveyed every other year until such time that the agencies or
Science Panel members deem otherwise. At the 2018 Science Panel meeting (2017 data report
review), the agencies agreed to the interval described in the PCS September 2017 letter. A copy
of the August 2018 USACE letter of approval for the clarified vegetation monitoring interval was
included in Supplement 1 of the 2018 data report.
In early pre -Mod Alt L years, percent cover and stem counts for small clumps of
Lemna sp. (duckweed) were documented at one transect on the main prong (HMW6) and two
transects (HWW4 and HWW7) on the west prong of Huddles Cut. Duckweed was not noted
during the 2013 and 2014 vegetation surveys. In the summer of 2016, the upstream reaches of
the main and west prongs of Huddles Cut experienced an exponential increase in duckweed;
coverage became thick and was noted on semi-monthly water quality collection data forms. The
decrease in canopy cover and increase in standing water (depth and length of time) after
Hurricane Irene, coupled with low flow, have created favorable conditions for duckweed to grow.
The amount of duckweed in Huddles Cut has increased at both prongs of Huddles Cut since
2016. For the vegetation surveys in 2016, 2017, and 2018, duckweed percent cover was
estimated for the entire transect but stems were not counted. In order to properly compare pre -
and post -Mod Alt L vegetation data, conservative estimates of the percent cover and stem counts
for duckweed were made and 2016, 2017, and 2018 vegetation data was updated to include
duckweed for each transect using field notes taken during the surveys, past data, and leaf surface
area of the largest Lemna species. This inclusion of duckweed into the data caused a change in
dominant herb species, percentages of brackish intolerant dominants, and percentages of
wetland/non-wetland species from data shown in post -Mod Alt L reports up to 2018.
2. Methods
Drainage basin modifications are most likely to affect vegetation communities
found along the relatively narrow riparian wetlands upstream of the CAMA jurisdiction markers.
For this reason, vegetation assessments and monitoring sites were concentrated in these areas.
Data are collected in August or September each monitoring year. Most transects are in the upper
reaches of the creeks, although the distance from the mouths of the creeks differs among the
creeks. Vegetation transects at Jacks Creek, Tooley Creek, Long Creek, and Huddles Cut are
all between 3,000 and 5,000 feet (0.57 mile to 0.95 mile) from the mouth of the creek, Jacobs and
Drinkwater creeks are between 5,000 and 6,000 feet (0.95 mile to 1.14 miles) from the mouth,
Duck and Porter creeks are between 9,000 and 20,000 feet (1.7 miles to 3.79 miles), DCUT11
transects are between 700 to 2,800 feet (0.13 to 0.53 mile), and DCUT19 transects are between
1,900 and 3,800 feet (0.36 and 0.72 mile). Broomfield Swamp Creek transects are between 2,800
and 3,420 feet (0.53 and 0.65 mile) and transects in SCUT1 are between 3,986 and 4,550 feet
(0.75 and 0.86 mile).
In August 1998, vegetation monitoring sites were established in the vicinity of each
of the WL-80 continuous monitors (replaced by Ecotones in 2007 and Level TROLLs in 2011)
located in the riparian wetlands of Jacks Creek, Tooley Creek, and Huddles Cut. Five vegetation
transects were established in Jacks Creek, four in Tooley Creek, and 12 in Huddles Cut. In 2007,
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vegetation monitoring transects were re-established in Huddles Cut, in 2010, transects were re-
established in Tooley Creek, and in 2011, transects were re-established in Jacks Creek.
Additional vegetation monitoring transects were established in 2011 in the vicinity of Level
TROLLs in five new creeks: one vegetation transect was established in Jacobs Creek, one in
Drinkwater Creek, one in Long Creek (control), two in Porter Creek, and four in Duck Creek
(control). In 2013, new vegetation monitoring transects were established in DCUT11 and
DCUT19. In 2018, new vegetation monitoring transects were established in Broomfield Swamp
Creek and SCUT1; but were not monitored until 2019. These last two creeks represent the final
creeks to be monitored under the Mod Alt L permit.
