-------
24 SAV TECHNICAL SYNTHESIS I
1996). A further concern in applying models devel-
oped for lake SAV communities to estuaries is that the
Secchi depth values in lakes tend to be much larger
than those for Chesapeake Bay. Data on depth and
colonization are quite sparse for the conditions of
interest for Chesapeake Bay (water depths 3 meters,
Kj 1.5 m4). The resulting models were constructed to
fit data that were generally different in range from
those data for which inferences about light require-
ments for Chesapeake Bay SAV need to be drawn.
Freshwater and Oligohaline SAV
A series of papers modeled the relationship between
light and maximum depth (Zmax) of freshwater species,
as shown in Figure III-l. Canfield et al. (1985) devel-
oped a best-fit model for predicting Zmax from Secchi
depth, based on data from lakes in Finland, Florida and
Wisconsin. In the case of the Florida lakes, Secchi
depth was determined once during the peak of SAV
abundance. Chambers and Kalff (1985) developed
regression models to predict Z^ using original data on
maximum colonization depths and Secchi depth from
lakes in southern Quebec and literature values from
throughout the world (only the global model is shown
in Figure III-l). Duarte and Kalff (1987) examined the
effect of latitude on Zmax and maximum biomass of
SAV in lakes using a data set that included subtropical
and tropical lakes, Secchi depth and light attenuation
coefficient (Kj) converted to Secchi depth.
Unlike the three studies cited above, which were
related to Secchi depth, Vant et al. (1986) developed a
relationship between monthly measurements of K^
and maximum depth of colonization in nine New
Zealand lakes. Kj was converted to Secchi depth using
an equation derived from data given in their text
(Table 1-Secchi depth = 1.96/ IQ ) to give the line
shown in Figure III-l. They calculated the mean
annual irradiance at Zmax (where Kj was available) and
found it to be in the range of 1-17 ^umol m~2 s"1, com-
parable to light compensation points determined for
freshwater SAV species by laboratory studies. They
compared their data with other studies that used Sec-
chi depth (Canfield et al. 1985; Chambers and Kalff
1985), and found that Zmax, as calculated using these
equations in the references, was invariably smaller
than that observed in New Zealand lakes, and irradi-
ance, as a percent of the subsurface value, was
much higher. They suggest this might be an effect of
' Canfield
Chambers and Kalff
Duarte and Kalff
' M&M-Caulescent angiosperms, nonlinear
M&M-Caulescent angiosperms, linear
Vant et al.
c
,o
*3
N
C
JO
o
o o>
JC 0)
Q
£
3
E
*x
<0
5
4
3
2
1
0
-1
0.5 1 1.5 2 2.5
Secchi Depth (meters)
FIGURE 111-1. Modeled Relationships of Maximum
Depth of Colonization to Secchi Depth for
Freshwater Lake SAV Species. Relationship of maxi-
mum depth of colonization (Zmax) to Secchi depth for
freshwater SAV species as modeled by Canfield et al.
(1985), Chambers and Kalff (1985), Duarte and Kalff
(1987), Middleboe and Markager (1997) and Vant era/.
(1986).
latitude. Note that the major differences in Zmax from
these studies appear at Secchi depths > 2 meters
(Figure III-l).
Middleboe and Markager (1997), working with data
from freshwater lakes in the United States, Denmark
and other countries, worked out both linear and non-
linear models for estimating Zmax from Secchi depth
for caulescent angiosperms, tall macrophytes with a
distinct stem and long internodes, similar to most of
the freshwater and oligohaline species in Chesapeake
Bay (Figure III-l). They also modeled rosette-type
angiosperms, plants with short, stiff leaves from a
basal stem (the isoetids and other species), most of
which grow in mats in shallow water and become
emergent during the growing season (Likens 1985),
but these plants are generally not found in Chesa-
peake Bay. It appears that Middleboe and Markager
(1997) used data on V. americana from Lake George
-------
Chapter III - Light Requirements for SAV Survival and Growth 25
(Sheldon and Boylen 1977) for their analysis, although
this species is quite different from the other species in
this group. Middleboe and Markager (1997) plotted
percent surface irradiance at Zmax for those data for
which they had K^ (Figure III-2). In order to compare
their studies with others, they also calculated percent
light at Zmax from Secchi depth by converting Secchi
depth to Kj using a conversion factor of 2.02 (not shown
in Figure III-2). These calculations yielded much higher
average percent light values at Zmax than those based on
Kj values. They pointed out that these considerations
demonstrate that it is difficult to draw conclusions
about light conditions in the water column only from
measurements of Secchi depth. Their percent light val-
ues would have been even higher if they had used the
Chesapeake Bay Secchi depth to Kj conversion factor
of 1.45. They suggested that a nonlinear relationship
between Secchi depth and Zmax was more appropriate
than a linear relationship, even though the r2 values
were comparable, indicating the models explained
about the same amount of variance (—55 percent).
Figure III-l shows a good correspondence among
models. For lake species in general, a depth of 1 meter
would be colonized when Secchi depth = 0.4 to 0.7
meters. The 0.4- to 0.7-meter range is comparable with
the light constraints mentioned by Carter and Rybicki
10
x
re
N
to
O)
Caulescent angiosperms
FIGURE III-2. Percent Surface Light at Zmax/Kd
Relationship for Freshwater Lake SAV Species.
Relationship of percent surface light at maximum depth
of colonization (Zmax) to light attenuation coefficient (Kd)
for freshwater lake SAV species (Middleboe and
Markager 1997).
in Batiuk et al. (1992). Although not considering a tar-
get depth of 1 meter, they suggested that when median
seasonal Secchi depths were 0.7 meters, SAV beds
would increase in size, whereas at Secchi depths 0.5
meters, revegetation would not occur. Between 0.5
and 0.7 meters, other factors, such as epiphyte loading,
available sunshine, size and number of tubers set in the
previous year, etc., play a role in determining survival.
Batiuk et al. (1992) made a distinction between
meadow-forming and canopy-forming species in
developing light requirements; V americana and Z.
marina were singled out as meadow-forming species.
The distinction between meadow-forming and canopy-
forming species, however, blurs at low tide, when all
species, including V. americana and Z. marina, can
form a canopy, and at high tide in the tidal rivers, when
even H. verticillata and M. spicatum are well below the
water surface. Vamericana'?, light requirements do not
appear to be very different from those of the canopy-
forming species. In fact, V. americana populations in
the tidal Potomac River appear to be more tolerant of
poor light conditions and persist after canopy formers,
such asH. verticillata, disappear (Carter et al. 1994).
Mesohaline and Polyhaline SAV
Models also have been prepared for several mesoha-
line to polyhaline SAV species (seagrasses), mostly
using Secchi depth to measure light (Figure III-3). All
four of these models converge where Secchi depth
equals two meters and diverge at Secchi depths above
and below this value. Vincente and Rivera (1982)
found a significant positive correlation between mean
Secchi depth and lower depth limits of Thalasia tes-
tudinum in Puerto Rico. Duarte (1991a), working with
worldwide data, used K^ measurements from the liter-
ature or converted Secchi depth to K^ Duarte then
developed a relationship between K^ and Zmax for sea-
grasses, reporting that SAV extends to depths receiv-
ing, on average, 11 percent of surface light. Oleson
(1996) also developed a relationship between Secchi
depth and Zmax for Z. marina in Denmark. Dennison
(1987) found a relationship between Kj and Zmax and
then developed a relationship between Zmax and Sec-
chi depth by using the Poole and Atkins relationship,
Kj = 1.7/Secchi depth. Dennison reported that the
maximum depth limit for Z. marina is approximately
equivalent to the Secchi depth, or about 10 percent
-------
26 SAV TECHNICAL SYNTHESIS !
N
o
JC
4-1
Q.
<1>
O
E
x
CO
3.5
3
2.5
2
1.5
1
0.5
— Dennison
— Oleson
— Duarte
• - - Vincents & Rivera
0 0.5 1 1.5 2 2.5 3
Secchi Depth (meters)
FIGURE III-3. Relationship of Maximum Depth of
Colonization/Secchi Depth for Polyhaline SAV
Species. Relationship of maximum depth of coloniza-
tion (Zmax) to Secchi depth for polyhaline SAV species
as modeled by Dennison (1987), Oleson (1996), Duarte
(1991 a) and Vincente and Rivera (1982).
c
O
Jp
N
C
.O
o
o
Q.
Q
E
X
(0
12
10
_ 8
12
«6
E
Vant et al.
-^— Dennison
— — Duarte
Species
0 0.5 1 1.5 2 2.5 3 3.5
Light Attenuation Coefficient (m 1)
FIGURE III-4. Comparison of Polyhaline and
Freshwater Lake SAV Species' Maximum Depth of
Colonization/Kd Relationships. Comparison of the
relationship between maximum depth of colonization
(Zmax) and light attenuation coefficient (K^ for polyhaline
and freshwater lake species as modeled by Vant et al.
(1986), Dennison (1987) and Duarte (1991 a).
surface light, but calculations using his equations give
19.8 percent at Zmax.
Comparison of Freshwater I Oligohaline Species
with Mesohaline/Polyhaline Species
Based on these reported models, it is possible to con-
clude that there is a significant difference in minimum
light requirements for freshwater-oligohaline SAV
species and meadow-forming mesohaline-polyhaline
SAV species. In order to compare the models for these
two sets of species, it is informative to look at models
based on Secchi depth and Kd, separately. Only
Dennison (1987) and Vant et al. (1986) developed a
relationship between Kj and Zmax using original,
unconverted Kj data (Figure III-4). The relationship
developed by Duarte (199la) using a conversion is also
plotted in Figure III-4. For any specific light attenua-
tion coefficient, the maximum depth of colonization is
much greater for freshwater and oligohaline species,
suggesting that a higher percent of surface light is nec-
essary for mesohaline and polyhaline species survival
and growth.
Figure III-5 compares the models based on Secchi
depth for freshwater/oligohaline species by Vant et al.
(1986), Duarte and Kalff (1987), and Middleboe and
Markager (1997) with those for mesohaline/polyhaline
species by Oleson (1996) and Vincente and Rivera
(1982). Both Middleboe and Markager's (1997) linear
and nonlinear equations are shown, although the non-
linear equation is preferred. Except for the linear equa-
tion of Middleboe and Markager (1997), as Secchi
depth increases, the colonization depths for mesoha-
line/polyhaline species and meadow-forming angio-
sperms diverge further from those for the freshwater/
oligohaline species—it appears that the latter grow to
greater depths, given the same amount of light. Esti-
mates of percent light available at Zmax for freshwater
and oligohaline species from the models range from 1.3
percent of surface light to <30 percent of surface light
(Table III-4). The estimates of percent light at Zmax for
mesohaline and polyhaline species range from 10.4 per-
cent to 18.8 percent of surface light.
-------
Chapter III - Light Requirements for SAV Survival and Growth 27
Duarte and Kalff
-^— M&M-Caulescent angiosperms, nonlinear
— — Oleson
Vincente and Rivera
Vant et al.
M&M-caulescent angiosperms, linear
C
o
*5
(0
N
C
o
o
o
0)
Q
x
tO
Freshwater
Lake
Species.
x Lake (M&M,
linear)
' ' —•*- **Polyhaline
"" ^ Species
0 0.5 1 1.5 2 2.5
Secchi Depth (meters)
FIGURE MI-5. Comparison of Polyhaline and Fresh-
water Lake SAV Species' Maximum Depth of Coloni-
zation/Secchi Depth Relationships. Comparison of the
relationship between maximum depth of colonization
(Zmax) and Secchi depth for polyhaline and freshwater
lake SAV species as modeled by Duarte and Kalff
(1987), Middleboe and Markager (1997), Oleson (1996),
Vincente and Rivera (1982) and Vant et al. (1986).
Examination of the four types of evidence for SAV
light requirements discussed above—photosynthesis
irradiance curves, field observations, light manipula-
tion and models—leads to the conclusion that the mod-
els represent the best source of comparative
information for developing light requirements for
Chesapeake Bay. The shading experiments, although
they do not help to refine the light requirements, offer
insight into the complexity of plant success under
reduced light conditions. The published literature does
not provide the specific numbers for Chesapeake Bay
SAV light requirements, but can be used to guide deci-
sions and suggest limiting factors. In the section below,
we briefly present some of the factors that must be con-
sidered in determining light requirements for Chesa-
peake Bay, along with the results of recent analyses
conducted in the Potomac and Patuxent rivers, which
support the light requirements recommended later in
this report.
DETERMINATION OF MINIMUM LIGHT
REQUIREMENTS FOR CHESAPEAKE BAY
Factors To Be Considered in Determining
Minimum Light Requirements
Lack of Literature Concerning Many Species
in Estuarine Environments
Although there is an abundance of literature about the
relationships between light availability and SAV distri-
bution in freshwater lakes and polyhaline environ-
ments, there is relatively little such information on
SAV in tidal fresh, oligohaline and mesohaline estuar-
ine environments. These environments are often char-
acterized by high turbidity, tidal fluctuations, variable
salinity and high-energy events (e.g., wind and waves).
If information from freshwater lake SAV studies is
used to guide the selection of light requirements for
the bay, it is especially important that Chesapeake Bay
research and monitoring results be used to adapt and
fine-tune these requirements.
Uncertainties in the Relationship between
Secchi Depth and Kd
In tidal waters there is high variability in Kj because
estuarine water columns are highly variable in time
and space. Zimmerman et al. (1994) have shown that
the daily light integral is not well approximated by
sinusoidal theory. There is considerable uncertainty
regarding the conversion of Secchi depth to K^ (e.g.,
Kirk 1994; Giesen et al. 1990). Table III-5 lists some of
the conversion factors found in the literature. Some
authors have discussed the inconsistencies introduced
by conversion (e.g., Vant et al. 1986; Middleboe and
Markager 1997). Chesapeake Bay Water Quality Mon-
itoring Program Secchi depth measurements are made
to the nearest decimeter, thus rendering these meas-
urements insensitive in very turbid waters. Until Kj
measurements are routinely collected or the sensitivity
of the Secchi measurements is increased beyond the
nearest decimeter, it is recommended that use of the
conversion factor of 1.45, published by Batiuk et al.
(1992) be continued for consistency.
Uncertainties in Measurement of Percent Light
There is a similar problem encountered in comparing
estimates of percent light based on underwater
measurements of flux to those based on Kj. The light
-------
28 SAV TECHNICAL SYNTHESIS II
TABLE II 1-5. Conversion of Secchi depth (SD) to Kd, Secchi depth equivalences, and percent light at
the 1 -meter depth for Secchi depths equal to 0.5, 1 .0 or 2.0 meters.
Formula
Kd=1.45/SD
Kd=1.7/SD
K,,= 1.44/SD
Kd=1.25/SD
Ka= (2.6/(SD+2.5))-0.0480
Kd=1.052*SD*536
K,!=1.47/SD
Kd= 1.46/SD
K,, = 2.02/SD
SD = 15% subsurface
intensity
"Implies K«=1.90/SD
SD = 18-24% subsurface
intensity
Percent light at 1
SD = 0.5
m
5.5
3.3
5.6
8.2
44.1
21.8
5.3
5.4
1.8
2.2
3.3 to 5.7
SD=1.0
m
23.5
18.3
23.7
28.7
49.9
34.9
23.0
23.0
13.3
15
18.1 to
23.9
meter
SD = 2m
48.4
42.7
48.7
53.5
58.9
48.4
48.0
48.0
36.4
38.7
42.5 to
48.9
References
Batiuketal. 1992
Poole and Atkins 1929
Holmes 1970
Visser 1970
Weinberg 1976
Pellikaan 1976
Duarte and Kalff 1987
Chambers and Kalff 1985
Middleboe and Markager
1997
Vollenweider 1971
Backman and Barilotti
1976
Source
Geisen 1990,
Duarte 1991a
Geisen 1990
Geisen 1990
Geisen 1990
Geisen 1990
Vincente and
Rivera 1982
Vincente and
Rivera 1982
"Implies Kft-l.
1.43/SD
SD assumed = 10% light level 1.0
*Implies K,, = 2.30/SD
SD = 22% surface irradiance
"Implies Kn=l.51/SD
4.9
10.0 31.7 Chambers and Kalffl985
22.1 47.0 Megard and Berman 1989 Dunton 1994
= ln(It/ISD)/SD
-------
Chapter III - Light Requirements for SAV Survival and Growth 29
attenuation coefficient, Kd, gives the relationship
between simultaneous measurements of irradiance at
depth and irradiance just below the surface of the
water. Estimates of percent light based on actual flux
at depth Z can be calculated using full sunlight, esti-
mated here as 2000 /jimol m~2 s"1, or some more
medium condition, for example, 1500 ju,mol m'2 s"1 or
even a less sunny condition, 1000 //mol m"2 s"1, but
these are not equivalent to percent light based on
actual Kj. Tables III-5 and III-6 illustrate how sensitive
percent light estimates are to assumptions made for
calculations and explain why there has been no
attempt in this chapter to estimate the percent light
plants were receiving based on measured fluxes with-
out surface or below-surface reference measurements.
Importance of Short-Term Events
Existence of SAV beds is assumed to depend upon the
average light climate over a growing season, but short-
term periods of light limitation can also influence
plant survival, as has been demonstrated for Z. marina
populations in tidal tributaries of lower Chesapeake
Bay (Moore et al. 1997). Canopy formers are most vul-
nerable to high turbidity during the early growing sea-
son, when plants are growing rapidly toward the
surface. If overwintering plant propagules are small or
few in number or if plants reproduce by seeds, the
impact of early spring turbidity could be serious. Sea-
sonal or short-term events that significantly reduce
light availability may cause annual estimates of light
availability to be misleading by overestimating average
light availability (Moore et al. 1997).
Relative Light Requirements for Canopy-Forming
vs. Meadow-Forming Species
It is important to recognize that different SAV species
with diverse growth strategies and/or growing in dif-
ferent habitats may have substantially different light
requirements. Light requirements in tidal fresh and
oligohaline environments may differ from those in
mesohaline and polyhaline environments not only
because of salinity stress, but also because of differ-
ences between canopy formers and meadow formers.
All the polyhaline species, including Z. marina, are
meadow species; most of the tidal fresh to oligohaline
species are canopy formers, with the exception of V.
americana. The biomass of canopy formers is generally
concentrated in the top half of the water column,
whereas the biomass of meadow formers such as V.
americana or Z. marina is concentrated in the lower
two-thirds of the water column (Carter et al. 1991;
Titus and Adams 1979).
