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
βSal values reflected increases in salinity (Figure 6c)
while the βFC 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.