Vegetation surveys focus on the shrub and herb layers. Compared to trees, shrubs
and herbs respond more quickly to changes in salinity and hydrology, and, therefore, provide
better indicators of changes in the vegetation over time. At each vegetation monitoring transect,
10 permanent sample quadrats were established along a 40-meter transect that proceeds on a
random compass azimuth from the well, but may be adjusted to ensure the transect stays in the
riparian floodplain. The quadrats are arranged on alternating sides of the transect axis, such that
each quadrat shares a corner with the quadrat behind it and the quadrat in front of it; however,
no quadrat shares a boundary with any other quadrat. Each sample quadrat consists of a 4-by-
4-meter woody shrub vegetation plot with a 1-by-1 meter herb plot nested in the proximal corner.
These will be used throughout the duration of the study to monitor density, coverage, and species
composition of the herb and shrub strata layers.
Shrubs and woody vines, defined as woody plants greater than 3.2 feet in height
but less than 3 inches in diameter at breast height (DBH), were inventoried in each of the 10 4-
by-4-meter plots located in the vicinity of each well in the riparian wetlands. For each species,
the number of stems present was counted and percent cover estimated. Herbs, defined as all
herbaceous vascular plants regardless of height and woody plants less than 3.2 feet in height,
were inventoried in each of the 1-by-1-meter plots nested within the 4-by-4-meter plots.
Qualitative descriptions of the overstory were made in the vicinity of each electronic well.
An importance value was calculated for each shrub and herb species present in
each transect. Relativized values of average percent cover, average stem count, and frequency
of occurrence in the 10 quadrats were used to calculate importance values. Dominant plants in
each transect were determined by applying the 50/20 rule to the importance values. The 50/20
rule was described in the 1989 wetland delineation manual (Federal Interagency Committee for
Wetland Delineation 1989) and is still used in delineating wetlands (Williams 1992, USACOE
2010). The 50/20 rule uses the quotient obtained from dividing each species' importance value
by the sum of all of the importance values for that transect (shrubs and herbs are treated
separately). This calculation expresses each species' importance value as a percentage of the
cumulative importance value of the entire transect. Beginning with the species having the highest
importance value and continuing in descending order, all species are listed until, cumulatively, 50
percent of the overall importance value has been reached. These species, along with any
additional species that individually comprise at least 20 percent of the overall importance value,
are considered to be dominant.
To further assist in determining whether changes in the plant communities have
occurred, the tolerance of brackish conditions was assessed for each dominant species. The
determination of each species' tolerance was based on habitat descriptions provided in Radford
et al. (1968), Beal (1977), Godfrey and Wooten (1979, 1981), Odum et al. (1984), Eleuterius
(1990), eFloras (2008), and Weakley (2015). A species was considered tolerant of brackish
conditions if any of the habitats listed were brackish, even if most of the habitats were fresh. The
percentage of dominant species intolerant of brackish conditions (viz. solely freshwater species)
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was calculated for each transect. A linear regression analysis was performed for each transect
using Sigma Plot 11.2. Pre- and post -Mod Alt L years were analyzed independently of each other
so that a comparison could be made. Any significant changes in the salinity of the creek should
be reflected by a shift in this percentage. Comparison of variability in the composition of the
vegetative community pre- and post -Mod Alt L was determined using similarity percentages
(SIMPER). SIMPER identifies the percent contribution of each individual species contributing to
dissimilarity (or similarity) between groups (in this case pre and post), hence, it detects the species
that are most important in causing the known dissimilarity (or similarity).
Another analysis was performed using the wetland indicator status (Lichvar et al.
2016) of the dominant plants. Indicator status categories: obligate wetland (OBL), facultative
wetland (FACW), facultative (FAC), facultative upland (FACU), and obligate upland (UPL). The
percentage of dominant species with a wetland indicator status of FACU or drier was calculated
for each transect but linear regression analysis was not performed for this parameter. For this
report, plant species with an indicator status of OBL, FACW, or FAC are considered wetland
species and species with an indicator status of FACU or UPL are considered non -wetland
species. Any major change toward drier conditions should be reflected by a change in this
percentage.