Canopy formation requires rapid growth toward the
surface during the early growing season and results in
the shading of plants or plant parts below the canopy
and the shedding of lower leaves. Epiphytes accumu-
late on the older parts of the foliage where they are
sloughed off with the leaves; continued growth pro-
duces "epiphyte-free" apical leaves. In meadow form-
ers, the new leaf tissue is near the base of the plant,
whereas older leaf tissue near the surface may be heav-
ily epiphytized; however, the leaf turnover rate is fairly
rapid because the life span of leaves is two months
(Sand-Jensen and Borum 1983).
TABLE 111-6. Percent light calculated from light flux at depth Z based on estimates of ambient
surface light.
Light flux at Percent light based on
depth Z ambient full sunlight
Oimol m 2 s-1) (2000 jwmol nr2 s'1)
10
50
100
200
300
0.5
2.5
5
10
15
Percent light based
on ambient 1500
/imol m"2 s"1
0.7
3.3
6.7
13.3
20
Percent light based
on ambient 1000
/imol m*2 s"1
1
5
10
20
30
-------
30 SAV TECHNICAL SYNTHESIS I
Morphological Adaptions to Low Light
In highly turbid environments the relationship
between available light and plant survival tends to
break down because of the effectiveness with which
certain SAV species can employ morphological adap-
tations, including leaf and stem etiolation, to cope with
low light in very shallow habitats (Middleboe and
Markager 1997). In some cases, plant seedlings and
vegetative sprouts can reach the water surface quickly
by concentrating on vertical growth and cell elonga-
tion. Once photosynthetic tissue approaches the water
surface there will be sufficient light to maintain posi-
tive net growth (e.g., Goldsborough and Kemp 1988).
Beginning Growing Season Carbohydrate Reserves
Rapid elongation toward surface light is helped by the
presence of large propagules (e.g., tubers and turions)
containing considerable stored carbohydrate reserves
(e.g., V americana, H. verticillata, P. pectinatus, P. cris-
pus). However, in years when light availability is poor,
fewer and smaller overwintering propagules may be
produced or less below-ground biomass built up, thus
influencing the following year's growth.
Middleboe and Markager (1997) suggest that the min-
imum light requirements for SAV depend on the
plant-specific carbon value (plant biomass per unit
light absorbing surface) for the species/group, indicat-
ing that the light requirements of SAV are tightly
linked to the plant's ability to harvest light and, hence,
to the growth form. The above-ground shoot biomass,
along with the specific leaf area for the shoot, deter-
mines the area available for light harvesting per unit of
plant biomass and thus plant-specific carbon. Plants
with a high plant-specific carbon value have a limited
capacity to tolerate losses, due to grazing or mechani-
cal damage, at low light. For perennial species, initial
growth often is supported by reserves of carbohydrates
stored in below-ground structures or shoots, allowing
plants to achieve high initial elongation rates despite
low irradiance and to form canopies in the upper, well-
illuminated part of the water column. Colonization
occurs either vegetatively from shallower water or
from propagules during periods with clear water
and/or high surface irradiance. High spring turbidities
also may limit survival of high-salinity species by
reducing the carbohydrate reserves necessary for sur-
vival during periods of temperature stress in the sum-
mer (Burke et al. 1996; Moore et al. 1997).
Light Attenuation by Epiphytic Material
Photosynthetically active radiation (PAR) attenuation
by epiphytic material accumulating on SAV leaves,
which is seldom considered in shading experiments or
Zmax vs. K^ models, will cause the minimum light val-
ues cited in these studies to be overestimates of actual
plant requirements. Under typical healthy field condi-
tions (early to mid growing season), light attenuation
across accumulated epiphytic material causes an addi-
tional 15 percent to 25 percent reduction of transmit-
ted light to polyhaline and mesohaline species (e.g.,
Bulthuis and Woelkerling 1983b; Staver 1984; Twilley
et al. 1985; Kemp et al. 1989; van Dijk 1993; Vermaat
and Hootsman 1994). Almost no information is avail-
able in the literature on the effects of epiphytic mate-
rial on light availability for fresh or oligohaline species.
Chesapeake Bay Research and
Monitoring Findings
Research and monitoring results from Chesapeake
Bay also provide insights into light requirements, espe-
cially in tidal fresh and oligohaline waters where there
is a paucity of published information. Batiuk et al.
(1992) established minimum seasonal water-column
based light requirements by salinity regime for restora-
tion of SAV to a depth of 1 meter throughout Chesa-
peake Bay: Kj = 2.0 nr1 in tidal fresh and oligohaline
regimes and Kj = 1.5 nr1 in mesohaline and polyhaline
segments. Using the relationship
percent light = 100*exp(-Kd *Z)
(III-l)
where Z = depth in the water column, and setting Z =
1 meter, the Chesapeake Bay minimum seasonal per-
cent light requirement as published in Batiuk et al.
(1992) was 13.5 percent in tidal fresh and oligohaline
environments and 22.3 percent in mesohaline and
polyhaline environments. More specific water-column
based seasonal light requirements were suggested by
Carter and Rybicki in Batiuk et al (1992) for the tidal
Potomac River and Estuary: Kd = 2.2 nr1 in tidal fresh
regions and K^ = 2.7 nr1 in oligohaline regions. In the
Potomac River and Estuary, the suggested water-
column based seasonal light requirements by Carter
and Rybicki in Batiuk et al. (1992) were 11 percent in
the tidal fresh and 7 percent in the oligohaline
environments.
-------
Chapter III - Light Requirements for SAV Survival and Growth 31
Tidal Fresh/Oligohaline Potomac River Findings
Before 1997, the Chesapeake Bay Program subdivided
the tidal Potomac River and Estuary into three
salinity-based segments—TF2 (tidal fresh), RET2
(oligohaline to mesohaline) and LE2 (mesohaline),
for the purpose of analyzing data and comparing trib-
utaries baywide. These segments were later redefined,
but SAV coverage for TF2 rather than the newer and
less inclusive Chesapeake Bay Program segment
POTTF1 was used for this analysis. Biweekly water-
quality monitoring data were acquired from the Mary-
land Department of Natural Resources. Annual SAV
coverage in the tidal Potomac River and Estuary
mapped by the Virginia Institute of Marine Science
was acquired from the Chesapeake Bay Program. SAV
coverage estimates for segment TF2 and stations
therein for 1983 were made on the basis of extensive
field work by Carter and Rybicki during the 1983
growing season. SAV coverage estimates for 1988 for
segments TF2 and POTOH were made from 1:12,000-
scale color aerial photographs acquired for the Metro-
politan Washington Council of Government's Aquatic
Plant Management Program.
From 1983 through 1996, SAV coverage in the
Potomac River varied greatly in both the TF2 and the
POTOH segments, as shown in Figure III-6. Both the
change in SAV coverage from the previous year (Fig-
ure III-7) and the median percent light calculated
from growing season Secchi depth (Figure III-8) var-
ied greatly, but both exhibited a general downward
trend during this period.
The change in SAV coverage from the previous year
can be plotted against the median percent light at
1 meter during the SAV growing season (April-
October), as shown in Figure III-9. Changes in SAV
were generally related in a positive, increasing manner
to percent light. When median percent light was
greater than 13 percent, SAV coverage showed only
positive increases over three years. However, positive
increases occurred even in years when median percent
light at 1 meter was considerably less than 13 percent,
indicating that other factors besides light also influ-
ence changes in coverage, or that SAV was growing at
depths < 1 meter. A median growing season percent
light of 13 percent at 1 meter is equivalent to a median
Secchi depth of 0.7 m or median 1^=2.07, assuming
Kj = 1.45/Secchi depth. Secchi depth is only reported
to 0.1 m, so the error in the median measurements is
2500
w
2 2000
re
u
o>
1500
CD
1 1000
o
u
500
to
Tidal Fresh (TF2)
Oligohaline (POTOH)
1982 1984 1986 1988 1990 1992 1994 1996
YEAR
FIGURE III-6. Potomac River Tidal Fresh/Oligohaline
SAV Coverage. Seasonal SAV coverage from 1983-
1996 for the Potomac River's tidal fresh and oligohaline
segments.
1000
0 (A
£
13 500
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re X
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19
------ Tidal Fresh (TF2) 1
~T — 0 — Oligohaline (POTOH) |~T—
^k O
T X
• \ / \ •
/ \ / W'T
* R*> /A \
\>a/ /^f f
° *A* \ \J/\ /
' P> O
1 / \ >
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- i < -
.
i i i i i i i i i i i i i i
82 1984 1986 1988 1990 1992 1994 1996
Year
FIGURE III-7. Change in Seasonal SAV Coverage for
Potomac River Segments. Changes in seasonal SAV
coverage from previous year, in hectares, for the tidal
fresh (TF-2) and oligohaline (POTOH) Potomac River
segments from 1983-1996.
-------
32 SAV TECHNICAL SYNTHESIS II
U 20
o
•o
a>
-5
15
re o>
go 10
~ S>
C
V
O)
C
Tidal Fresh (TF2)
Oligohaline (POTOH)
I I I
I I
I I I
1982 1984 1986 1988 1990 1992 1994 1996
YEAR
FIGURE III-8. Change in SAV Growing Season Light
Penetration for Potomac River Segments. Median
percent light at the one-meter depth during the April-
October SAV growing season for the tidal fresh (TF2)
and oligohaline (POTOH) Potomac River segments from
1983-1996, assuming Kj = 1.45/Secchi depth.
Change in SAV coverage from
previous year, in hectares
is si
o o o o o
• Tidal Fresh (TF2) I
~r~l O POTOH {Oligohaline) I"1"
: °
! •
t>
o • o o
• c ° 8 {
Oo
1
•
" Percent light =12.6
Kd=2.07m-1\J
Secchi Depth = 0.7 m
» "
•
o
3
»
»
»
) 5 10 15 20
Median light penetration at 1 meter,
as % surface light
FIGURE III-9. Change in SAV Coverage in Relation to
Light Penetration. The change in SAV coverage from
the previous year in the tidal fresh (TV2) and oligohaline
(POTOH) segments of the Potomac River is shown in
relation to the median percent light at the one-meter
depth during the April-October SAV growing season.
±0.05 m, median seasonal Secchi depth ranges from
0.65 to 0.75 m and, therefore, K^ ranges from 1.93 to
2.23 m"1. This suggests that for the tidal fresh and
oligohaline segments of the Potomac River and Estu-
ary, a corresponding range of percent light of 11 per-
cent to 14.5 percent presents a boundary condition for
net increase in growth from year to year. It should also
be noted that if other habitat conditions are favorable,
SAV may tolerate worse light conditions for a season,
but not on a protracted basis.
Tidal Fresh Patuxent River Findings
The tidal Patuxent River is a lower energy environ-
ment than the tidal Potomac River in terms of river
width and fetch, so that plants may be able to colonize
shallower areas than possible in the Potomac River.
After having no SAV for many years, the tidal fresh
Patuxent River (PAXTF) had notable SAV for the
years 1993 through 1998. Corroboration of the SAV
light requirements suggested by the Potomac data
comes from observations on the Patuxent River.
For the period of 1985 to 1996, light conditions in the
tidal fresh Patuxent River (Maryland Department of
Natural Resources monitoring station PXT0402)
improved. Kj dropped from 6 m'1 to about 4 m"1 (Mike
Naylor, unpublished data) and average Secchi depth
increased from 0.25 to 0.4 meters. During the last four
years of this period, colonization by SAV also
increased, primarily in the shallow areas less than 0.5
meters deep mean lower low water (MLLW). A K^ of
four results in 13.5 percent light at a depth of 0.5
meters. A second Patuxent River tidal fresh water
quality monitoring station (PXT0456) also showed a
significant increase in Secchi depth during the SAV
growing season in this same period.
It appears that when the seasonal Secchi depth at
PXT0456 was greater than a threshold value of
0.35 meters, the SAV coverage continued to increase,
whereas a Secchi depth below 0.35 meters coincided
with a decrease in SAV coverage. A Secchi depth
threshold of 0.35 meters for plants colonizing a depth
of less than 0.5 meters is equivalent to a 0.68-meter
Secchi depth threshold for plants colonizing a depth of
less than 1 meter (as seen in the Potomac). Thus it
appears that similar threshold light conditions are
required for successful recolonization in the tidal fresh
areas of both the Potomac and Patuxent rivers.
-------
Chapter III - Light Requirements for SAV Survival and Growth 33
Mesohaline Potomac Findings
In the mesohaline segment of the Potomac River, SAV
has continued to increase steadily since 1983, although
the coverage remains relatively small compared to pre-
1960 conditions. Colonization by SAV has taken place
primarily in areas less than 1 meter deep. Midchannel
light conditions are better in the mesohaline segment
of the river compared to either the tidal fresh or oligo-
haline segments, with the median seasonal Secchi
depth generally never dropping below 1 meter for the
period 1983-1996. Secchi depth is only reported to
0.1 meters, so the error in the median measurements is
at least ±0.05 meters. If median Secchi depth is
1 meter, then using a conversion factor of 1.45 to cal-
culate Kj, median light conditions are 23.5 percent at
1-meter depth (MLW), ranging from 21.7 percent to
25.1 percent. Thus, it appears that the Chesapeake
Bay water-column based light requirements published
previously by Batiuk et al (1992) for mesohaline and
polyhaline segments are consistent with what has been
seen in the mesohaline region of the Potomac River.
WATER-COLUMN LIGHT REQUIREMENTS
Based on an in-depth review of the results of shading
experiments and model findings published in the sci-
entific literature, a water-column light target of >20
percent is needed for Chesapeake Bay polyhaline and
mesohaline species. Consistent with the value derived
from the extensive scientific literature review, the
water-column light requirement of 22 percent was
determined for mesohaline and polyhaline regions of
Chesapeake Bay and its tidal tributaries by applying
the 1992 SAV habitat requirement for K^ (=1.5 m'1,
Table VII-1) through the equation for determining the
percent light through water, PLW (see Chapter V):
PLW= 100[exp(-Kd)(Z)]
(Equation II-l).
Considering measurement precision in Secchi depth
measurements, this requirement has a margin of
uncertainty that can be expressed as 21-24 percent
light for the mesohaline and polyhaline segments. This
water-column light requirement is confirmed by field
observations since 1983 in the mesohaline Potomac
River (21.7 percent to 25.1 percent; see "Mesohaline
Potomac Findings").
Based on published model findings, confirmed by a
review of the results of recent tidal Potomac and
Patuxent River research and monitoring studies, a
water-column light requirement of 13 percent light is
recommended for Chesapeake Bay tidal fresh and
oligohaline species. This water-column light require-
ment calculated using Equation II-l and the appropri-
ate 1992 SAV habitat requirement for Kj (<2 nr1,
Table VII-1). Considering measurement precision
in Secchi depth measurements, these water-column
based light requirements have a margin of uncertainty
that can be expressed as 11 percent to 14.5 percent
light for the tidal fresh to oligohaline segments. The
literature suggests that field requirements are three to
five times greater than minimal light conditions meas-
ured in a laboratory, and the only lab experiment for
tidal fresh Chesapeake Bay SAV species (Goldsbor-
ough and Kemp 1988) yielded a light requirement of 3
percent. We suggest about a fourfold multiplier to 13
percent because this is consistent with what has been
seen in the Potomac and Patuxent rivers. This water-
column light requirement is also consistent with the
13.5 percent requirement published by Batiuk et al.
(1992) and Dennison et al. (1993).
The large and diverse literature describing responses
of different SAV species to variations in light regime
under field and laboratory conditions has been sum-
marized in this chapter. The material here points to
the need for different water-column light require-
ments for different salinity zones in Chesapeake Bay,
largely because of species differences. Chapter VI
also illustrates that tidal range may drastically alter
available light and may be a factor in determining the
area available for colonization. Targeting a specific
percentage of light makes these light requirements
more universally usable than does specifying Kj for
restoration of vegetation to a particular depth. Until
more definitive research is conducted, these require-
ments should be considered with a margin of uncer-
tainty based primarily on the measurement error built
into the Secchi depth measurement.
Chapter V, "Epiphyte Contributions to Light Attenua-
tion at the Leaf," focuses on how changes in water
quality alter the light available at SAV leaves, consid-
ering not only the water-column attenuation, but also
the attenuation of light by epiphytic algae, organic
detritus and inorganic particles attached to the leaf.
Based on the application of the spreadsheet model of
Chapter V for calculating PAR attenuation by epi-
phytic material accumulating on SAV leaves, the
water-column light requirements described here can
be translated into minimum light requirements based
on both water-column and epiphytic attenuation, as
described in Chapter VII.
-------
CHAPTER | V
Factors Contributing to
Water-Column Light Attenuation
The penetration of sunlight into coastal waters
places severe constraints on the survival and
spatial distribution of submerged aquatic vegetation.
Currently the best estimate for the minimum amount
of light required for survival of SAV is as high as
22 percent of incident sunlight for mesohaline and
polyhaline estuarine species (Chapter III).
Light penetration through the water column is con-
trolled by the amount and kinds of materials that are
dissolved and suspended in the water. Quantitative
understanding of the mechanisms by which the various
materials affect the transmission of light through
water forms the basis for setting water quality require-
ments for the restoration and protection of SAV. Light
reaching the surface of an SAV leaf is further attenu-
ated by attached epiphytic algae and other mineral and
organic detritus adhering to the leaf. Therefore, target
concentrations of optically active water quality con-
stituents must be regarded as minimum requirements
for SAV survival and growth, which may be modified
as needed by conditions that promote growth of epi-
phytic algae on leaf surfaces (Chapter V).
This chapter documents the development and man-
agement application of diagnostic tools for defining
the necessary water quality conditions to develop goals
and management actions for restoring and protecting
SAV. The diagnostic tool pertains only to water quality
conditions that influence light attenuation within the
water column. The additional light attenuation occur-
ring at the leaf surface due to the accumulation of epi-
phytes and associated material is addressed in Chapter
V. The process of light attenuation underwater is
briefly summarized. It will be shown that, in spite of
known nonlinearities, a linear expression relating the
attenuation coefficient to water quality concentrations
is all that is justifiable, because of the variability in the
optical properties of the water quality constituents and
in the measurements. The diverse origins of sus-
pended particulate matter is one factor that increases
the difficulty of modeling light attenuation over such a
large geographic extent as Chesapeake Bay. The con-
tribution of phytoplankton to total suspended solids is
estimated to better define the relative roles of nutrient
reduction and sediment controls increasing light pene-
tration for different locations. The use of a linear
model of light attenuation to plot a range of water
quality conditions that will result in depth specific
attainment of minimum light requirements is then
demonstrated.
WATER-COLUMN LIGHT ATTENUATION
Light underwater is diminished by two processes:
absorption and scattering (Kirk 1994). Absorption
removes light altogether, whereas scattering changes
the direction of propagation. Scattering does not
directly remove light from the water, but rather
increases the probability that it will be absorbed, by
increasing the path length or distance that the light
must travel.
Absorption and scattering interact in a complex and
nonlinear manner to govern the attenuation of light
underwater. The equations governing the propagation
of light underwater, called the radiative transport
equations, have no exact solution; but several
Chapter IV - Factors Contributing to Water-Column Light Attenuation 35
-------
36 SAV TECHNICAL SYNTHESIS I
computer programs have been written to solve the
equations by various numerical methods (Mobley et al.
1993). Despite the complexities of the radiative trans-
port equations, field measurements of underwater
irradiance nearly always show a negative exponential
decay of light with depth. In the absence of strong dis-
continuities in water quality, such as nepheloid layers,
subsurface chlorophyll a maxima or humic-stained sur-
face layers, measurements of photosynthetically active
radiation (PAR, 400-700 nm) are well described by a
single exponential equation of the form
IZ2 = IZiexp[-Kd(Z2 - Zi)]
(IV-1)
where Izi and Iz2 are irradiances at depth Zi and Z2
(Z2>Zi), and Kd is the diffuse attenuation coefficient
for PAR. Several expressions useful for describing the
light available to SAV are easily derived from Equa-
tion IV-1. For example, if Zi represents the surface
(depth=0) and Z2 is the maximum depth of SAV colo-
nization, Zmax, then the percentage of surface light
penetrating through the water (PLW) to the plants at
depth Zmax is given by
PLW = exP(-KdZmax)*100
(IV-2).
We denote the minimum PLW required for growth as
the water-column light requirement (WCLR). In
Equation IV-2 and the equations that follow, it should
be understood that decimal fractions are being used
for quantities such as PLW and WCLR, expressed as
percentages (i.e. 22 percent=0.22). If WCLR is
known, then the largest diffuse attenuation that would
permit growth to depth=Zmax is given by
= ln(WCLR)/Zn
(IV-3).
Expressing Kd in Equation IV-3 as a function of the
optically active water quality parameters forms the
basis for the diagnostic tool for identifying a range of
water quality conditions necessary for achieving the
water-column light target.
PARTITIONING SOURCES OF
WATER-COLUMN LIGHT ATTENUATION
Underwater light is attenuated by water itself and by
certain dissolved and particulate substances. The opti-
cally important water quality parameters are colored
dissolved organic matter or yellow substance (Kirk
1994), and suspended particulate matter. Suspended
particulate matter can be further characterized by its
contributions from fixed (i.e., noncombustible) sus-
pended solids composed of clay, silt and sand mineral
particles, and volatile (i.e., combustible) suspended
solids composed of phytoplankton chlorophyll a and
nonpigmented organic detritus. Each of the materials
has characteristically shaped light absorption spectra.
Because PAR is measured over a wide range of wave-
lengths, the spectral dependence of absorption means
that the effect of one material, for example, phyto-
plankton, on light attenuation will depend on the con-
centrations of other materials present at the same
time. For this and other reasons related to the non-
linearity of the radiative transport equations, the con-
cept of a partial attenuation coefficient for the various
optical water quality parameters is only an approxima-
tion, and one that has been criticized (Kirk 1994). In
spite of these known limitations in partitioning the dif-
fuse attenuation coefficient into contributions due to
individual components, that approach is adopted here
because of the need to derive a tool that is simple to
use with large amounts of data and can be interpreted
by managers unacquainted with the details of radiative
transport theory.
First, the attenuation coefficient for downwelling
(moving down through the water) light is expressed as
the sum of that due to water (W) plus dissolved
organic matter (DOC), phytoplankton chlorophyll a
(Chi) and total suspended solids (TSS). Based on the
analyses presented below, it is assumed that attenua-
tion due to dissolved matter is relatively constant and
may be included with water itself. We further assume
that the contributions to light attenuation due to
chlorophyll a and total suspended solids are propor-
tional to their concentrations, so that the diffuse atten-
uation coefficient may be written as
Kd = K(W+Doc) + kc[Chl] + ks[TSS] (IV-4)
where K(W+DOC) is the partial attenuation coefficient
due to water plus colored dissolved matter, and kc and
ks are the specific-attenuation coefficients due, respec-
tively, to chlorophyll a and to total suspended solids.
By combining equations IV-3 and IV-4, combinations
of chlorophyll a and total suspended solids that just
meet the WCLR may be calculated using:
ln(WCLR)/Zmax =
kc[Chl] + ks[TSS]
(IV-5).
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 37
By assuming that K(W+DOC) is constant, Equation IV-6
can be used to calculate linear combinations of con-
centrations of total suspended solids and chlorophyll a
that just meet the WCLR,
[TSS] = {[ln(WCLR)/ZinM]-K(w+Doc)kc[CM]}/ks
(IV-6).
For a 1-meter colonization depth and PLW equaling
22 percent, ln(WCLR)/Zmax=1.51. Parallel lines for
other colonization depths are found by dividing
In(PLW) in Equation IV-5 by the appropriate value of
Zmax. Adjustment of the colonization depth for tidal
range is a simple but important modification pre-
sented in Chapter VI.
DIAGNOSTIC TOOL COEFFICIENTS
Application of the diagnostic tool requires values for
three coefficients: the attenuation due to water plus
dissolved matter, K(W+Doc)> the specific-attenuation
coefficients for phytoplankton chlorophyll, kc, and
total suspended solids, ks. Initially, coefficients
(including a separate specific-attenuation coefficient
for dissolved organic carbon) were estimated by multi-
ple linear regression of K 5 meters.
The optical model of Gallegos (1994) with water as the
only factor contributing to attenuation predicts a
range of KW from about 0.16 to 0.13 m'1 as the depth
is varied from 1 to 3 meters. Though the variation may
seem small, the same model calculates Lorenzen's
(1972) value of 0.038 m'1 for seawater over a 51-meter
depth interval. Thus, in shallow water, the attenuation
due to water itself is not negligible, though much
smaller than the intercepts estimated by linear regres-
sion in Table IV-2. Evidently, the regressions lump
much unexplained variance into the intercept.
Dissolved Organic Carbon
Statistically significant coefficients for specific attenu-
ation of dissolved organic carbon were obtained at
only two stations, giving specific-attenuation co-
efficients of 0.09 and 0.2 m2 g'1 (Table IV-2). The over-
all coefficient of determination and accompanying
-------
38 SAV TECHNICAL SYNTHESIS II
TABLE IV-1. Statistical summaries of concentrations of optical water quality parameters — chlorophyll,
dissolved organic carbon and total suspended solids for Chesapeake Bay Water Quality Monitoring
Program stations at which underwater light measurements were made.
Number of
Station Observations
Mean
Median
Derivation
Standard
Minimum
Maximum
CHESAPEAKE BAY MAINSTEM STATIONS
Chlorophyll (/Ag/L)
CB1.1
CB2.2
CB3.3C
CB4.3C
CBS. 2
164
174
177
176
176
8.28
5.03
15.5
8.10
9.44
7.60
3.62
11.6
7.20
7.05
6.38
4.51
15.7
4.36
8.01
1.00
1.00
1.00
1.70
1.10
52.0
27.9
105
24.4
44.0
Dissolved Organic Carbon (mg/L)
CB1.1
CB2.2
CB3.3C
CB4.3C
CBS. 2
165
170
174
173
175
2.68
2.76
2.79
2.85
2.95
2.61
2.71
2.71
2.74
2.78
0.656
0.558
0.427
0.667
0.744
1.26
1.60
0.820
2.09
2.13
5.88
5.87
4.07
8.97
8.86
Total Suspended Solids (mg/L)
CB1.1
CB2.2
CB3.3C
CB4.3C
CB5.2
Chlorophyll Qig/L)
MEE3.1
MET4.2
MET5.1
MET5.2
MLE2.2
174
175
180
179
181
TIDAL
5
7
141
137
147
10.8
16.9
7.61
4.59
4.91
7.18
14.7
6.85
4.40
4.50
12.8
11.0
3.79
1.33
1.80
1.50
1.85
2.50
1.60
1.50
108
87.3
29.9
10.3
11.5
TRIBUTARY STATIONS
4.72
9.26
49.6
12.05
14.7
4.69
9.12
31.80
9.64
11.4
2.15
1.92
56.29
10.30
11.9
1.82
6.94
4.58
0.83
2.18
7.79
12.71
391.2
66.18
continued
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 39
TABLE IV-1. Statistical summaries of concentrations of optical water quality parameters — chlorophyll,
dissolved organic carbon and total suspended solids for Chesapeake Bay Water Quality Monitoring
Program stations at which underwater light measurements were made (continued).
Number of
Station Observations
MWT5.1
PXT0402
XDA1177
XDE5339
XEA6596
XED4892
Dissolved Organic
MEE3.1
MET4.2
MET5.1
MET5.2
MLE2.2
MWT5.1
PXT0402
XDA1177
XDE5339
XEA6596
XED4892
141
150
129
158
90
89
Mean
49.6
40.3
6.89
19.3
16.0
14.0
Median
Derivation Standard
31.8
35.7
4.38
15.3
9.72
11.7
56.3
30.3
7.70
18.7
16.4
13.9
Minimum
4.58
0.260
0.880
2.57
0.430
0
Maximum
391
126
43.8
189
71.4
122
Carbon (mg/L)
5
7
138
105
110
110
147
54
136
60
86
3.52
2.76
6.13
3.85
3.33
3.46
4.67
3.99
3.16
3.86
4.52
3.58
2.78
5.83
3.62
3.27
3.26
4.69
3.69
3.13
3.73
4.50
0.155
0.200
2.25
1.27
1.22
1.35
1.07
1.21
0.725
1.09
0.854
3.25
2.41
0.820
1.70
0.820
0.780
1.11
1.42
1.22
1.67
2.27
3.63
3.01
19.7
8.73
7.84
6.97
8.10
7.54
5.90
7.25
7.09
Total Suspended Solids (mg/L)
MEE3.1
MET4.2
MET5.1
MET5.2
MLE2.2
MWT5.1
PXT0402
XDA1177
XDE5339
XEA6596
XED4892
5
7
143
138
147
142
152
130
158
93
89
13.2
6.57
33.1
13.8
11.8
17.3
36.1
20.1
11.3
19.1
34.2
16
7
30.5
12.2
11
15
33
18
9.62
17
29.3
10.6
3.87
17.1
9.00
6.81
11.3
19.1
11.5
6.03
9.37
20.6
2
1
5
1
1
1
1
3.5
1
2
9.5
27
12
96
52
40
58
156
71
39
51
136
-------
40 SAV TECHNICAL SYNTHESIS II
TABLE IV-2. Coefficients (an estimate of specific-attenuation coefficient) and intercepts (an estimate of
attenuation due to water alone) estimated by linear regression of diffuse attenuation coefficient, Kd
(dependent variable), against concentrations of dissolved organic carbon, phytoplankton chlorophyll
and total suspended solids. Data from Chesapeake Bay Water Quality Monitoring Program, but limited
to stations at which underwater light measurements were made, ns = not statistically significant,
P>0.05.
Station
Mainstem
CB1.1
CB2.2
CB3.3C
CB4.3C
CB5.2
Coefficient
of
Determination
Chesapeake Bay
0.569
0.528
0.453
0.148
0.271
Degrees
of
Freedom
104
117
129
121
121
Intercept
0.581
1.143
0.610
0.533
0.393
Dissolved
Organic
Carbon
Kg1)
0.1015ns
-0.0100 ns
0.0142 ns
-0.0091 ns
0.0209 ns
Phytoplankton
Chlorophyll
(rn'rng1)
0.0022 ns
-0.0082 ns
-0.0012 ns
0.0192
0.0105
Total
Suspended
Solids
Kg1)
0.101
0.074
0.076
0.041
0.042
Tidal Tributaries
MET5.1
MET5.2
MWT5.1
PXT0402
0.208
0.378
0.530
0.109
XDA1177 0.338
XDE5339
XEA6596
XED4892
0.219
0.321
0.463
96
80
78
104
42
100
47
71
3.227
0.605
1.581
2.833
1.327
0.807
1.271
2.096
0.1960
0.0931
-0.0493 ns
0.3065 ns
0.0695 ns
0.0409 ns
0.0074 ns
0.1225ns
-0.0236
0.0170
0.0108
-0.0113ns
-0.0153 ns
0.0048 ns
0.0020 ns
-0.0461
0.033
0.013
-0.001 ns
0.043
0.047
0.042
0.064
0.058
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 41
coefficients for specific-attenuation of total suspended
solids were anomalously low at these two tidal tribu-
tary stations, casting doubt on the reliability of these
values.
Only a variable fraction of dissolved organic carbon,
referred to as colored dissolved organic matter, con-
tributes to light attenuation (Cuthbert and del Giorgio
1992). Therefore, the lack of statistically significant
coefficients at most stations is not surprising. Colored
dissolved organic matter absorbs light but does not
contribute appreciably to scattering (Kirk 1994). In the
PAR waveband, absorption by colored dissolved
organic matter is maximal in the blue region of the
spectrum and decreases exponentially with wave-
length. Optically, the effect of colored dissolved
organic matter on attenuation is best quantified by the
absorption coefficient of filtered estuary water (0.2 or
0.4 mm membrane filter) at a characteristic wave-
length, which, by convention, is most often 440 nm
(Kirk 1994).
In an effort to quantify the contribution of colored dis-
solved organic matter to attenuation, the regression of
Gallegos et al. (1990) between absorption coefficient
(corrected to 440 nm) and dissolved organic carbon
was incorporated into the model of Gallegos (1994).
Water quality conditions for other parameters, chloro-
phyll a and total suspended solids, were chosen to rep-
resent average conditions for a range of water quality
monitoring stations along the upper length of the
mainstem Chesapeake Bay (Table IV-1).
The specific attenuation coefficient of dissolved
organic carbon calculated by the model varied from
0.026 m2 g'1 for upper Bay tidal fresh conditions to
0.031 m2 g"1 for lower Bay mesohaline conditions.
Concentrations of dissolved organic carbon were sur-
prisingly uniform along the axis of the mainstem
Chesapeake Bay, ranging from about one to six mg
liter'1 in the upper Chesapeake Bay to two to nine mg
liter1 at station CB5.2, located in the mainstem Chesa-
peake Bay off the mouth of the Potomac River (Table
IV-1). The contribution of dissolved organic carbon to
light attenuation can, therefore, be expected to aver-
age about 0.07 m"1 and range from about 0.03 to 0.23
m"1. The average contribution of dissolved organic car-
bon to light attenuation is less than that of water itself
(i.e., >0.13 m"1, see above) in shallow systems and
therefore can be expected to be difficult to detect in
monitoring data, which are subject to expected levels
of sampling and analytical error.
Therefore, as discussed above, the effect of dissolved
organic carbon was incorporated into the regression
for K was
determined by setting total suspended solids and
chlorophyll a concentrations in the model of Gallegos
(1994) to zero, and allowing concentrations of dis-
solved organic carbon to vary according to a normal
distribution with mean of 2.71 mg liter"1 and standard
deviation of 0.44 mg liter"1, similar to observations at
Chesapeake Bay Water Quality Monitoring Program
station CB3.3C (Table IV-1). Attenuation due to water
and dissolved organic carbon calculated in this manner
varied from 0.21 to 0.31 m"1 and averaged 0.26 m"1,
which was used as a trial value.
Phytoplankton Chlorophyll
Phytoplankton, being pigmented cells (i.e., particles),
contribute both to absorption and the scattering of
light. Light absorption by phytoplankton varies
strongly with wavelength. The shape of the in vivo
absorption spectrum of phytoplankton varies with
species, but generally, peaks occur at about 430 nm
and at 675 nm, with a broad minimum in the green
region of the spectrum (Jeffrey 1981). Because of this
spectral dependence, the contribution of phytoplank-
ton to attenuation varies with the depth and com-
position of the water (Atlas and Bannister 1980), and
to a lesser extent in natural populations, with species
composition.
By linear regression on data from the Chesapeake Bay
Water Quality Monitoring Program, statistically signif-
icant estimates for the specific-attenuation coefficient
for chlorophyll a were obtained at 6 of 13 stations
(Table IV-2). Two of those were negative values and
must be considered spurious. Significant positive
-------
42 SAV TECHNICAL SYNTHESIS II
values ranged from 0.011 to 0.019 m2 (mg Chi)4. This
range compares favorably with values reported in the
literature. For example, Atlas and Bannister (1980)
used a fixed specific absorption spectra and calculated
a range of the chlorophyll-specific attenuation coeffi-
cients near the surface ranging between 0.013 and
0.016 m2 (mg Chi)'1. The overall magnitude of the
chlorophyll-specific absorption spectrum, however,
varies considerably with physiological state, photo-
adaptation, and recent history of light exposure of the
phytoplankton population. A wider survey of the liter-
ature, reviewed by Dubinsky (1980) suggested values
between 0.005 and 0.040 m2 (mg Chi)'1, but most esti-
mates range more narrowly between 0.01 to 0.02 m2
(mg Chi)4 (Lorenzen 1972; Smith and Baker 1978;
Smith 1982; Priscu 1983).
Model-generated estimates of kc can be similarly vari-
able. The effect of chlorophyll a on K
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 43
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FIGURE IV-1. Relationship of Turbidity and
Chlorophyll to Total Suspended Solids:
Rhode River and Chincoteague Bay. This figure
illustrates the relationship between turbidity (an
estimate of scattering coefficient, see text) and
total suspended solids (TSS) in (+) the Rhode River,
Maryland, a tributary sub-estuary of Chesapeake Bay,
and in (o) Chincoteague Bay, a coastal lagoon (A).
Despite similar relationships between turbidity and
TSS, the Rhode River and Chincoteague Bay contrast
in their composition of TSS; chlorophyll contributes
a much greater proportion to TSS in the Rhode River
than in Chincoteague Bay (B).
covered were increased, and different instruments and
standards were employed.
The spectral specific-absorption curves of suspended
solids, including both inorganic silts and clays and
organic detritus, typically have a negative exponential
shape similar to that of dissolved organic matter (Kirk
1994). A single curve was sufficient to model absorp-
tion by non-algal turbidity in the Rhode River and
Chincoteague Bay, Maryland (Gallegos 1994), but dif-
ferent site-specific curves were needed in the Indian
River Lagoon, Florida (Gallegos and Kenworthy
1996). Overall, the spatial variability of absorption by
non-algal suspended particulate matter has not been
well studied.
With absorption and scattering accounted for in the
optical model (Gallegos 1994), the specific-attenuation
coefficient for total suspended solids was calculated by
making small increments in total suspended solids
concentrations, as was done above for dissolved
organic carbon. The resulting value for ks was 0.072 m2
g"1, with only minor dependence on other water qual-
ity parameters. This value is very similar to literature
estimates, although the calculation is based on a single
specific-absorption curve and does not take into
account possible changes in the specific-absorption
curve caused by potential variations in the mineralogi-
cal or humic content of soils around the Bay region.