G. Fish Monitoring
1. History
Monitoring of fish assemblages occurred in Jacks Creek, Tooley Creek, Muddy
Creek, and Huddles Cut under the 1998 plan. Under the 2011 plan, fish assemblage monitoring
was expanded to include seven new creeks in 2011 and two additional creeks (DCUT11 and
DCUT19) in 2013. In 2018/2019, the final creek under the 2011 plan, Broomfield Swamp Creek
(and its control, SCUT1), were added. The geomorphic characteristics of the seven new study
creeks were described in the 2011 PCS Creeks Report (CZR et al. 2012), the geomorphic
characteristics of the two DCUTs are described in Section I-B of the 2013 report, and Broomfield
Swamp Creek and SCUT1 geomorphology were described in Supplement 2 of the 2018 report.
2. Methods
If a monitored stream is too shallow and narrow to sample using a trawl, fyke nets
are used to sample fish (Huddles Cut, DCUT11, and DCUT19). Each fyke net sample event is
conducted using two fyke nets (one net fished upstream and one fished downstream) anchored
for a set time of approximately 16 hours (late afternoon until the following morning). Each fyke
net is deployed across the entire width of the sampled stream and consists of 0.25-inch mesh net
with four 21-inch hoops, a 6-inch throat, and a 22-foot wingspan.
For monitored streams large enough to trawl, each fish trawl sample is conducted
using a two -seam otter trawl. The trawl was constructed with a 10.5-foot head rope, 0.25-inch
bar mesh wings and body, and 0.12-inch bar mesh cod end. The trawl is towed for approximately
one minute, covering approximately 75 yards, from a beginning point marked in the field with
flagging (fixed GPS coordinates) near the mouth of each monitored creek. Tow direction is always
toward the creek mouth.
All fish captured by either method are identified and counted. Species that are
identified in the field are released; unidentified fish are preserved for later identification. Total
length is measured to the nearest millimeter for the first 30 specimens of each species.
Representative photographs of sample stations are taken during the first sample event and on
other occasions and are on file with CZR Incorporated.
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Water quality data are collected with a YSI Pro Plus Quatro multi -parameter
instrument prior to deployment and retrieval of fyke nets and/or before each trawl at each creek.
Parameters measured include temperature, dissolved oxygen (DO), salinity, conductivity, pH, and
both Secchi depth and water depth. In the deeper creeks, excluding Secchi depth, measurements
for all other water quality parameters are taken at both surface and near bottom levels. Estimates
of the percentage of the water surface covered by submerged aquatic vegetation (SAV) are also
made. Water quality data are examined with regard to how well each site provided habitat
appropriate for the preservation of fish communities. Particular attention is given to dissolved
oxygen, as low DO levels are commonly implicated in fish kills.
3. Statistical Methods
Multivariate cluster analysis among creeks/years analyzed spatial variability of fish
species composition and abundance. Fish data are transformed to reduce the multiplicity of
variance among creeks/years and assembled into a Bray -Curtis dissimilarity matrix. Bray -Curtis
has the advantage of incorporating not just the presence of similar species but also the relative
abundance of species found between sample locations and is therefore a comprehensive
descriptor of community similarity. Bray -Curtis (Bray and Curtis 1957; Clarke et al. 2006) is
defined as:
Cbc= 100*1- Ei I Yn-Yi2 I, where
Ei(yil+yi2)
y, and y2 are the total number of the itb species at site 1 and 2.
The resulting dendrogram created from the Bray -Curtis dissimilarity matrix is
tested for significant cluster/group differences among fish assemblages using a similarity profile
test (SIMPROF) at the 5 percent level (P = 0.05) for individual creeks and the 1 percent level (P
= 0.01) when all creeks are included in the matrix. Comparison of variability between fish
assemblage clusters/groups is determined by means of similarity percentages (SIMPER).
Similarity percentages (SIMPER) identifies the percent contribution of each individual fish species
contributing to dissimilarity (or similarity) between clusters/groups, hence, it detects the fish
species that are most important in causing the known dissimilarity (or similarity).
Analysis of similarity (ANOSIM) is a nonparametric test that is used to compare
interannual variation in fish species abundance and composition to detect spatial differences
between pre- and post -Mod Alt L years for creeks with drainage basin reduction and related
control creeks. Significance was set at 0.05.