Based on the similarity of literature and model esti-
mates, an initial estimate for ks of 0.074 m2 g"1 was
selected.
EVALUATION OF THE Kd REGRESSION
Based on the initial selections of specific-attenuation
coefficients, the predicted diffuse-attenuation coeffi-
cients from Chesapeake Bay Water Quality Monitor-
ing Program data are given by the linear regression
Kd = 0.26 + 0.016[Chl] + 0.074[TSS] (IV-7).
An examination of the predicted values against meas-
ured values (Figure IV-2) showed a tendency for the
regression to underestimate measured Kj at both
mainstem Bay (Figure IV-2A) and tidal tributary (Fig-
ure IV-2B) water quality monitoring stations. At main-
stem Chesapeake Bay water quality monitoring
stations there appeared to be bias in the slope of pre-
dicted against observed, whereas at tidal tributary sta-
tions there appeared to be an offset as well. By trial
-------
44 SAV TECHNICAL SYNTHESIS II
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Measured Kd (rri
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Measured K
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Measured Kd (m~1)
FIGURE IV-2. Comparison of Measured Kd with Predictions by Linear Regression Model. Comparison of
measurements of diffuse attenuation coefficient (Kd) made in the Chesapeake Bay Water Quality Monitoring Program
(1986-1996) with predictions made by linear regression against optical water quality parameters. (A) and (B) are
predictions based on initial estimates of coefficients (Equation IV-7); (C) and (D) are predictions based on Equation
IV-8, in which attenuation due to water plus dissolved organic carbon and partial attenuation coefficient of total
suspended solids were adjusted upward. (A) and (C) are data from mainstem Chesapeake Bay stations; (B) and
(D) are data from Maryland tidal tributary stations.
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 45
and error, predictions were improved by adjusting the
estimated K^w+Doc) to 0.32 nr1 and the specific-
attenuation coefficient of total suspended solids to
0.094 m2 g'1 (Figure IV-2C, -2D), possibly indicating
that the highest attenuation coefficients are dominated
by times and locations of highest river flows. These
modifications result in a final regression model of
Ka = 0.32 + 0.016[Chl] + 0.094[TSS] (IV-8).
Overall, the scatter in these plots indicates that more
sophisticated models cannot be considered, given the
limited amount of optical information in the data that
are available. The r2 for the final fits are 0.61 for main-
stem Chesapeake Bay stations, and only 0.37 for tidal
tributary stations. It is also likely that site-specific coef-
ficients for ks and possibly K(W+DOC) will be needed as
a future refinement. Present experience with regres-
sions on the monitoring data (Table IV-2) indicate that
site-specific refinements to coefficients will need to be
computed from optical modeling, based on direct
determination of specific-absorption and specific-
scattering spectra of total suspended solids from a
wide range of sites around the Bay.
With these coefficients, Equation IV-6 can be used to
write equations for combinations of chlorophyll a and
total suspended solids that meet the WCLR for depths
of 0.5,1.0 and 2.0 meters. For mesohaline and polyha-
line habitats where WCLR = 0.22 (22 percent, from
Chapter III), the equations are
0.5 m [TSS] = 28.8 0.17[Chl], [Chi] < 169.4 (IV-9a)
1.0 m [TSS] = 12.7 0.17[Chl], [Chi] < 74.7 (IV-9b)
2.0 m [TSS] = 4.65 0.17[Chl], [Chi] < 27.4 (IV-9c)
where the upper bound on [Chi] is the chlorophyll a
concentration at which the predicted [TSS] = 0 for
that depth; that is, higher chlorophyll a concentrations
would result in a prediction of a 'negative concentra-
tion' for [TSS].
Comparable equations for tidal fresh and oligohaline
habitats are determined by substituting 0.13 (13 per-
cent from Chapter III) for WCLR in Equation IV-6,
0.5 m [TSS] = 40.0 - 0.17[Chl], [Chi] < 235 (IV-lOa)
1.0 m [TSS] = 18.3 - 0.17[Chl], [Chi] < 107 (IV-lOb)
2.0 m [TSS] = 7.45 - 0.17[Chl], [Chi] < 43.8 (IV-lOc).
COMPONENTS OF TOTAL SUSPENDED SOLIDS
Total suspended solids consist of the dry weight of all
particulate matter in a sample, including clay, silt and
sand mineral particles, living phytoplankton and het-
erotrophic plankton, including bacteria and particu-
late organic detritus. Therefore, phytoplankton and
the heterotrophic community it supports contribute to
what is measured by total suspended solids. As shown
above, optically it is difficult to distinguish the effect of
particulate organic matter, including that contributed
by phytoplankton, from that of mineral particulates.
Nevertheless, it is useful to examine their relative con-
tributions to the measurement of total suspended
solids, since organic particulates (due, in part, to nutri-
ent over-enrichment) must be controlled differently
than mineral particulates (due, in part, to erosion or
sediment resuspension). In particular, a reduction in
chlorophyll a will be accompanied by a proportional
reduction in total suspended solids due to the dry
weight component of phytoplankton. This additional
reduction in total suspended solids needs to be incor-
porated into the predicted response of Kj when using
equations (IV-9a-c) for determining the water quality
conditions necessary for achieving the minimum
light requirements.
Upon combustion, the particulate organic matter in a
sample is oxidized, leaving behind the mineral compo-
nent and ash of the organic fraction. The fraction
remaining after combustion is referred to as fixed sus-
pended solids (FSS), and the difference between the
total and the fixed fraction of suspended solids is
called total volatile suspended solids (TVSS). The per-
centage of total suspended solids that is of organic ori-
gin can then be estimated as TVSS/TSS*100.
Fixed suspended solids and total volatile suspended
solids have been measured at the Virginia tidal tribu-
tary and mainstem stations of the Chesapeake Bay
Water Quality Monitoring Program. At very high con-
centrations of total suspended solids, total volatile sus-
pended solids appears to approach a relatively
constant fraction, about 18 percent, of total suspended
solids (Figure IV-3). The extremely high concentra-
tions probably represent flood conditions, and the
fraction of total volatile suspended solids in those sam-
ples are probably characteristic of the terrestrial soils.
At more realistic total suspended solids concentra-
tions, i.e., those < 50 mg liter1, a much wider range in
the percentage of total volatile suspended solids is
-------
46 SAV TECHNICAL SYNTHESIS II
observed (Figure IV-3, inset), exceeding 90 percent in
some samples.
The relationship between total volatile suspended
solids and particulate organic carbon shows a great
deal of scatter (Figure IV-4A) but on average, particu-
late organic carbon is about 30 percent of total volatile
suspended solids. This estimate is larger than that of
living phytoplankton (26 percent) (Sverdrup et al.
1942) and lower than that of carbohydrate (37 per-
cent). The particulate organic carbon in a sample con-
sists of living phytoplankton, bacteria, heterotrophic
plankton, their decomposition products, organic
detritus from marshes or terrestrial communities and
resuspended SAV detritus. As expected, a plot of par-
ticulate organic carbon against phytoplankton chloro-
phyll a displays considerable scatter (Figure IV-4B),
but during sudden phytoplankton blooms, phytoplank-
ton might comprise the major component of carbon in
a sample. The ratio of carbon to chlorophyll a in
0.0
100 200 300 400 500 600 700 800
TSS (g rrf3)
FIGURE IV-3. Fraction of Total Suspended Solids
Lost on Ignition. Fraction of total suspended solids
(TSS) that is lost on ignition as a function of TSS. At
concentrations of TSS < 50 mg liter1 (inset), the frac-
tion of TSS that is volatile varies from 0 to >90 percent.
Total volatile suspended solids (TVSS) calculated as
total suspended solids minus fixed suspended solids,
that is, the mass remaining after combustion. Fraction
total volatile suspended solids calculated as total volatile
suspended solids divided by total suspended solids.
Data from Chesapeake Bay Water Quality Monitoring
Program, Virginia tidal tributary stations, 1994-1996.
35 H
POC (g rrf3)
\ I ' I \ T I
0 20 40 60 80 100 120 140
Chlorophyll a (mg m ~3)
FIGURE IV-4. Relationships of Total Volatile
Suspended Solids, Particulate Organic Carbon and
Chlorophyll. Concentration of total volatile suspended
solids (TVSS) as a function of particulate organic carbon
(POC) for Virginia tidal tributary stations, 1994-1996.
Line shows estimate of TVSS as POC/0.3 (A).
Relationship of particulate organic carbon to chlorophyll
concentration for Virginia tidal tributary stations (B).
Lines bracket approximate contribution of phytoplankton
to POC based on a range of phytoplankton
carbon:chlorophyll ratios from 20 (dashed line) to 80 mg
C (mg chlorophyll)'1 (solid line).
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 47
phytoplankton varies widely (Geider 1987). A range of
about 20 to 80 mg C (mg chl)"1 provides a lower bound
of most of the points in Figure IV-4B, and this range is
well within the physiological limits of phytoplankton
(Geider 1987). Choosing 40 mg C (mg chl)'1—the
geometric mean of 20 and 80-as a representative car-
bon:chlorophyll a ratio, and using the 30 percent par-
ticulate organic carbonrtotal volatile suspended solids
ratio from Figure IV-4A, the minimum contribution of
phytoplankton chlorophyll a to total volatile sus-
pended solids, designated ChlVS, is estimated as
ChlVS = 0.04[Chl]/0.3
(IV-11)
where the 0.04 results from the conversion of fig to mg
chlorophyll liter1. Thus, although the optical effects of
particulate organic detritus cannot be distinguished from
that of mineral particles, the minimum contribution of
phytoplankton to the measurement of total suspended
solids is approximately given by ChlVS. This also implies
that management action to reduce the concentration of
chlorophyll a at a site will also result in a reduction of
total suspended solids by an amount approximated by
ChlVS. The actual reduction may be larger if a substan-
tial heterotrophic community and the organic detritus
generated by it are simultaneously reduced. This obser-
vation has significant implications for the implementa-
tion of site specific management approaches.
SUMMARY OF THE DIAGNOSTIC TOOL
The exponential decline of light intensity under water
(Equation IV-1) allows for the percentage of surface
light penetrating to a given depth to be written as a
simple function of the diffuse attenuation coefficient
(Equation IV-2). Equation IV-4 expresses in a general
(albeit approximate) way the relationship between the
diffuse-attenuation coefficient and the concentrations
of optical water quality parameters. Once the SAV
minimum light requirement and the SAV restoration
depth are specified, Equation IV-4 may be rearranged
to predict the concentrations of total suspended solids
and chlorophyll a that exactly meet the water-column
light requirement (Equation IV-6). Equations IV-9a-c
express these water quality relationships for mesoha-
line and polyhaline regions for three depth ranges, and
in terms of the specific-attenuation coefficients esti-
mated for Chesapeake Bay from the literature, by
optical modeling and by analysis of data from the
Chesapeake Bay Water Quality Monitoring Program.
Equations IV-lOa-c express the same relationships for
tidal fresh and oligohaline regions. Equation IV-11
estimates an approximate minimum concentration of
total suspended solids attributable to phytoplankton.
Equation IV-11 is used to better predict the reduction
in total suspended solids, and, therefore, the diffuse
attenuation expected to occur when the chlorophyll a
concentration is reduced.
APPLICATION OF THE DIAGNOSTIC TOOL
A plot of measured total suspended solids against
chlorophyll a concentrations from a given station in
relation to lines defined by equations IV-9a-c demon-
strates the extent to which the water-column light
requirement is met at that location. In addition, a line
representing Equation IV-11 shows the minimum con-
tribution of chlorophyll a to total suspended solids at
the location. Three examples from the Chesapeake
Bay Water Quality Monitoring Program demonstrate
information that may be determined by examining
plots of total suspended solids against chlorophyll a in
relation to the restoration depth-based water-column
light requirements (Figure IV-5).
Suspended Solids Dominant Example
At station CB2.2, located in the upper Chesapeake
Bay mainstem near the turbidity maximum, total sus-
pended solids dominates the variability in light attenu-
ation (Figure IV-5 A). The median water quality
concentrations fail to meet the 1-meter water-column
light requirement, and the predominant direction of
variability in the scatter of individual data points is ver-
tical, i.e., parallel to the total suspended solids axis.
Stations characterized by elevated total suspended
solids and low chlorophyll a indicate cases in which
suspended solids dominate the variation in light atten-
uation. Depending upon site-specific factors, the
source of suspended solids may be due to land-based
erosion, channel scour and/or the resuspension of bot-
tom sediments due to winds or currents. When meas-
urements are principally parallel to the total
suspended solids axis, reductions in total suspended
solids will be needed to achieve light conditions for
SAV survival and growth.
Phytoplankton Bloom Example
Variations in chlorophyll a dominate the variability in
attenuation at tidal tributary Chesapeake Bay Water
-------
48 SAV TECHNICAL SYNTHESIS II
A Upper Bay, CB2.2
0 102030405060708090
Chlorophyll a (mg m ~3)
B. Baltimore Harbor, MWT5.1
40
Measured
Median
0.5m WCLR
1 m WCLR
2 m WCLR
15
12
o~^
'E 9H
O)
C. Lower mesohaline, CB5.2
0
0 30 60 90 120 150 180
Chlorophyll a (mg m ~3)
0
30
60
90
-3x
Chlorophyll a (mg m " )
FIGURE IV-5. Application of the Diagnostic Tool Illustrating Three Primary Modes of Variation in the Data.
Application of diagnostic tool to two mainstem Chesapeake Bay stations and one tributary station, which
demonstrate three primary modes of variation in the data: (A) variation in diffuse attenuation coefficients is
dominated by (flow related) changes in concentrations of total suspended solids (TSS) (upper Bay station CB2.2);
(B) variation in attenuation coefficients is dominated by changes in chlorophyll concentration (Baltimore Harbor,
MWT5.1); and (C) maximal chlorophyll concentration varies inversely with TSS indicative of light-limited phytoplank-
ton. Plots show (points) individual measurements and (asterisk) growing season median in relation to the water-
column light requirements (WCLR) for restoration to depths of 0.5 m (short dashes), 1.0 m (solid line), and 2.0 m
(dotted line); water-column light requirements calculated by equations IV-9a-c (see text). Note the change in scale.
Approximate minimum contribution of chlorophyll to TSS (ChIVS) is calculated by Equation IV-11 (long dashes).
Data from Chesapeake Bay Water Quality Monitoring Program, April through October, 1986-1996.
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 49
Quality Monitoring Program station MWT5.1 in Balti-
more Harbor, Maryland (Figure IV-5B). Median con-
centrations indicate that conditions for growth of SAV
to the 1-meter depth are met, but many individual
points violate both the 1-meter and 0.5-meter water-
column light requirements (Figure IV-5B). The main
orientation of points that violate 1-meter and 0.5-
meter water-column light requirements is parallel to
the ChlVS line (Figure IV- 5B, long dashes). Stations
with elevated chlorophyll a concentrations that exhibit
variability parallel to the ChlVS line can be classified
as nutrient-sensitive, because attenuation is often
dominated by phytoplankton blooms, indicating a sus-
ceptibility to eutrophication. Reduction of chlorophyll
a concentrations would simultaneously reduce total
suspended solids, moving the system parallel to the
ChlVS line.
Light-Limited Phytoplankton Example
Another recognizable pattern exhibited in the data is
an apparent upper bound of total suspended solids
and chlorophyll a concentrations, aligned parallel to
the water-column light requirements seen at mainstem
Chesapeake Bay Water Quality Monitoring Program
station CB5.2 (Figure IV-5C). Such behavior indicates
that the maximal phytoplankton chlorophyll a concen-
trations are dependent on total suspended solids con-
centrations, and that the phytoplankton are
light-limited (i.e., nutrient-saturated). Under those
conditions, reducing suspended solids concentrations
alone would not improve conditions for SAV, since
phytoplankton chlorophyll a would increase propor-
tionately to maintain the same light availability in the
water column. This process is well-described by
Wofsy's model (1983), in which water-column or mix-
ing-layer depth is an important parameter. Applica-
tion of Wofsy's (1983) Equation 17 with the
specific-attenuation coefficients in Equation IV-7
(above) suggests that the community exhibits nutrient-
saturated behavior with a mixing depth of 6 to 7
meters. The data indicate that conditions for growth of
SAV to 1 meter are nearly always met at CBS.2, but if
water with these properties were advected to shallower
areas and maintained sufficient residence time there,
it would support higher chlorophyll a concentrations.
It is, of course, possible for a system to display all three
modes of behavior at a given location, particularly
where there is strong seasonal riverine influence. For
example, high total suspended solids and low chloro-
phyll a might be observed at spring flooding; nutrient-
saturated behavior might occur as total suspended
solids concentrations decline after spring floods sub-
side; and blooms aligned parallel to the ChlVS line
could occur in response to episodic inputs of nutrients
at other times. Alignment along any of the trajectories
described need not occur as a sequence in time. That
is, floods, phytoplankton blooms, or nutrient-saturated
combinations of total suspended solids and chloro-
phyll a in separate years will generally tend to align in
the directions indicated in Figure IV-5. However,
because of the high degree of seasonal and interamrual
variability in such data, these patterns might not be
discernible at many stations, especially shallow loca-
tions where nutrient-saturated combinations of total
suspended solids and chlorophyll a might be indistin-
guishable from phytoplankton blooms.
Generation of Management Options
A computer spreadsheet program for displaying data
and calculating several options for achieving the
water-column light requirements has been developed
and has been made available in conjunction with this
report through the Chesapeake Bay Program web site
at www.chesapeakebay.net/tools. The spreadsheet
program calculates median water quality concentra-
tions, and evaluates them in relation to the minimum
light requirements for growth to 0.5-, 1- and 2-meter
restoration depths. Provisions are included for specify-
ing a value for the water-column light requirement
(WCLR) appropriate for mesohaline and polyhaline
and regions (WCLR=0.22) or for tidal fresh and
oligohaline areas (WCLR=0.13). When the observed
median chlorophyll a and total suspended solids con-
centrations do not meet the water-column light
requirement, up to four target chlorophyll a and total
suspended solids concentrations that do meet the cri-
teria are calculated based on four different manage-
ment options (Figure IV-6). Under some conditions,
some of the management options are not available
because a 'negative' concentration would be
calculated.
Option 1 is based on projection from existing median
conditions to the origin (Figure IV-6A). This option
calculates target chlorophyll a and total suspended
solids concentrations as the intersection of the water-
column light requirement line with a line connecting
the existing median concentration with the origin, i.e.,
chlorophyll=0, TSS=0. Option 1 always results in pos-
itive concentrations of both chlorophyll a and total
suspended solids.
-------
50 SAV TECHNICAL SYNTHESIS II
A. Projection to Origin
B. Normal Projection
.• Median
conditions
Target
.. .Habitat
•'' requirement
CO
CO
N/A .•••
Median
conditions
Target--.
s N/A
Chlorophyll a
C. TSS Reduction Only
Chlorophyll a
D. Chlorophyll Reduction Only
CO
CO
Median
conditions
Target
N/A
co
CO
N/A
Median
conditions
Target'
Chlorophyll a
Chlorophyll a
FIGURE IV-6. Illustration of Management Options for Determining Target Concentrations of Chlorophyll a and
Total Suspended Solids. Illustration of the use of the diagnostic tool to calculate target growing-season median
concentrations of total suspended solids (TSS) and chlorophyll a for restoration of SAV to a given depth. Target con-
centrations are calculated as the intersection of the water-column light requirement, with a line describing the reduc-
tion of median chlorophyll a and TSS concentrations calculated by one of four strategies: (A) projection to the origin
(i.e. chlorophyll a =0, TSS=0); (B) normal projection, i.e. perpendicular to the water-column light requirement;
(C) reduction in total suspended solids only; and (D) reduction in chlorophyll a only. A strategy is not available (N/A)
whenever the projection would result in a 'negative concentration'. In (D), reduction in chlorophyll a also reduces
TSS due to the dry weight of chlorophyll a, and therefore moves the median parallel to the line (long dashes) for
ChIVS, which describes the minimum contribution of chlorophyll a to TSS.
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 51
Option 2 is based on normal projection (Figure IV-
6B). It calculates target chlorophyll a and total sus-
pended solids concentrations as the projection from
existing median conditions perpendicular to the water-
column light requirement. Geometrically, option 2 is
the one that requires the least overall reductions in
chlorophyll a and total suspended solids concentra-
tions. In practice, target chlorophyll a and total
suspended solids concentrations for the normal pro-
jection, when permissible (i.e., no negative con-
centrations are calculated), are frequently very similar
to those calculated in option 1 using projection to
the origin.
Option 3 is based on a total suspended solids reduc-
tion only (Figure IV-6C). This option calculates target
chlorophyll a and total suspended solids concentra-
tions assuming the target can be met by only reducing
the concentration of total suspended solids. Option 3
is not available anytime the median chlorophyll a
exceeds the TSS=0 intercept. Whenever a system is
nutrient-saturated and light-limited, reduction of total
suspended solids alone poses the risk of relieving light
limitation and promoting further phytoplankton
growth. Such a tendency is indicated on the diagnostic
tool plot whenever data points tend to align parallel to
the water-column light requirements lines as illus-
trated previously in Figure IV-5C (Wofsy 1983).
Option 4 is based on a chlorophyll a reduction only
(Figure IV-6D). This option calculates target chloro-
phyll a and total suspended solids concentrations,
assuming that the target can be met by only reducing
the concentration of chlorophyll a (Figure IV-6D).
Option 4 is not available whenever the median total
suspended solids concentration exceeds the chloro-
phyll = 0 intercept of the water-column light require-
ment. The target total suspended solids concentration
reported for option 4 is actually lower than the existing
median, due to the suspended solids removed by
reduction of phytoplankton and associated carbon,
i.e., ChlVS.
SENSITIVITY OF TARGET CONCENTRATIONS
TO PARAMETER VARIATIONS
The sensitivity of target concentrations calculated by
each of the four management options was examined by
calculating the change in target concentrations of
chlorophyll a and total suspended solids in response to
a 20 percent increase in each of the parameters
(except Zmax) in Equation IV-5 that define the behav-
ior of the diagnostic tool (Table IV-3). The diagnostic
tool is formulated so that, in general, increases in
parameter values result in decreases in target concen-
trations. An increase in the water-column light
requirement increases the light required by SAV,
resulting in lower target concentrations of total
suspended solids and chlorophyll a. Increases in the
specific-attenuation coefficients increase the light-
attenuation coefficient, which reduces light availability
at Zmax, and, therefore, also reduces the target water
quality concentrations. Parameters in Equation IV-11
were an exception. Reduction in the ratios of particu-
late organic carbonxhlorophyll a and total volatile
suspended solidsrparticulate organic carbon resulted
in a negligibly higher target chlorophyll a concentra-
tion under option 1 (Table IV-3).
Reductions in target water quality concentrations were
by far the most sensitive to increases in WCLR (Table
IV-3). For management options 1 and 2, target chloro-
phyll a concentrations were reduced by about 27 per-
cent from about 22/Ag liter'1 to 16 jiig liter'1, and target
total suspended solids by about 29 percent from nine
mg liter'1 to 6.3 mg liter'1 with a 20 percent increase in
WCLR (Table IV-3). Management options 3 and 4
were eliminated by a 20 percent increase in WCLR
(Table IV-3). The large sensitivity to WCLR occurs
because an increase in WCLR moves the entire line
described by Equation IV-5 closer to the origin with-
out changing the slope, i.e., in a manner similar to
increases in Zmax (see Figure IV-5B).
The sensitivity of calculated target concentrations of
chlorophyll a and total suspended solids to 20 percent
increases in the remaining parameters in Equation IV-
5 differed according to management option and
parameter. Lowest target concentrations and greatest
percentage reductions for chlorophyll a occurred in
management option 4, i.e., chlorophyll a reduction
only. The target concentration of chlorophyll a was, of
course, insensitive to parameter variations under man-
agement option 3, total suspended solids reduction
only. For management options 1 and 2, the calculated
target chlorophyll a concentration was most sensitive
to the parameter ks, the specific-attenuation coeffi-
cient of total suspended solids, and relatively insensi-
tive to increases in kc, the specific-attenuation
coefficient of chlorophyll a. Insensitivity to kg may
seem counterintuitive, because kg governs the relative
contribution of chlorophyll a to overall attenuation,
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52 SAV TECHNICAL SYNTHESIS II
and we might expect higher values to attribute more
attenuation to chlorophyll a; but higher values of kc
also imply that a given reduction in chlorophyll a is
more effective in reducing overall JQj, and hence less
of a reduction is needed to achieve the target.
For total suspended solids, the lowest target concen-
trations and largest percentage reductions were calcu-
lated for option 3, total suspended solids reduction
only (Table IV-3). Target total suspended solids con-
centrations were slightly sensitive to parameter varia-
tions under management option 4, due to the
contribution of chlorophyll a to total suspended solids
as expressed in Equation IV-11. For management
options 1 and 2, the calculated target total suspended
solids concentration was most sensitive to ks. The
higher sensitivity to ks occurs because of the additional
contribution of chlorophyll a to total suspended solids,
so that increasing ks has an effect similar to that of
increasing the water-column light requirement.
SUMMARY AND CONCLUSIONS
The empirically observed exponential decay of light
underwater, which can be characterized by a single
attenuation coefficient, provides the means of deriving
a simple expression for the percentage of surface light
available to SAV at the bottom of a water column of
any specified depth. The magnitude of the attenuation
TABLE IV-3. Percent change of the target chlorophyll a and total suspended solids concentrations
calculated by the diagnostic tool in response to 20 percent increases in each of the parameters describing
the dependence of diffuse attenuation coefficient on water quality (Equation IV-8). Baseline parameter val-
ues and the value after the 20 percent increase are given under the parameter name and units. Data used
in the analysis were from the Maryland Chesapeake Bay Water Quality Monitoring Program for MWT5.1
station in Baltimore Harbor, restricted to the SAV growing season (April through October) 1986-1995.
Baseline target concentrations are those calculated for the 1 m restoration depth minimum light
requirement (Equation IV-9b) for each of the management options: 1-projection to origin; 2-normal
projection; 3-total suspended solids reduction only; and 4-chlorophyll a reduction only (see text).
N/A=not available.
Sensitivity of Management Option
Parameter varied:
Chorophyll a
2 3
Total Suspended Solids
234
Baseline target 22.18 22.85 34.1 11.6 8.93 8.81 6.9
10.73
WCLR
0.22 to 0.264
kc, (m2 mg')
0.016 to 0.0192
ks, (m2 g')
0.094 to 0.113
KW+DOC'^ '
0.32 to 0.384
POC:Chl(mg//g")
0.04 to .048
TVSS:POC
0.3 to 0.36
-28.2
-5.6
-25.8
-2.5
N/A
0.0
-12.4
-5.4
0.0
0.0
-14.2
-4.9
-3.8
-3.8
0.0
0.0
0.0
0.0
N/A -28.2 -29.2 N/A N/A
-10.1 -5.6 -7.4 -16.8 -1.5
-56.0 -12.3 -11.4 -16.7 -7.2
12.9 -5.4 -5.6 -9.9 -2.8
-14.9 -0.1 -1.9 0.0 -3.2
-10.1 0.0 1.7 0.0 -8.0
-------
Chapter IV - Factors Contributing to Water-Column Light Attenuation 53
coefficient is governed mainly by the concentrations of
three water quality parameters: dissolved organic car-
bon, chlorophyll a and total suspended solids. Of
these, only chlorophyll a and total suspended solids
show substantial contribution to light attenuation at
most locations around Chesapeake Bay. Sites where
colored dissolved organic matter contributes substan-
tially to attenuation, such as the Pocomoke River on
the Maryland/Virginia border, are not considered in
this analysis.
Linear partitioning of the diffuse-attenuation coeffi-
cient into contributions due to water plus dissolved
organic carbon, phytoplankton chlorophyll a and total
suspended solids involves known compromises in real-
ism but is an approximation that has proved useful in
the past and leads to a tractable solution for purposes
of water quality management. Due to unexplained
variability in the data from the Chesapeake Bay Water
Quality Monitoring Program, specific-attenuation
coefficients for water plus dissolved organic carbon,
chlorophyll a and total suspended solids were esti-
mated by a combined approach using statistical
regression, optical modeling and comparison with
literature values.
It will be shown elsewhere (Gallegos, unpublished) that
the use of a single linear regression (Equation IV- 4),
when applied across the full range of observed
water quality conditions, produces biased diffuse-
attenuation coefficients with respect to a more mecha-
nistic model of light attenuation. Nevertheless,
unbiased diffuse-attenuation coefficients can be
obtained from a suitably calibrated optical water
quality model. The present version of the diagnostic
tool incorporates unbiased diffuse-attenuation coeffi-
cients determined by an optical model calibrated for a
site near the mesohaline region of the mainstem Bay
(Gallegos 1994). There is an urgent need for a region-
ally customized application of this approach (see
"Directions for Future Research").
The diagnostic tool is based on a plot of measured
concentrations of total suspended solids versus chloro-
phyll a, in relation to the linear combination of total
suspended solids and chlorophyll a that meet the min-
imum light habitat requirement. Characteristic behav-
iors can be identified by the orientation of points:
points scattered along the vertical (TSS) axis indicate
attenuation dominated by episodic inputs of total sus-
pended solids; points oriented parallel to the line
defining the contribution of chlorophyll a to total sus-
pended solids indicate variation of light attenuation
governed by phytoplankton blooms; and points ori-
ented parallel to the line describing the water-column
light habitat requirement indicate that maximal
chlorophyll concentrations are dependent on the
concentration of total suspended solids, signifying a
nutrient-saturated system.
An analysis of total suspended solids indicated that
total volatile suspended solids were a variable fraction
of total suspended solids, and that on average, partic-
ulate organic carbon is about 30 percent of total
volatile suspended solids. Using a reasonable estimate
of the phytoplankton carbon:chlorophyll a ratio, along
with the contribution of particulate organic carbon to
total volatile suspended solids, indicated that phyto-
plankton carbon contributes to the overall total sus-
pended solids. Any reduction in chlorophyll a would
be accompanied by a proportionate decrease in total
suspended solids.
Up to four management options for moving the system
to conditions that meet specified water-column light
requirements are calculated by the diagnostic tool.
The precision of the calculations obviously implies a
degree of control over water quality conditions that
clearly is not always attainable. Nevertheless, report-
ing of four potential targets provides managers with an
overall view of the magnitude of the necessary reduc-
tions, and some of the tradeoffs that are available. Fur-
thermore, the spreadsheet reports the frequency with
which the water-column light requirements for each
restoration depth are violated by the individual meas-
urements. This information may be useful in the future
if water-column light requirements for SAV growth
and survival become better understood in terms of
tolerance of short-term light reductions.
Directions for Future Research
Continued collection of monitoring data is necessary
to track recovery (or further degradation) of the sys-
tem with respect to the optical water quality targets
defined for the various regions using the diagnostic
tool. However, it is doubtful that additional monitor-
ing data will improve the ability to derive statistical
estimates of specific-attenuation coefficients by
regression analysis. Inherent variability in the spectral
absorption and scattering properties of the optical
water quality parameters, combined with normal
-------
54 SAV TECHNICAL SYNTHESIS II
uncertainty associated with sampling and laboratory
analyses, probably account for the low coefficients of
determination and statistically insignificant estimates
of some specific-attenuation coefficients.
Nevertheless, some attempt to determine regionally
based estimates of optical properties should be made,
because of the pronounced changes in the nature of
particulate material that occur from the headwaters to
the mouth of major tributaries as well as the mainstem
Chesapeake Bay itself. An approach based on direct
measurement of particulate absorption spectra and
optical modeling will be needed to obtain regionally
customized diagnostic tools.
-------
CHAPTER V
Epiphyte Contributions to Light
Attenuation at the Leaf Surface
Building from the diagnosis and quantification of
water-column contributions to attenuation of
light, the present chapter focuses specifically on how
changes in water quality variables alter the light avail-
able at SAV leaves and considers effects of light atten-
uation resulting from substances both in the overlying
water column (phytoplankton, suspended particles
and dissolved organics) and attached to SAV leaves
(epiphytic algae, organic detritus and inorganic parti-
cles). A simple model is developed to calculate photo-
synthetically available radiation (PAR) at the leaf
surface for plants growing at a given restoration depth
(Z) under specific water quality conditions. The com-
puted value for PAR at the plant leaves is compared to
a target "minimum light requirement" for SAV sur-
vival, which is defined in Chapter VII of this report.
The overall objective is to apply this model using water
quality monitoring data to estimate growing season
mean light levels at SAV leaves for a particular site or
geographic region. The calculated light levels at SAV
leaves are then compared to the applicable minimum
light requirement value to assess whether water qual-
ity conditions are suitable to support survival and
growth of SAV. The relative contributions of water
column vs. epiphytic substances in attenuating incident
light to SAV leaves are also computed. The scientific
basis of this model is described here in some detail.
Numerous models have been developed previously for
making theoretical computations of SAV growth con-
sidering light attenuation by water-column materials
only (e.g., Best 1982; Zimmerman et al. 1987) or
water-column plus epiphytic substances (Wetzel and
Neckles 1986; Hootsman 1991; Bach 1993; Kemp et al.
1995; Madden and Kemp 1996; Fong et al. 1997).
Other studies have described simple statistical models
for predicting depth distribution of SAV in relation to
variations in observed data on water column trans-
parency (e.g., R0rslett 1987; De Jong and De Jong
1992; Scheffer et al. 1993). The model described in this
chapter combines these approaches to calculate, from
field observations, light available for SAV survival and
growth, considering light attenuation from both water
column and epiphytic materials.
APPROACH AND METHODOLOGY
To compute median PAR at the leaf surface of SAV,
the model requires SAV growing season medians for
four water quality variables: 1) dissolved inorganic
nitrogen (nitrate + nitrite + ammonia), or DIN; 2)
dissolved inorganic phosphorus (primarily phosphate),
or DIP; 3) total suspended solids (TSS); and 4) diffuse
downwelling PAR attenuation coefficient (Kj). Values
for Kd are either obtained from direct measurements
of PAR decrease with water depth using a cosine-
corrected sensor, or they are calculated from observa-
tions on the depth at which a Secchi disk disappears
(see Chapter III for the Secchi depth/Kd conversion).
An implicit assumption in this analysis is that light
(PAR) availability is the primary environmental factor
that limits SAV survival and growth in temperate
coastal waters (Duarte 199la; Dennison et al. 1993;
Zimmerman et al. 1995). In the model, light is attenu-
ated by dissolved and particulate materials in the
water column (Chapter IV) and by biotic and abiotic
epiphytic materials accumulated on SAV leaves.
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 55
-------
56 SAV TECHNICAL SYNTHESIS II
The light attenuation to the SAV leaf surface is calcu-
lated using an exponential equation, with a depth-
dependent term for water column shading and a
mass-specific term for epiphyte attenuation. These are
standard equations widely used in aquatic science
(Kirk 1994) and ecosystem modeling (e.g., Hootsman
1991; Madden and Kemp 1996). The depth of the site
is defined by the local bathymetry and the "target
depth" for SAV restoration. Specific targets and for-
mally adopted goals for restoration of SAV in Chesa-
peake Bay, originally defined and quantified in Batiuk
et al (1992) and Dennison et al (1993), are summa-
rized in Chapter VIII.
Specifically, the model calculates PAR at the SAV leaf
surface for a given water depth (Izs) as a fraction of the
incident radiation at the water surface (I0) using the
following formulation:
There are four variables on the right side of this equa-
tion: 1) the water-column PAR attenuation coefficient,
Kd ; 2) the depth of leaves growing up from sediments
at the lower edge of a potential SAV habitat, Z; 3) the
biomass of epiphytic algae growing on SAV leaves, Be;
and 4) the biomass-specific PAR attenuation coeffi-
cient for epiphytic algal material, Kg.
The model user defines Z with the assumption that
SAV must grow upward from the sediment surface
early in the growing season. As the plants grow upward
and shoots get closer to the water surface, they begin
to self-shade, which is not considered directly in this
analysis. Kd is an input variable derived from field
monitoring data. The model computes Be from input
water quality monitoring data on dissolved inorganic
nitrogen, dissolved inorganic phosphorus, Kd and the
selected value for Z. The fourth variable, Ke, is esti-
mated from two statistical correlations derived from
experimental data (Staver 1984) and field observations
in oligohaline and mesohaline regions of the Potomac
and Patuxent River estuaries (Carter et al, unpub-
lished data; Boynton et al, unpublished data) and in
the mesohaline and polyhaline reaches of the York
River estuary (Neckles 1990). The first correlation is
between Kg and the ratio Be/Bde, where Bde is the total
dry weight of epiphytic material (both algal and other
material per dry weight of SAV leaf). The ratio, Be/Bde,
is itself calculated from a second statistical relation-
ship with total suspended solids, using total suspended
solids water quality monitoring data as input to the
computation.