A two -sample t statistic (t-Test) is used to compare interannual variation in
individual fish species catch -per -unit -effort (CPUE) and abundance to detect spatial differences
between pre- and post -Mod Alt L years for creeks with drainage basin reduction and related
control creeks. Significance is set at 0.05. When normality failed and the data do not meet
assumptions for a parametric test, a nonparametric test is used (Mann -Whitney Rank Sum Test).
Biota and/or environmental matching (BEST) using the BIOENV method was used
to analyze the relationship between the multivariate fish data and both in -situ collected
hydrographic data and ECU water quality data. As with fish data, water quality data are also
transformed to reduce the multiplicity of variance among creeks/years. Following transformation
of the water quality data, Euclidean distance is calculated between the water quality variables and
a dissimilarity matrix is formed. BIOENV then calculates all possible combinations of variables
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between the two dissimilarity matrices; biota and/or environmental matching (BEST) at this
moment displays the environmental variables that best correlate to the multivariate fish
assemblage clusters/groups.
Fish Guild Analysis
Each species caught in trawl or fyke nets was assigned to one of six trophic guilds:
zoobenthivore, zooplanktivore, piscivore, herbivore, omnivore, or detritivore (see Table 1 for each
species' designation). For all species, the predominant prey item was chosen in order to assign
species to guilds and, for most species, guilds were assigned by information available on
FishBase (Froese and Pauly 2016). Definitions for guilds are as follows (from Elliot et al. 2007):
• zoobenthivore — feed on invertebrates associated with the substratum;
• zooplanktivore — feed on zooplankton, including crustaceans, fish eggs,
and fish larvae;
• piscivore — feed on finfish and large invertebrates (e.g., squid);
• herbivore — feed on plants, including algae and phytoplankton;
• omnivore — feed on both plants and animals; and
• detritivore — feed on detritus.
Information for six species (chain pipefish, mud sunfish, naked goby, redear
sunfish, sheepshead minnow, and swamp darter) was not available on FishBase. Guild
designations for these fish were assigned based on previously published scientific literature.
These six fish species represented about 1.5 percent of the total catch over all years.
For each sample event, the number of fish in each guild was determined. Then,
relative abundance of each guild was calculated by dividing the number of fish in each guild by
the total number of fish caught in that particular sample. Relative abundance was used as
opposed to total abundance in order to focus on compositional changes in community structure
through time rather than changes in total numbers.
Statistical analysis of the guilds proceeded in a similar manner as the fish and
benthos sections of previous reports. For each creek, a Similarity Profile (SIMPROF) was used
to determine clusters among the different years. Similarity Percentage Analysis (SIMPER) was
then used to determine how clusters differed. For the impacted creeks that have pre- and post -
Mod Alt L data, an Analysis of Similarity (ANOSIM) was used to determine if trophic guild
designation differed based on Mod Alt L status. Through the 2016 data collection year, or creeks
with more than five years of data, a Biota and/or Environmental Matching (a.k.a. BEST routine)
was conducted to determine the set of environmental variables most important in fish trophic guild
structure. In 2017, the BEST routine was used again but if a strong correlation (Spearman rank
correlation > 0.60) with one or more environmental variables was displayed, a multivariate
analysis of variance (MANOVA) was used to determine the significance of the variables (p < 0.05).
H. Benthos Monitoring
1. History
Monitoring of benthic macroinvertebrate communities occurred in Jacks Creek,
Tooley Creek, Muddy Creek, and Huddles Cut under the 1998 plan and was expanded to include
seven of the new creeks identified under the 2011 plan; two more creeks (DCUT11 and DCUT19)
were first monitored for benthos in 2013. In 2018, Broomfield Swamp Creek in the S33 Tract
A-17
portion of the Mod Alt L permit and its control creek, SCUT1, were added into the study for a total
of 14 creeks; full monitoring began in these two creeks in 2019.
In 2019, Mr. Larry Eaton of Eaton Scientific LLC was contracted to review the
benthic sections of the report, provide ecological interpretation, and attend the Science Panel
meeting. As mentioned below, prior to his retirement from the NC Division of Water Resources in
2017, Mr. Eaton assigned the tolerance values to those benthic species encountered in the PCS
creeks study for which enough data existed in the state database to assign a value.