MODEL DESCRIPTION
In this section, each step in the model calculation is
explained and its derivation described (Table V-l). All
key assumptions are stated explicitly, and their im-
plications are discussed. The model is based on the
relation described in Equation V-l, where light (as a
fraction of that at the water surface) is attenuated by
two exponential relations. One of these relations
[e"(Kd)(Z)] accounts for attenuation by the water overly-
ing SAV leaves and dissolved and suspended materials
contained in that water, and the other term [e"(Ke)(Be)]
accounts for effects of materials accumulated on
SAV leaves.
Most of the model description that follows explains
how the second of these terms was derived from a
combination of statistical relations and numerical
model simulations. First, a description is provided on
how an estimate of potential biomass of epiphytic
algae is calculated from nutrient concentrations and
other water quality measurements. Next, an approach
is described for estimating a biomass-specific PAR
attenuation coefficient for epiphytic material (Ke) in
relation to the ratio of epiphytic algal biomass (Be, as
chlorophyll a) to total dry weight of material (Bde) on
SAV leaves. Then a statistical correlation is described
for estimating the ratio (Be/Bde) in relation to water
quality conditions.
Computing Epiphytic Algal Biomass (Be)
from Nutrient Concentration
A numerical ecosystem simulation computation is
used in the first three steps of the overall model to
compute growth of epiphytic algal biomass as a func-
tion of nutrient concentrations (e.g., Twilley et al 1985;
Borum 1985) and light availability (e.g., Short et al.
1995; Moore et al 1996). This numerical submodel
(adapted from Kemp et al 1995 and Madden and
Kemp 1996) is used to calculate mean epiphytic algal
biomass from input data on dissolved inorganic nutri-
ent concentrations, water depth and Kd. The numeri-
cal model also takes into account other environmental
factors including temperature (Madden and Kemp
1996), grazing on epiphyte biomass (Hootsman and
Vermaat 1985; Jernakoff et al. 1996) and water
-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 57
TABLE V-1. Summary of the approach used to estimate photosynthetically available radiation at the leaf
surface of submerged aquatic vegetation using water quality data routinely monitored in Chesapeake Bay.
Step in Model Calculation
Functional Relationship
Input Data
Source of Model
Relationship
Units
1) Decide limiting nutrient
DIN/DIP > 16, use DIP
DIN/DIP £16, use DIN
DUST, DIP
Fisher et al. 1992
2) Derive general equation
to calculate epiphyte biomass
B, = (Be)m [1+208 (DIN-"***)]'1
• (Be)m = maximum Be value
J = characteristic coeff.
DIN, DIP
Numerical model
(Madden and Kemp
1996)
DIN.//M
J , none
3) Calculate PAR effect on KN(OD)
and (B.)«
(B.). = 2.2- [0.251(OD123)]
• OD = Optical Depth = K, (Z)
) = 2.32(1-0.031OD142)
K..Z
Numerical model
(Madden and Kemp
1996)
Z, m
4) Calculate epiphyte dry weight
B* = 0.107 TSS + 0.832 B,
5) Calculate epiphyte biomass-
specific PAR attenuation coeff.
K, = 0.07 + 0.32 (B, /B* )*»
TSS Regression from
B, experimental data
(e.g., Staver 1984)
Be, Regression from
B«fe experimental and field
data
TSS, mg T1
B,, mg chl gdw1
B^.gdwgdw'1
B,, //g chl cm-2
Bj,, mg dw cm*2
K«, cm2jug chl'1
6) Calculate PAR at SAY leaves (U
Install Equation Editor and double-
[ n —click here to view equation.
7) Compare SAY leaf PAR with
Minimum Light Requirement
DIN, DIP, K,,,
TSS,Z
Combining steps #1-5
(from above)
See Chapter VH
DIN.^M
DIP.//M
TSS, mg I-1
Ka.OT1
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58 SAV TECHNICAL SYNTHESIS II
exchange rate (Sturgis and Murray 1997). Earlier ver-
sions of this numerical model were calibrated using
data from field sites in Chesapeake Bay (Kemp et al.
1995; Madden and Kemp 1996). The numerical model
used in this analysis was calibrated using data from
both field sites (e.g., Lubbers et al. 1990) and con-
trolled mesocosm experiments (Sturgis and Murray
1997).
The molar ratio of concentrations (SAV growing sea-
son mean values) of dissolved inorganic nitrogen to
dissolved inorganic phosphorus (DIN:DIP) is com-
pared to the Redfield ratio of 16:1 (Redfield 1934) to
select which nutrient should be used in the analysis
(Table V-l). Here, a single limiting nutrient is
assumed. If the molar ratio is < 16, dissolved inorganic
nitrogen data are used; if the ratio is > 16, dissolved
inorganic phosphorus data are used. This assumption
is generally consistent with observations from Chesa-
peake Bay algal bioassay and mesocosm experiments
(D'Elia et al. 1986; Neundorfer and Kemp 1993; Fisher
et al. 1992, 1998).
The numerical ecosystem simulation was used to com-
pute a family of sigmoidal shaped curves relating
nutrient concentration to epiphyte biomass, with dif-
ferent curves for different water column light regimes
(Figure V-l). Model biomasses are calculated in terms
of organic carbon, so epiphytes are reported here as g
C epiphyte g C SAV'1 (Table V-l). Light regimes are
characterized by the "optical depth," which is the
product of Kd times the water depth Z (e.g., Kirk
1994). It can be seen that changes in optical depth
have a more pronounced effect on the maximum epi-
phyte biomass attained than on nutrient responses at
low dissolved inorganic nitrogen concentrations. Con-
sistent patterns are evident in the family of curves gen-
erated by this model, and these can be described by the
following general function:
Be = (Be)
208(DIN-KN)]-
(V-2)
where the two rate coefficients (Be)m and KN(OD) are
the maximum possible epiphytic algal biomass (ulti-
mately limited by space) and a shape coefficient
describing the Be vs. dissolved inorganic nitrogen
(DIN) relationship, respectively. As the optical depth
(OD = Z»Kd) increases, values for (Be)m decrease,
while values for KN(OD) increase.
2.5
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20 40
Total DIN
60
80
FIGURE V-1. Epiphytic Algal Biomass Responses
to Varying DIN and Light Conditions. Calculated
responses of epiphytic algal biomass (Be, mg C/mg C
SAV) to changes in dissolved inorganic nitrogen (DIN)
concentration under varying light conditions in estuarine
waters of Chesapeake Bay. Each curve represents esti-
mated response under specific light regimes, character-
ized by different optical depths (OD = K
-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 59
(Bp)m = 2.2-(0.251 -OD1-23)
0
o Model Output
= 2.32(1-(0.031xOD1-42))
01234
Optical Depth (O D), Kg • Z
FIGURE V-2. Model Predicted Responses of Epiphytic
Algal Biomass to Changes in Optical Depth and DIN
Concentrations. Model predicted the effects of changing
optical depth (OD = Kd • Z) on coefficients describing
response of epiphytic algal biomass Be to changes in
dissolved inorganic nitrogen (DIN) concentrations (see
Fig. V-1). The coefficient (Be)m is the maximum possible
value for Be at a given OD (upper panel, A), and KN(OD>
is a coefficient describing the shape of the Be versus
DIN relationship (lower panel, B).
There are a limited number of complete data sets
available for testing these relations between nutrient
concentration, light availability and epiphyte biomass.
This is, in part, because of the difficulty in obtaining
nutrient data integrated over appropriate time scales
to coincide with epiphytic algal growth (e.g., Sand-
Jensen and Borum 1991; Duarte 1995). Data used as
inputs to equations V-2 through V-4 to calculate
epiphyte biomass were measurements of dissolved
inorganic nitrogen, dissolved inorganic phosphorus,
Kd and Z (averaged over the course of the studies)
from two field sites in Chesapeake Bay tidal tributar-
ies—the Potomac River Estuary (Carter et al, unpub-
lished data) and the Patuxent River Estuary (Boynton
et al., unpublished data)—and from a recent meso-
cosm experiment (Sturgis and Murray 1997).
Epiphyte biomass measurements are based on artifi-
cial substrates deployed and retrieved in the two refer-
enced field studies and on direct measurements from
leaves of Potamogeton perfoliatus in the referenced
mesocosm experiments. All biomass measurements
were converted from chlorophyll a to carbon units
using measured chlorophyll arcarbon ratios. The
model assumed a constant (mean) value for SAV
biomass over the course of a 60-day simulation.
Although there were only eight separate data points
for this comparison, the "predicted" (PRED) biomass
values compared well to measured (OBS) values
(Figure V-3). There appears to be a slight bias, where
the prediction tends to underestimate measured val-
ues at moderate biomasses; however, the relationship
is statistically significant (OBS = 0.21 + 0.93 PRED,
r2 = 0.81).
Relationships between epiphytic algal biomass and
nutrient concentrations or loading rates previously
have been reported for a wide range of conditions.
While most of these are from experimental manipula-
tions (Philips et al. 1978; Twilley et al. 1985; Neundor-
fer and Kemp 1993; Neckles et al. 1993; Williams and
Ruckelshaus 1993; Short et al. 1995; Sturgis and Mur-
ray 1997), several field studies also revealed positive
relations between nutrients and epiphytic algal bio-
mass (Borum 1985; Cattaneo 1987; Lapointe et al.
1994). Many recent studies have emphasized the
importance of invertebrate grazing as a control on epi-
phytic algal biomass (e.g., Cattaneo 1983; Orth and
van Montfrans 1984; Hootsman and Vermaat 1985;
Howard and Short 1986), and other studies suggest
that heavy grazing pressure may preclude epiphytic
algal responses to nutrients (Neckles et al 1993; Jer-
nakoff et al. 1996; Alcoverro et al. 1997). Results of
other recent studies have indicated that muted epi-
phytic algal responses to nutrient enrichment may also
result from shading associated with phytoplankton
growth (e.g., Taylor et al. 1995; Short et al. 1995; Lin et
al. 1996) or other sources of turbidity (Moore et al.
-------
60 SAV TECHNICAL SYNTHESIS I
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-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 61
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2.5
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-------
62 SAV TECHNICAL SYNTHESIS I
epiphytic algal biomass-specific PAR attenuation
coefficient, Ke (cm2 /ig chla"1). These studies were
associated with various SAV species, including Pota-
mogeton perfoliatus (Staver 1984; Twilley et al. 1985;
Neundorfer and Kemp 1993), P. pectinatus (Vermaat
and Hootsman 1994; van Dijk 1993), R. maritima
(Twilley et al. 1985), Z. marina (Sand-Jensen and
Borum 1983; Neundorfer and Kemp 1993; Neckles et
al., unpublished), Heterozostera tasmanica (Bulthuis
and Woelkerling 1983a), Posidonia australis (Silber-
stein et al. 1986) and Thalassia testudinum (Kemp et al.
1989; Dixon and Leverone 1995).
Unfortunately, these studies used four different con-
ventions for units of measure of epiphyte abundance:
1) /ig chlorophyll a cnr2 (leaf); 2) mg dry weight cm'2
(leaf); 3) mg ash-free dry weight cm"2 (leaf); 4) g dry
weight epiphyte per g dry weight (SAV leaves). Infor-
mation on plant morphology was used to convert
between leaf area and dry weight (e.g., Duarte 1991b),
and observed carbonrchlorophyll a ratios (e.g., Staver
1984) were used to convert between /u,g chlorophyll a
and mg ash-free dry weight for epiphytic material.
Although it was anticipated that values of attenuation
coefficients would converge from the different
sources, this was not the case. Estimates of Ke varied
as much as two- to threefold, expressed either in terms
of epiphytic algal chlorophyll a or total dry weight of
epiphytic material.
One factor contributing to the widely varying esti-
mates of Kg appears to be the composition of epiphytic
material in terms of relative contributions of algal bio-
mass, detritus and inorganic particles. Although the
ratio of epiphytic algal biomass to detrital epiphytic
matter may vary somewhat over the course of a grow-
ing season (Staver 1984), it was assumed that epiphytic
algal biomass would serve as an index of contributions
of both living and non-living organic matter to total Kg.
However, because of the highly dynamic nature of
resuspension and deposition (e.g., Ward et al. 1984), it
was assumed that the contribution of inorganic solids
to Kg could vary widely from site to site, depending on
hydrographic and sedimentological conditions. Pre-
sumably, these inorganic materials are resuspended
from bottom sediments, transported into SAV beds
and deposited onto SAV leaves, where they may be
incorporated into the epiphytic matrix (e.g., Brown
and Austin 1973; Ward et al. 1984; Kendrick and
Burt 1997).
In an experimental study, Staver (1984) considered
how Kg varied with the ratio of epiphyte biomass (Be,
mg chlorophyll a cm"2 substrate) to total dry weight
(Bde, g dw cm"2 substrate). Here, nearly 100 simultane-
ous observations of K», Ee, and B^ were separated into
four groups based on this ratio (Be: B^e). While Staver
(1984) found great variance when all observations were
pooled, highly significant correlations between Be and
PAR attenuation (the slope of which is Kg) were
observed when the data were separated (according to
the ratio of biomass to dry weight) into the four groups.
Recent field studies in two tidal tributaries of Chesa-
peake Bay—the Potomac River estuary (Carter et al.
unpublished) and the Patuxent River estuary (Boynton
et al unpublished)—have provided an expanded data
base. These field data were combined with the previ-
ously described mesocosm data (Staver 1984) to gen-
erate a significant inverse relationship between Kg and
Be:Bde (Figure V-5),
= 0.07 + 0.322 (Be /Bde )"°-88
(V-5).
0)
c
o
•<4— »
CD
3
C
£
on
Q.
O
Q.
'a.
LU
TO
2
E
o
0
K =0.07+0.322(Be/Bde)
w
(r2 = 0.73)
-0.88
024
Epiphyte Composition,
PQ Chi a (mg dw)'1
6
FIGURE V-5. Epiphytic Composition vs. Epiphytic
PAR Attenuation. Relationship between the com-
position of epiphytic material (fig chlorophyll-a
(mg dry weight)"1) and the biomass-specific PAR
(photosynthetically available radiation) attenuation
coefficient (cm2/^g chl-a"1) for epiphytes. Data are
pooled from a pond mesocosm experiment (Staver
1984) and from field studies in Patuxent River estuary
(Boynton et al., unpublished) and Potomac River
estuary (Carter era/., unpublished). In all cases, epi-
phytic material was measured on artificial substrates.
-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 63
Equation V-5 expresses a complex relationship in that
its slope is essentially a ratio of ratios. However, it
clearly indicates that the chlorophyll a-specific attenu-
ation coefficient, Kg, increases (in a non-linear way) as
the relative contribution of chlorophyll a-bearing
material decreases. Since light attenuation is meas-
ured per unit algal chlorophyll a, the increase in Kg
with decreasing values of Be: Bde (mg chl-a g dry wt"1)
is due to the light-attenuating effects of non-algal
materials. Thus, while IQ, appears to vary widely
among sites depending on physical conditions, it can
be predicted with confidence from data on the ratio of
epiphyte biomass to dry weight.
Similar hyperbolic relationships can be produced for
each of the three separate field and mesocosm data
sets. There was no statistically significant difference
among any of these, nor between any particular site
and the relationship illustrated in Figure V-5 for the
pooled data. At low values of the epiphyte composi-
tion ratio, Be: Bae < 0.5 mg chl-« g dry wt"1, Equation
V-5 is highly sensitive to small changes in that ratio.
However, applying field data on Be: Bde to Equation
V-5 illustrated that calculated values of Kg rarely
exceeded 1.5 cm2 jitg chl a'1.
The option of calculating the biomass-specific epiphyte
attenuation coefficient (Kg) in terms of total dry weight
of epiphytic material rather than algal chlorophyll a was
also explored. The dry weight-specific coefficient
yielded a significant, but slightly weaker, relationship
compared to that for chlorophyll a-specific attenuation.
Therefore, the chlorophyll a -specific attenuation coeffi-
cient was retained in the model because chlorophyll a is
a better measure of algal biomass, which is what is being
predicted in Equation V-2.
Estimating the Ratio of Epiphyte Biomass to
Total Dry Weight
The next step of the analysis involves deriving a means
for computing, from available water quality monitoring
parameters, the ratio of epiphytic algal biomass to total
dry weight of epiphytic materials. Toward this end, it
was postulated that the contribution of inorganic parti-
cles to total dry weight of epiphytic material would
increase with sediment resuspension and associated
water-column concentrations of total suspended solids.
Previous studies in Chesapeake Bay have shown that
rates of total suspended solids deposition in SAV beds
are proportional to SAV biomass and to total sus-
pended solids load (Ward et al. 1984). It was further
assumed that sedimenting particles would tend to be
trapped in the organic matrix of epiphytic material in
proportion to the biomass of algal epiphytes. In hydro-
dynamically active coastal environments, where SAV
plants are in constant motion, very little of the sinking
particles would adhere to leaves without the organic
'glue' associated with algal biomass. In fact, in a
Swedish lake, the total dry weight of epiphytic materi-
als (Bde) was inversely related to wave exposure
(Strand and Weisner 1996).
While there is no published quantitative description of
the relationship between total suspended solids, Be
and Bde, the assumed pattern is consistent with numer-
ous observations with SAV in field and experimental
conditions (e.g., Kemp et al. 1983; Twilley et al. 1985).
An existing data set was used to develop an empirical
relationship to calculate B^e [(g dw epiphytic material)
(g dw SAV)"1] from data on Be [(mg epiphyte chl-a)
(g dw SAV)"1] and concentrations of total suspended
solids (mg I"1]) in the adjacent water. This relationship
would allow values for a biomass-specific epiphytic
PAR attenuation coefficient (Kg) to be estimated from
the previous step in the analysis.
Simultaneous measurements of total suspended solids,
Bde and Be were available from a set of studies in
experimental ponds (Twilley et al. 1985; Staver 1984).
A significant (r2 = 0.85) relationship was observed
using the data,
Bde = 0.107 TSS + 0.832 Be
(V-6).
To test the robustness of Equation V-6, values for dry
weight of epiphytic material (Bde) predicted from the
equation were compared with measured values. A
highly significant fit was observed between model and
data (r2 = 0.85, Figure V-6), although predicted values
tend to underestimate observed values at intermediate
values of Bde (2-4 mg dw mg dw"1). An alternative non-
linear formulation with an interactive term (TSS • Be)
on the right side of the equation provided a substan-
tially poorer statistical fit.
Field data with simultaneous measurements of total
suspended solids, Bde and Be were much harder to
identify. Attempts were made to use data collected in
the Potomac and Patuxent River estuaries, where
observations were made on one- to three-week inter-
vals; however, these data yielded substantially weaker
-------
64 SAV TECHNICAL SYNTHESIS II
6
CO
I
?4
-a
?3
^»_*i
f
CD
•°2
T3
-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 65
In terms of total light reduction, this analysis revealed
that PAR levels were most sensitive, by far, to changes
in Kd. This is not surprising, given the fact that K' |
:l ^1
x2 xO.5 x2 xO.5 x2 xO.5 x2 xO.5
TSS DIN DIP fy
FIGURE V-7. Percent Light at the Leaf Sensitivity
Analysis. Sensitivity analysis for values of percent of
incident light at SAV leaf surface (PLL) calculated from
the spreadsheet model (Table V-1) in response to dou-
bling (x 2) and halving (x 0.5) concentrations of total
suspended solids (TSS), dissolved inorganic nitrogen
(DIN), and dissolved inorganic phosphorus (DIP), and
values for light attenuation coefficient (Kd). Calculated
values based on previously published SAV habitat
requirements (Batiukera/. 1992) are used as refer-
ences, with values of PLL and percent incident light in
water overlying SAV (PLW) shown as horizontal dashed
lines for A) tidal fresh and oligohaline, B) mesohaline
and C) polyhaline regions of Chesapeake Bay.
-------
66 SAV TECHNICAL SYNTHESIS II
50
40
520
10
0
PLL
incident)
0
10 15
TSS(mg H)
20
FIGURE V-8. Effects of DIN and TSS on Percent Light
at the Leaf. Interacting effects of dissolved inorganic
nitrogen (DIN) and total suspended solids (TSS) con-
centrations on percent incident light at SAV leaf surface
(PLL). Family of isolumes (lines of constant light) for PLL
of 10-20 percent calculated from the model described in
this report for a restoration depth of 1 m (see Table V-1).
solids would also impart substantial effects on Ka;
however, the purpose of this analysis was to isolate the
effects on epiphytic attenuation only.
The relative contribution of epiphytic material to total
PAR attenuation varies with depth and water column
turbidity. In the 1992 SAV habitat requirements for
the mesohaline and polyhaline regions of Chesapeake
Bay (DIN = 10 M, Ka = 1.5 ny1; Batiuk et al. 1992),
PAR attenuation by epiphytic material is approxi-
mately 25 percent of the total at 0.5 m and 10 percent
of the total at 1-meter depth (Figure V-9, upper
panel). This contribution decreases substantially at
lower ambient nutrient concentrations. At lower val-
ues of Kd (1.0 m"1) and 0.5 m water depth, epiphyte
contribution to total PAR attenuation increases to
almost 40 percent at DIN =10 M and 20 percent at
DIN = 2 M (Figure V-9, lower panel). These sensitiv-
ity calculations emphasize the fact that at Z > 0.5 m,
epiphytic material contributes substantially less to the
total shading of SAV than do materials suspended and
dissolved in the overlying water. However, in many
cases the additional reductions in ambient light associ-
ated with epiphytic accumulations is sufficient to
100
DIN concentration (\M)
DIN concentration (\M)
20
Depth (m)
FIGURE V-9. Effects of Water Depth, DIN, and Kd on
Epiphyte Contribution to PAR Attenuation. Effects of
water depth, dissolved inorganic nitrogen concentration
(DIN) and light attenuation (Kd) on relative contributions
of epiphytes to total PAR attenuation to SAV leaves.
Lines calculated from the model developed in this
report (see Table V-1).
reduce SAV growth below the minimum levels needed
for plants to survive (e.g., Kemp et al 1983; Twilley
etal 1985).
This spreadsheet model calculation of the percent
light reaching SAV leaf surface (PLL) was applied to
sites in the mainstem and tidal tributaries of Chesa-
peake Bay for field verification and to explore regional
patterns in the estuary. Growing season median values
for dissolved inorganic nitrogen, dissolved inorganic
phosphorus, total suspended solids and Kd measured
at Chesapeake Bay water quality monitoring stations
-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 67
within 2 to 5 km of existing and potential nearshore
SAV habitats were compiled from the period 1985-
1996 for stations in the mainstem Bay and from all
monitored tidal tributary and embayment estuaries.
These data are updated versions of those used for field
verification analyses presented previously (Batiuk et
al 1992).
Results of model computations are summarized (Fig-
ure V-10) in bar graphs as mean light levels calculated
at the SAV leaf surface (PLL, including epiphyte
attenuation) for all sites in the estuary. Values for PLL
were calculated at water depths of 1 meter and 0.5
meters (upper panel only). Results are summarized for
five categories of SAV abundance: 1) "always none";
2) "usually none"; 3) "sometimes some"; 4) "always
some"; and 5) "always abundant." These categories
are defined precisely in Chapter VII. Calculations are
also parsed into three salinity regions of the Bay:
1) tidal fresh/oligohaline; 2) mesohaline and 3) poly-
haline. No sites qualified for the "always none" cate-
gory in the polyhaline region, where SAV is generally
most abundant. Calculations are provided for water
depth of 0.5 m in the tidal fresh/oligohaline region
because of the prevalence of relatively shallow, broad
and protected sites in the upper Bay.
In general, there is a consistent pattern of increasing
light (PLL) with increasing probability of SAV occur-
rence (Figure V-10). The one exception is for the
"always abundant" category in the oligohaline region
(Figure V-10, upper panel). It is assumed that mini-
mum light required for SAV survival should fall
between the mean light levels associated with the
"sometimes some" and "always some" categories of
SAV abundance.
For the mesohaline and polyhaline regions, the mean
calculated PLL values range from 20-25 percent sur-
face irradiance for sites having SAV occurrence char-
acterized between "sometimes" and "always." This
analysis suggests that the target value of 15 percent
surface irradiance for SAV minimum light require-
ment derived from analysis of the literature serves as a
conservative but robust index of SAV habitat suitabil-
ity for these regions of Chesapeake Bay.
Light requirements in the tidal fresh and oligohaline
regions are more difficult to discern; however, for the
same SAV occurrence categories, calculated values for
PLL range from 4 to 7 percent at 1-meter water depth
40
30
10
-^40
A. Tidal Fresh & Oligohaline
ZZZIPLL at 0.5m
ilEIPLLat 1.0m
20 f ~ i
113-20%
1
i,
30-
03
U
CO
**-
CO
a.
20 -
Ot
B. Mesohaline
(PLL at 1.0m)
*2
3-25
%
40
30
20
10
C.
Polyhaline
(PLL at 1.0m)
20%
N/A
Always Usually Sometimes Always Always
None None Some Some Abundant
Relative SAV Abundance
FIGURE V-10. Calculated Percent Light at the Leaf
Values by Relative SAV Abundance by Salinity
Regime. Calculated mean values for percent incident
light at SAV leaf surface (PLL) for all water quality moni-
toring stations in the mainstem, tidal tributaries and
embayments of Chesapeake Bay during 1985-1996
grouped into five categories of relative SAV abundance
or occurrence and three salinity regimes. Values of PLL
were calculated for water depth of 0.5 and/or 1.0 m
using the model described in this report (see Table V-1)
with input data (total suspended solids, dissolved inor-
ganic nitrogen, dissolved inorganic phosphorus, Kd)
for SAV growing season (April-October for tidal fresh,
oligohaline, and mesohaline and March-May and
September-November for polyhaline) of each year from
the Chesapeake Bay Program water quality monitoring
program. N/A indicates that there were no sites in the
polyhaline region without occasional SAV presence.
-------
68 SAV TECHNICAL SYNTHESIS II
and from 13-20 percent at 0.5 meters. Since the mean
water depths for sites with SAV growing in these Bay
regions tend to fall between 0.5 meters and 1 meter,
the target value (derived from the literature review) of
9 percent surface irradiance is also very consistent with
mean light conditions calculated to support minimal
SAV growth. The tidal fresh and oligohaline regions of
the Bay are generally the most turbid (e.g., Schubel
and Biggs 1969; Keefe et al. 1976; Smith and Kemp
1995). The reduced consistency between light variabil-
ity and SAV occurrence in this turbid region of the Bay
(Figure V-10, upper panel) is consistent with observa-
tions in turbid lakes (Middleboe and Markager 1997).
Regional variations in the relative contributions of
water-column and epiphyte attenuation for defining
potential SAV habitats can be seen by comparing cal-
culated values for PLL and PLW at sites pooled into
different salinity regimes (Figures V-ll and V-12). In
general, values of both PLL and PLW tend to increase
as one moves from lower to higher salinity regions
(Figure V-ll; upper, middle and lower panels, respec-
tively). Although Figure V-ll presents data for Vir-
ginia portions of the Bay only, similar patterns are
evident for the Maryland waters of the Bay.
Sites in the tidal fresh and oligohaline regions appear
to have greater potential effects of light attenuation by
epiphytic material, as indicated by the data points
falling well below the 1:1 line (Figure V-ll). In these
low salinity regions, almost half of the total attenua-
tion is attributable to epiphytic materials. This is
because of the higher nutrient concentrations and
total suspended solids levels in lower salinity areas.
There is little difference in the relative contribution of
epiphytes in the mesohaline and polyhaline regions,
where the epiphyte effect [(PLW-PLL)/PLL] tends to
range from 25-40 percent and increases as PLL
decreases (Figure V-12). While there is a clear pattern
of changing contribution of epiphyte attenuation along
the estuarine salinity gradient, there is less of a marked
difference in the PLL vs. PLW relationship for upper
Bay (Maryland waters) compared to lower Bay
(Virginia waters) areas (Figure V-12). In both cases,
mean epiphyte contributions f(PLW-PLL)/PLL] range
from about 20-50 percent, and they are greatest at
more turbid sites. Thus, it is clear that at 1-meter water
depth, potential accumulation of epiphytic material
represents a significant fraction of total potential light
attenuation at sites throughout Chesapeake Bay.
60
50
40
30
20
10
0
A. Tidal Fresh & Oligohaline
0
u
•§
3
V)
M—
03
-------
Chapter V - Epiphyte Contributions to Light Attenuation at the Leaf Surface 69
Maryland Chesapeake Bay
and Tributaries
10 20 30 40 50 60
Virginia Chesapeake Bay
and Tributaries
10 20 30 40 50
Calculated PAR at SAV Canopy
(PLW, %l )
60
FIGURE V-12. Percent Light at the Leaf vs. Percent
Light Through the Water Column, by State.
Comparing values for percent surface light (PAR) at SAV
leaf surface (PLL) and percent surface light in water just
above SAV leaf (PLW) for all monitored sites in the main-
stem, tidal tributaries and embayments of Chesapeake
Bay during 1985-1996 grouped into upper (Maryland)
and lower (Virginia) estuary regions. Values of PLL and
PLW were calculated for water depth of 1 m using the
model described in this report (Table V-1) with input
monitoring data (total suspended solids, dissolved
inorganic nitrogen, dissolved inorganic phosphorus,
KCJ) for the SAV growing season of each year. Lines
indicate position of points where epiphyte attenuation
reduced ambient light levels at the leaf surface by 0,
25, 50 and 75 percent.
CONCLUSIONS
The model developed in this chapter to calculate con-
tributions of water-column and epiphytic materials to
light attenuation under different water quality condi-
tions works well for sites throughout Chesapeake Bay,
including its tidal tributaries across all salinity regimes.
Values for PLL calculated from water quality data vary
widely among sites throughout the Bay. The model
relies on a combination of empirical relationships
derived from field studies and experimental systems
and numerical computations from a well-calibrated
ecosystem process model. Much of the information on
which the model is based comes from the measure-
ments and analyses done in the mesohaline and poly-
haline regions of Chesapeake Bay; particularly studies
of two SAV species—Potamogeton perfoliatus and Z.
marina (e.g., Staver 1984; Twilley et al. 1985; Golds-
borough and Kemp 1988; Neckles 1990; Moore 1996;
Sturgis and Murray 1997). This is due to limited com-
parable data from lower salinity tidal habitats.
The model is easily used and is amenable to simple
spreadsheet computations on diverse platforms. It has
substantial utility as a screening tool to assess trends in
SAV habitat conditions at individual sites, based on
changes in water quality variables. In ecosystems such
as Chesapeake Bay, where a broad monitoring pro-
gram exists to support efforts to improve water quality
for restoring SAV to degraded habitats, this model
provides an additional important tool to guide man-
agement efforts.
-------
CHAPTER V|
Beyond Light: Physical, Geological
and Chemical Habitat Requirements
Light availability has been identified as the major
factor controlling the distribution and abundance
of SAV in Chesapeake Bay (e.g., Dennison et al. 1993).
Therefore, parameters that can affect the light avail-
ability in an environment (total suspended solids,
chlorophyll a concentration, epiphyte biomass) are
commonly included in predictions of the suitability of
certain areas for SAV growth. Several other parame-
ters that have the potential to override the light
requirements of the plants are not often considered
when determining the suitability of a site for SAV
growth (Livingston et al. 1998). For example, very high
wave energy may prevent SAV from becoming estab-
lished (due to the drag exerted on the plants and/or
the constant sediment motion), even when the light
requirements are met (Clarke 1987).
This chapter discusses physical, geological and chemi-
cal factors that affect the suitability of a site for SAV
growth. These factors differ from those described in
chapters III, IV and V in that the parameters consid-
ered there modify the light requirements of SAV. The
parameters discussed in the present chapter override
the established SAV light requirements. The parame-
ters addressed here (waves, currents, tides, sediment
organic content, grain size and contaminants) can
influence the presence/absence of SAV in a certain
area, independently of light levels. Figure VI-1 shows
how previously established SAV habitat requirements
(light attenuation coefficient, dissolved inorganic
nutrients, chlorophyll a, total suspended solids and
epiphytes) as well as the parameters discussed in this
chapter (waves, currents, tides, sediment characteris-
tics and chemical contaminants) can affect the distri-
bution of SAV.
FEEDBACK BETWEEN SAV AND THE
PHYSICAL, GEOLOGICAL AND CHEMICAL
ENVIRONMENTS
SAV beds can reduce current velocity (Fonseca et al.
1982; Fonseca and Fisher 1986; Gambi et al. 1990;
Koch and Gust 1999; Sand-Jensen and Mebus 1996;
Rybicki et al. 1997), attenuate waves (Fonseca and
Cahalan 1992; Koch 1996), change the sediment char-
acteristics (Scoffin 1970; Wanless 1981; Almasi et al.
1987; Wigand et al 1997) and even change the height
of the water column (Powell and Schaffner 1991;
Rybicki et al. 1997). In turn, these SAV-induced
changes can affect the productivity of the plants.
Therefore, a complex feedback mechanism exists
between SAV and the abiotic conditions of the habitat
they colonize, making it difficult to attribute the distri-
bution of SAV to only one factor, such as light.
By reducing current velocity and attenuating waves,
SAV beds create conditions that lead to the deposition
of small (inorganic) and low-density (organic) parti-
cles within meadows or canopies (Grady 1981; Kemp
et al. 1984; Newell et al. 1986). This in turn can affect
the availability of light (Moore et al 1994), nutrients
(Kenworthy et al. 1982) and compounds that can be
toxic to SAV (phytotoxins), such as sulfide in the sedi-
ments (Carlson et al. 1994; Holmer and Nielsen 1997).
Therefore, all these parameters contribute, to some
degree, to the regulation of SAV growth.
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 71
-------
72 SAV TECHNICAL SYNTHESIS II
Light
Transmission
Waves
Plankton
Chlorophyll a
Total
Suspended
Solids
Tides
Settling
of
Organic
Matter
Biogeo-
chemical
processes
FIGURE VI-1. Interaction between Light-Based, Physical, Geological and Chemical SAV Habitat
Requirements. Interaction between previously established SAV habitat requirements, such
as light attenuation, dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus
(DIP), chlorophyll a, total suspended solids (TSS) and other physical/chemical parameters
discussed in this chapter (waves, currents, tides, sediment organic matter, biogeochemical
processes). P = phosphorus; N = nitrogen; PLW = percent light through water; PLL =
percent light at the leaf.
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 73
When plant density is low, the attenuation of current
velocity and wave energy is also low. This results in lit-
tle accumulation of organic matter and, subsequently,
little change in sediment nutrient and phytotoxin con-
centrations. Light availability may also be low, due to
the resuspension of sediments. Oxygen demand of the
roots (to counteract the detrimental effect of the phy-
totoxins) may also be low, due to reduced photosyn-
thetic biomass.
In SAV beds with high shoot density, water flow is
reduced and more particles are deposited, leading to
higher light, nutrient and phytotoxin availability than
in less dense beds. The SAV density may reach a point
where so much organic matter is trapped that the
resulting high phytotoxin levels are no longer tolerated
by the plants, and they may start dying back (Roblee et
al. 1991; Carlson et al. 1994; Holmer and Nielsen
1997). At that point, the reduction in density may lead
to higher water flow, reduced organic matter ac-
cumulation and reduced phytotoxin levels in the sedi-
ment. This feedback mechanism (hydrodynamics —>
sediment characteristics —* plant biomass —> hydrody-
namics) may assure the health of marine and higher
salinity estuarine SAV populations over time. The
above mechanism may be less applicable for SAV col-
onizing low-salinity estuarine areas, because sulfide
concentrations do not reach levels as toxic as those in
marine environments. However, low-salinity species
can also be susceptible to sulfide, as shown by the
decreased growth rates of Potamogeton pectinatus
when sulfide was added to the sediments (van Wijk
etal. 1992).
SAV AND CURRENT VELOCITY
SAV beds reduce current velocity by extracting
momentum from the moving water (Madsen and
Warnke 1983). The magnitude of this process depends
on the density of the SAV bed (Carter et al. 1991; van
Keulen 1997), the hydrodynamic conditions of the
area (stronger reduction in flow in tide-dominated vs.
wave-dominated areas; Koch and Gust 1999) and the
depth of the water column above the plants (Fonseca
and Fisher 1986). The highest reduction in current
velocity occurs in dense, shallow beds exposed to tide-
dominated conditions (unidirectional flow). Currents
in SAV beds can be 2 to 10 times slower than in adja-
cent unvegetated areas (Ackerman 1983; Madsen and
Warncke 1983; Carter et al. 1988; Gambi et al. 1990;
Rybicki et al. 1997).
Positive Effects of Reduced Current Velocity
The advantages of reduced water flow in SAV beds
include the following:
1) Reduced self-shading due to the more vertical
position of the blades in beds resulting from
reduced drag on SAV leaves (Fonseca et al 1982);
2) Increased settlement of organic and inorganic par-
ticles, increasing the light availability and the sedi-
ment nutrient concentration (Kenworthy et al.
1982; Ward et al. 1984; references in the review by
Fonseca 1996). Notice that this can also lead to a
disadvantage due to increasing sulfide concentra-
tion in marine/higher salinity estuarine habitats
(Koch 1999) (see "Negative Effects of Reduced
Current Velocity").