2. Field Methods
a. Macrobenthos Sweeps
Timed sweep methodology is used to sample benthic macroinvertebrates
along the shoreline at each upstream and downstream station in the monitored creeks following
NCDENR, NCDWR standard operating procedure (SOP) for timed sweep methods (NCDENR
2006). The timed sweep samples consist of 10-minute collections with a D-frame net in
representative shoreline and near -shore habitats. Water quality data are collected with a YSI Pro
Plus Quatro multi -parameter instrument during the sweep sampling. Parameters measured
include temperature, dissolved oxygen (DO), salinity, conductivity, pH, and both Secchi depth and
water depth. In the deeper creeks, excluding Secchi depth, measurements for all other water
quality parameters are taken at both surface and near bottom levels. Water quality data are
examined with regard to how well each site provided habitat appropriate for the preservation of
benthic macroinvertebrate communities.
Within each sampling station, three replicate samples are collected from
the same three locations as in previous years. Sweep contents are sieved in the field through a
0.5 mm mesh screen, preserved and returned to the laboratory for benthic sorting, enumeration,
and identification to the lowest practical taxonomic level (usually species). Each replicate is
enumerated, and the mean number of individuals per taxa is calculated for all taxa collected at
each station. An Estuarine Biotic Index (EBI) is calculated for each upstream and downstream
station in each creek (NCDENR 1997):
EBI = E(SV;*n ), where
N
SV; is the ith taxa's sensitivity value,
n; is the ith taxa's abundance value (1, 3, 10, 30, or 100),
N is the sum of all abundance values.
An EBI is a mean of the water quality sensitivity values for each taxon in the sample, weighted by
abundance values of the taxa. Only those taxa for which sensitivity values are available can be
used to calculate an EBI. An EBI can be used at all salinities to make comparisons and assess
differences in site water quality (NCDENR 1997). Theoretically, an EBI can range from a low of
one to a high of five. A high EBI indicates many intolerant taxa and good water quality at a
location, while a low EBI is indicative of stressed conditions (NCDENR 1997). All sensitivity
values for taxa encountered between 2000 through 2005 and 2009 through 2016 were verified
and assigned by Larry Eaton, Surface Water Protection, NCDWQ in an effort to standardize and
evaluate benthic data from past study years; however, Larry Eaton retired in 2017 and no
tolerance values have been assigned since his retirement. To date, there is no one within NC
Division of Water Resources (NCDWR, formerly Division of Water Quality or DWQ) with
equivalent estuarine expertise to continue to make assignments.
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b. Macrobenthos Ponars
Five ponar grabs are taken from a boat near mid -stream at each upstream
and downstream station in all 15 creeks in this study. Little Creek, Duck Creek, DCUT11,
DCUT19, and SCUT1 upstream stations and both upstream and downstream stations at Huddles
Cut are not accessible by boat; therefore, a manual ponar grab method was used to obtain the
five samples. Just as with benthic sweeps above, basic hydrographic data are collected at each
ponar sample station.
Collected sediments are placed in 1 gallon plastic bags, with a full bag
constituting a sample. Samples are then sieved in the field through a 0.5 mm mesh screen. All
material retained on the screen are preserved and returned to the laboratory for benthic sorting,
enumeration, and identification to the lowest practical taxonomic level (usually species). For each
taxon, the mean number of individuals per grab is calculated. An EBI is calculated for each
sampling station based on total individuals of each taxon collected from all five replicate grabs.
3. Statistical Methods
Multivariate cluster analysis
Multivariate cluster analysis among creeks/years/stations analyzed spatial
variability of taxa richness and abundance for both sweep and ponar collections. Benthic data
are transformed to reduce the multiplicity of variance among creeks/years/stations and assembled
into a Bray -Curtis dissimilarity matrix. Bray -Curtis has the advantage of incorporating not just the
presence of similar taxa but also the relative abundance of taxa found between sample locations
and is, therefore, a comprehensive descriptor of community similarity. Bray -Curtis (Bray and
Curtis 1957; Clarke et al. 2006) is defined as:
Cbc= 100*1- Ei I Yi,-Y;2 I, where
�i(yi +y12)
y, and y2 are the total number of the itb species at site 1 and 2.
The resulting dendrogram created from the Bray -Curtis dissimilarity matrix is
tested for significant cluster differences among benthic macroinvertebrate communities using a
similarity profile test (SIMPROF) at the five percent level (P = 0.05). Comparison of variability
between benthic macroinvertebrate community clusters/groups is determined by means of
similarity percentages (SIMPER). Similarity percentages (SIMPER) identifies the percent
contribution of each individual taxon contributing to dissimilarity (or similarity) between
clusters/groups, hence, it detects the taxa that are most important in causing the known
dissimilarity (or similarity); however, such comparisons of statistical significance must be made
with care since very abundant taxa were not fully enumerated (counts stop at 100 for each sweep
and ponar grab samples).
Analysis of similarity (ANOSIM) is a nonparametric test that is used to compare
interannual variation in taxa abundance and composition to detect spatial differences between
pre- and post -Mod Alt L years for creeks with drainage basin reduction and related control creeks.
Significance was set at 0.05.
Biota and/or environmental matching (BEST) using the BIOENV method was used
to analyze the relationship between the multivariate benthos data (both sweep and ponar) and
both in -situ collected hydrographic data and ECU water quality data. As with benthos data, water
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quality data are also transformed to reduce the multiplicity of variance among creeks/years.
Following transformation of the water quality data, Euclidean distance is calculated between the
water quality variables and a dissimilarity matrix is formed. BIOENV then calculates all possible
combinations of variables between the two dissimilarity matrices; biota and/or environmental
matching (BEST) at this moment displays the environmental variables that best correlate to the
multivariate benthic macroinvertebrate community clusters/groups.
In addition to the dendrogram created from the Bray -Curtis dissimilarity matrix for
ponar collections, Shannon -Wiener diversity index (H') (Shannon 1948), is also used to detect
spatial differences in species diversity in ponar samples collected at all 13 creeks. The index is
defined as:
H'=- Ep; Inp;, where
n;= number of individuals of the it" species,
N= total number of individuals of all species, and
pi= n/N.
Fuzzy Correspondence Analysis
For each species collected in the ponar grabs, two different characteristics were
analyzed: trophic level and functional feeding guild. Trophic level describes the position of a
species in a food chain and consisted of four levels: detritivore, herbivore, carnivore, and parasite.
Functional feeding guild describes the mechanism by which each species acquires food
resources and/or nutrients and consisted of six categories: gatherer/collector, filterer/collector,
scraper, grazer, shredder, and predator.
Each species ever collected in a ponar grab was assigned a number between 0
and 3 for each of the four trophic level categories and each of the six functional feeding guild
categories. A '0' indicated that the species had no affinity to that particular category whereas a
'3' indicated that the species had high affinity to that particular category. An advantage to this
approach is that a species could be assigned nonzero values in multiple categories, which
addresses species that are omnivorous (e.g., herbivore and carnivore), or species that display
multiple or facultative feeding modes (e.g., predator and gatherer/collector). Omnivory and
multiple feeding modes are extremely common among benthic macroinvertebrates.
Prior to species designations into categories, several sources were consulted
including the Environmental Protection Agency (EPA), the United States Geological Survey
(USGS), and the Georgia Environmental Protection Division (GAEPD). Peer -reviewed scientific
publications were also consulted for species not covered in the aforementioned three sources.
Final species designations were reviewed by Dr. Larry Eaton of the North Carolina Department
of Environmental Quality Division of Water Resources (NCDEQ DWR) and any necessary
adjustments were made. Wendell Pennington of Pennington Associates, Inc. also made
suggestions.
The list of species designations was analyzed using a fuzzy correspondence
analysis (FCA). An FCA is similar to an ordinary correspondence analysis except that species
can have membership in multiple categories. The outputs of an FCA are standardized, normally
distributed scores for a certain number of axes for each species. Each axis represents some type
of differentiation of the categories. For example, a particular axis of the FCA may differentiate
parasite from all the other trophic levels by assigning species that are parasites a relatively large
negative (or positive) number. For the initial FCA analyses, a total of six axes were produced
which differentiated the other trophic level categories and functional feeding guild categories;
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however, a different number of axes could be selected. Thus, each species was described by a
separate score for the different axes, each score describing a different combination of trophic
level and/or functional feeding guild.
To characterize the trophic level and functional feeding guild of the benthic
macroinvertebrate community of each creek, each species' FCA score on each of the axes was
multiplied by its relative abundance for each ponar grab conducted in the creek. Relative
abundance is computed as the abundance of a particular species divided by the total abundance
of the sample. Species with 0 abundance are given a relative abundance of 0. A sum of the FCA
scores weighted by the relative abundance of each species was obtained. This process was
repeated for each of the axes from the FCA. Thus, the benthic macroinvertebrate community of
every ponar grab across all creeks, years, locations (upstream vs. downstream), and sample (1-
5) is described by this summed value for each of the FCA axes. For example, a ponar grab
sample that has a relatively large negative or positive number for a particular axis relative to the
others indicates that the benthic macroinvertebrate community was dominated by that particular
category (either a trophic level or a guild depending on the particular axis). These summed
weighted FCA scores were used in subsequent statistical analyses.
Spatial and Temporal Variability of Benthic Guilds
Multivariate cluster analysis among creeks/years/locations analyzed spatial
variability of benthic trophic level/functional feeding guild scores of all six FCA axes. The scores
were assembled into a Euclidean distance matrix.
The resulting dendrogram created from the Euclidean distance matrix is tested for
significant cluster differences among benthic trophic level/functional feeding guild scores by a
similarity profile test (SIMPROF) at the five percent level (P = 0.05; in some years, a more
conservative threshold may be set). Comparison of variability between benthic trophic
level/functional feeding guild clusters/groups is determined by similarity percentages (SIMPER).
Similarity percentages (SIMPER) identifies the percent each axis contributes to the distance
between clusters/groups, hence, it detects the FCA axes that drive the known distance; however,
such comparisons of statistical significance must be made with care since very abundant taxa
were not fully enumerated (counts stop at 100 for each ponar grab sample).
Analysis of similarity (ANOSIM) is a nonparametric test used to compare
interannual variation in benthic trophic level/functional feeding guild scores to detect spatial
differences between pre- and post -Mod Alt L years for creeks with drainage basin reduction and
related control creeks. Significance was set at 0.05 (in some years, a more conservative threshold
may be set). As done for the fish guilds, in 2017, the BEST routine was used for the benthic
guilds to determine environmental variables of importance to guild structure; if a strong correlation
(Spearman rank correlation > 0.60) with one or more environmental variables was displayed, a
multivariate analysis of variance (MANOVA) was used to determine the significance of the
variables (p < 0.05).
Mixed -model analysis of Pre- and Post -Mod Alt L Creeks
A fully factorial mixed -model analysis of variance (ANOVA) was used to determine
how macroinvertebrate guilds in impacted creeks have changed in post -Mod Alt L years, and how
any changes in guild structure compared to control creeks over identical time -frames. Separate
models were run for each of the six axes from the FCA described above and for each impact -
control creek comparison. For each model, the FCA axis was the dependent variable, Creek and
Mod Alt L status were fixed independent variables, and Ponar subsample was a random
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independent variable. Pre- and post -Mod Alt L status for the control creek sample years were
assigned to match those of the impacted creek. Five ponar subsamples were taken in each year
for all creeks, thus these subsamples were treated as a random effect in order to avoid
pseudoreplication. Each year of sampling was treated as a true replicate. Models were fit with
the Ime() function in R, and the anova() function was used to conduct null hypothesis testing.
Alpha values for determining the significance of main and interaction effects were
set at 0.05 (in some years, a more conservative threshold may be set). For each model, the
significance of the interaction between Creek and Mod Alt L Status was determined. A significant
interaction indicated that the change in benthic macroinvertebrate guild composition (i.e.,
depending on the particular guild Axis examined) from pre- to post -Mod Alt L differed between
the impact and control creeks. This interaction would indicate that the macroinvertebrate
community in impacted creeks responded differently than a control creek to a change in Mod Alt
L status. Although less relevant, significance of the main effects of Creek and Mod Alt L Status
was also determined. A significant Creek effect indicated that the two creeks differ in the particular
guild Axis analyzed, and a significant Mod Alt L Status indicated that pre -Mod Alt L Status guild
structure was different than post -Mod Alt L Status guild structure among both creeks combined.
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