3) Lower friction velocities at the sediment surface
than in unvegetated areas (Fonseca and Fisher
1986) reducing sediment resuspension and total
suspended solids concentrations and increasing
light availability (references in the review by
Fonseca 1996).
4) High residence time, allowing molecules of dis-
solved nutrients to stay in contact with SAV leaves
and epiphytes for longer periods of time, therefore
increasing the likelihood of being taken up. As a
result, high residence time reduces the nutrient
concentration in the water column (Bulthuis et al.
1984), perhaps limiting epiphytic growth, which
would otherwise lead to further reduction in light
attenuation (see Chapter V). Epiphytes and SAV
will compete for nutrients in the water column,
while only SAV can remove nutrients from the sed-
iments. Therefore, the SAV should not become as
nutrient-limited as epiphytes since they primarily
use nutrients from the sediments.
5) Increased settlement of spores of algae and larvae
of a variety of organisms, resulting in higher
species diversity of invertebrates and algae in SAV
canopies than in adjacent unvegetated areas
(Homziak et al. 1982).
-------
74 SAV TECHNICAL SYNTHESIS II
Negative Effects of Reduced Current Velocity
The points listed above illustrate the potential positive
effects of reduced current velocity in SAV beds. Alter-
nately, reduced water flow can also have detrimental
effects:
1) Concentration of phytotoxins will increase in estu-
arine/marine sediments (Koch 1999). The concen-
tration of phytotoxins in the sediment leads to an
increased oxygen demand by the roots which, if not
met due to poor light availability, has the potential
to kill the plants (Robblee et al. 1991; Carlson et al.
1994; Nepf and Koch 1999).
2) Thicker blade diffusion boundary layers will form
under reduced current velocity in SAV beds (Koch
1994). The diffusion boundary layer is a thin (a few
/am) layer of water on the surface of any submersed
object (including plants) where the transport of
solutes (e.g. carbon needed for photosynthesis or
oxygen produced by photosynthesis) is dominated
by viscous forces (i.e., by diffusion).
Increases in the thickness of this diffusion boundary
layer lead to a longer diffusional path (or thick diffu-
sion boundary layer) for carbon molecules to move
from the water column to the SAV leaf, where they are
used for photosynthesis. As the current velocity
decreases, a critical maximum diffusion boundary
layer thickness, where the flux of carbon to the plant is
slower than the flux needed for the plant to support
maximum photosynthesis, can be reached. The critical
diffusion boundary layer thickness was estimated to be
280 jim for Thalassia testudinum and 98 /urn for
Cymodocea nodosa (Koch 1994).
If a plant is exposed for long periods of time to diffu-
sion boundary layer thicknesses greater than the criti-
cal diffusion boundary layer thickness, growth can
decline, due to carbon limitation independent of the
light levels at the site. The length of time that a plant
can survive under such conditions depends on the
internal carbon reserves in the plant tissue and how
fast these reserves can be accessed (Koch 1993). This
has not yet been determined for most SAV species and
has the potential to be important in areas where mari-
nas and other structures may cause stagnant condi-
tions in SAV habitats.
Some estuarine and freshwater SAV species, such as
Potamogeton pectinatus, are capable of colonizing rela-
tively stagnant waters (like those found in ponds) due
to a physiological adaptation: the release of H+ on one
side of the blade (polar leaves) reduces the pH in the
diffusion boundary layer (Prins et al. 1982). This
decrease in pH shifts the carbon balance toward car-
bon dioxide (CO2), increasing local diffusion boundary
layer CO2 concentration and, therefore, increasing the
flux of CO2 into the plant. Other SAV can also incor-
porate CO2 from sediment porewater, where dissolved
inorganic carbon concentrations are usually much
higher than open-water concentrations (Sondergaard
and Sand-Jensen 1979; Madsen 1987). The CO2 incor-
porated by the roots is then transported to the photo-
synthetic tissue via the lacunae system (Madsen and
Sand-Jensen 1991). For a detailed discussion on mech-
anisms aquatic plants developed to deal with reduced
carbon fluxes due to thick diffusion boundary layers,
see Jumars et al. (accepted).
Epiphytes and Current Velocity
Although epiphytes are usually seen as organisms that
are detrimental to SAV growth, the very early stages of
epiphytic colonization on SAV leaves have the poten-
tial to be beneficial for the plants (Koch 1994). Very
low densities of epiphytes (only visible under a micro-
scope) may disrupt the diffusion boundary layer
enhancing the flux of carbon to the blade (Koch 1994).
As epiphytes compete for light, nutrients and carbon,
later stages of epiphytic colonization (when the epi-
phytic layer is too dense to disrupt the diffusion
boundary layer) become detrimental to SAV growth.
At the community level, epiphytes will contribute to
the reduction in current velocity (due to increased leaf
drag) which leads to the positive aspects listed above
(see "Positive Effects of Reduced Current Velocity").
Therefore, in some aspects, epiphytes can have posi-
tive effects on SAV communities, although the nega-
tive effects of epiphytes on SAV leaves (light
attenuation and increased drag on the leaves, poten-
tially dislodging them at high flows) also need to be
kept in perspective.
Epiphytes on SAV leaves may also respond to water
flow independently of the SAV response to flow. In a
study using acrylic plates, maximum periphyton bio-
mass was observed at intermediate current velocities.
Diatoms were dominant under high current conditions
while a green alga was dominant at lower current
velocities (Horner et al. 1990). In a mesocosm experi-
ment, epiphyte biomass on Vallisneria americana
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 75
increased with current velocity (Merrell 1996). There-
fore, a second order of complexity (water flow) needs
be added to future refinements of the model evaluat-
ing the effect of epiphytes on light available to SAV
leaves (see Chapter V).
Current Velocity SAV Habitat Requirements
From the positive and negative effects of the reduced
current velocities found in SAV beds, it can be con-
cluded that these plants could benefit from intermedi-
ate current velocities (Boeger 1992; Koch and Gust
1999; Merrell 1996; Koch 1999). Extremely low water
flows could increase the blade diffusion boundary
layer thickness as well as the accumulation of organic
matter in the sediment leading to carbon starvation or
death due to high phytotoxin concentrations in the
sediment, respectively. In contrast, extremely high
water flow has the potential to 1) increase drag above
a critical value where erosion of the sediment and
plants occurs, 2) reduce light availability through
resuspension of sediment and self-shading and 3)
decrease the accumulation of organic matter, leading
to reduced nutrient concentration in the sediments.
A literature review revealed that 1) the range of cur-
rent velocities tolerated by marine SAV species lies
between approximately 5 and 100 cm s"1 (physiological
and mechanical limits, respectively); 2) the range of
current velocities tolerated by freshwater SAV species
seems generally to be lower than that for the marine
species; and 3) some freshwater SAV species can tol-
erate extremely low current velocities (Table VI-1).
This may be due to alternative mechanisms of carbon
acquisition present in these freshwater plants but not
in marine plants (see "Negative Effects of Reduced
Current Velocity").
Survival of SAV in high current velocity environments
may be possible if the development of seedlings
occurred under conditions of slow current velocity in
space (e.g., a protected cove) or time (e.g., a low water
discharge period). Once a bed is established under
such conditions, it can expand into adjacent areas with
higher currents due to the reduced currents at the
edge of the bed or persist during times of higher water
flow. Therefore, the stage of the plants (for example,
seeds, seedlings, vegetative shoots, reproductive
shoots) also needs to be taken into account when con-
sidering if current velocity is above or below the estab-
lished requirement for growth and distribution. Based
on the literature review presented here, no data are
available on the current velocity requirements of
plants other than those found in well-established beds.
In summary, intermediate current velocities between
10 and 100 cm s"1 are needed to support the growth
and distribution of healthy marine SAV beds. These
requirements are lower for freshwater/estuarine SAV
species—between 1 and 50 cm s"1—especially for those
with polar leaves. If currents are above or below these
critical levels, the feedback mechanisms in the system
may become imbalanced and possibly lead to the
decline or even complete loss of the vegetation.
Although some of the feedback mechanisms between
SAV beds and current velocity involve light availability
through the effects of resuspension of sediments, self-
shading and epiphytic growth, extreme currents alone
can limit the growth of SAV. Therefore, current
velocity should be considered as a key SAV habitat
requirement.
SAV AND WAVES
As waves propagate over SAV beds, wave energy is lost
(Fonseca and Cahalan 1992; Koch 1996). This is due to
the same mechanism that causes SAV beds to reduce
current velocities-loss of momentum (Kobayashi et al.
1993). The efficiency with which waves are attenuated
by SAV beds depends on the water depth (Ward et al.
1984; Mork 1996), the current velocity (Stewart et al.
1997), leaf length (Fonseca and Cahalan 1992) and the
type of vegetation (canopy or meadow) (Elwany et al.
1995; Mork 1996; Stewart et al 1997).
Wave attenuation is strongest in dense SAV beds due
to increased drag and in meadows (where most of the
biomass is found close to the sediment surface) colo-
nizing shallow waters, where plant biomass takes up a
large portion of the water column. Canopy-forming
species that have long stems and concentrate most of
their biomass, and consequently drag, on the water
surface of a relatively deep water body have the ten-
dency to oscillate with the waves. Acting as though
imbedded in the waves, canopy-forming species
impose little drag on them (Seymour 1996) and, there-
fore, have little effect on wave attenuation.
The effect the constant motion waves imposes on
plants may lead to the breakage of the plants
(Idestam-Almquist and Kautsky 1995; Stewart et al.
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76 SAV TECHNICAL SYNTHESIS II
TABLE VI-1. Minimum and maximum current velocities required for SAV growth and distribution.
Minimum current velocities required to saturate photosynthesis
Current
> 0.04 cm s"1
> 0.08 cm s'1
> 0.5 cm s"1
> 3 cm s"1
> 5 cm s"1
> 5 cm s"1
> 13 cms"1
> 16 cm s"1
Current
< 7cms"1
< 45 cm s"1
< 50 cm s"1
< 50 cm s"1
< 120 cm s"1
< 150 cms"1
< 180 cms'1
Species
Potamogeton pectinatus *
Callitriche stagnalis
Ranunculus pseudofluitans
Zostera marina
Ranunculus pencillatus
Thalassia testudinum
Cymodocea nodosa
Z. marina
Source
Madsen and Sondergaard
1983
Westlake 1967
Westlake 1967
Koehl and Worcester 1991
Werner and Wise 1982
Koch 1994
Koch 1994
Fonseca and Kenworthy 1987
Maximum currents at which the following species occur
Species Source
Vallisneria americana
Ranunculus pencillatus
Zannichellia palustris
Z. marina
Z. marina
Z. marina
Z. marina
Merrell 1996
Werner and Wise 1982
Sculthorpe 1967
Conover 1964
Scoffin 1970
Fonseca etal. 1982
Phillips 1974
* Indicates species for which leaf polarity has been confirmed.
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 77
1997). This effect has been observed to be more severe
for a canopy-forming species (Myriophyllum spp.) than
for a meadow-forming species (Vallisneria spp.; Stew-
art et al. 1997). Breakage of underwater plants exposed
to waves is inversely related to current velocity. As cur-
rent velocity increases, the plants lie closer to the sed-
iment surface and are, therefore, less affected by the
orbital motion of the waves (Stewart et al. 1997).
Table VI-2 summarizes the capacity of marine SAV
species to attenuate waves under a variety of field and
laboratory conditions. The values obtained in the lab
are much higher than those obtained in the field,
because the meadow-forming plants used in the lab
experiments occupied the entire water column,
and wave attenuation is positively correlated with the
percentage of the water column occupied by the
vegetation.
Effects of High Wave Energy
The impact of high wave energy on SAV can be direct
or indirect. The direct impact of waves on SAV can be
seen when waves (in combination with currents) erode
the edges of an SAV bed (Clarke 1987) or when por-
tions of the plants are removed by storm-generated
(Thomas et al. 1961; Eleuterius and Miller 1976;
Rodriguez et al. 1994; Dan et al. 1998) or boat-
generated waves (Stewart et al. 1997). Indirect conse-
quences of wave energy in SAV beds include sediment
resuspension, changes in sediment grain size, mixing
of the water column and epiphytic growth. If the
capacity of an SAV bed to attenuate waves is reduced,
for example, due to a reduction in shoot density
because of clam dredging or eutrophication, the
underlying sediment will become more vulnerable to
erosion, and higher concentrations of suspended
TABLE VI-2. Attenuation of wave energy in meadow-forming marine SAV beds.
Attenuation of Wave
Wave Energy Period
(seconds)
Site Species
Comments Source
1.6%
7.7 %
43%
43%
44%
44%
0.35
0.35
0.4 and 0.7
0.4 and 0.7
0.4 and 0.7
0.4 and 0.7
Field
Field
Flume*
Flume*
Flume*
Flume*
Thalassia
Thalassia
Zostera
Syringodium
Halodule
Thalassia
Within
SAV bed
Edge of
SAV bed
1 minto
SAV bed
1 minto
SAV bed
1 minto
SAV bed
1 minto
Koch 1996
Koch 1996
Fonseca and Cahalan
1992
Fonseca and Cahalan
1992
Fonseca and Cahalan
1992
Fonseca and Cahalan
SAV bed 1992
Plants (meadow-formers) occupied the entire water column.
-------
78 SAV TECHNICAL SYNTHESIS II
sediment particles can be expected in the water. This
is especially true for SAV beds in which fine particles
have accumulated over time. These sediments may be
resuspended at lower wave energy than the coarser
sediments outside the SAV bed (Posey et al. 1993).
Wave attenuation and sediment resuspension in vege-
tated areas depend on the water levels above the
plants. At low tide, wave energy is reduced to a greater
extent than during high tide (Ward et al. 1984). Resus-
pension of fine particles will alter the grain size distri-
bution of the sediment. In areas of high wave
exposure, sediments are coarser, which leads to lower
nutrient concentration in the sediment and, conse-
quently, lower root biomass (Idestam-Almquist and
Kautsky 1995). By contrast, the above-ground biomass
of Potamogeton pectinatus depends directly on wave
exposure; shoots are shorter in areas with high wave
exposure than in areas with low wave exposure
(Idestam-Almquist and Kautsky 1995).
In Chesapeake Bay, shore erosion (caused by wave
action) contributes 13 percent of the total suspended
matter in the upper Bay and 52 percent in the middle
Bay (Biggs 1970). Perhaps, before the decline of SAV
in this area, SAV protected the coastlines from the
direct impact of waves. Ward et al. (1984) observed
that in shallow (< 2 meters) unvegetated areas in the
Choptank River, total suspended solids concentrations
increased tenfold when the wind came from the direc-
tion of highest fetch and was >25 km h"1, but the
increased total suspended solids concentrations dissi-
pated within 24 hours after the storm. As the wind
intensifies, wave period and wave length increase lead-
ing to deeper wave mixing depths (Chambers 1987).
The small grain size sediments are the first to become
resuspended. Therefore, if wave energy increases in an
SAV bed, a shift toward coarser sediments will occur.
The consequences of this shift are addressed below, in
"SAV and the Sediments It Colonizes."
SAV growth and distribution seems to be limited by
high, but not low wave energy (Dan et al. 1998; Table
VI-3). However, high wave exposure can also benefit
the plants by reducing the epiphytic biomass (Strand
and Weisner 1996; Kendrick and Burt 1997; Weisner et
al. 1997). In high wave exposure areas, where sedi-
ments are constantly being shifted and grain size may
be skewed toward coarser particles, SAV may not be
able to become established due to the balance between
the anchoring capacity of the roots and the drag
exerted on the leaves. High wave exposure also leads
TABLE VI-3. Quantitative and qualitative descriptions of wave tolerance for Chesapeake Bay species.
Species
Canopy formers
Myriophyllum spicatwn
Wave Tolerance
Source
Wave limited
Rawls (1975), Stewart et al 1997
Zannichellia palustris
Wave limited
Flowering structures ofRuppia Wave sensitive
maritima
Meadow formers
Zoster a marina
Potamogeton pectinatus
Vallisneria americana
2 m waves
Wave tolerant
Stevenson and Confer 1978
Joanen and Glasgow 1965
Dan et al. 1998
Hannan 1967
More wave tolerant Stewart et al. 1997
than Myriophyllum
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 79
to reduced light availability due to increased sediment
resuspension, but this may be compensated by the
lower epiphytic biomass on the leaves of wave-exposed
plants. The mechanism that allows for reduced epi-
phytic biomass on plants exposed to high wave energy
is not well understood. It could be due to the rubbing
of the blades against each other.
Wave Mixing Depth Effects on
SAV Minimum Depth Distributions
The minimum depth of distribution of aquatic plants
in lakes with good water quality has been correlated to
the resuspension of sediments by waves resulting in
the scouring of sediments, uprooting of plants and
increased turbidity (Figure VI-2). Consequently,
Chambers (1987) suggested that the minimum depth
distribution (Zmin) of aquatic plants can be determined
by the wave mixing depth (Zwave), which extends to a
depth equal to half the wavelength (L),
Z =Z =-
mm wave o
(vi-iy
L can be calculated from the wave period (T) using the
following equation:
L = 4^ (VI-2)
where g is acceleration of gravity (9.805 m s"2). Equa-
tion VI-2 is a standard equation for waves propagating
over depths larger than half the wavelength (i.e.,
before they "feel the bottom" and define Zmin; see
Equation VI-1). The wave period (T) for these waves
can be predicted according to the following equation:
T =
.0.46W.
g
1.28
(VI-3)
where W is the wind velocity (m s"1) and F is the effec-
tive fetch (m). These equations allow for the predic-
tion of the wave mixing depth (Zwave) in shallow
SAV habitats.
In relatively exposed areas at the mouth of Chesa-
peake Bay (Timble Shoal Entrance and Timble Shoal
Light), wave periods (T) range between 4 and 13 sec-
onds (Boon et al. 1996). These values are typical for
wind- generated waves. Marine SAV can colonize such
areas, as seen in the Thalassia testudinum beds off-
shore of the Florida Keys (Koch 1996), the Cymodocea
nodosa beds in the Mediterranean (Koch 1994) and
the Posidonia oceanica beds in many areas in Aus-
tralia. In the shallower, more protected SAV habitats
in Chesapeake Bay, waves with smaller periods
(ripples) can be expected. Figure VI-3a shows the
wave mixing depth for ripples and wind-generated
waves while Figure VI-3b shows more details for rip-
ples, typical for shallow, semi-enclosed SAV habitats.
Low wave energy
High wave energy
Wave length
Wave mixing dent
*««*••* W**»»»«n*>«
SAV
FIGURE VI-2. Wave Energy Effects on SAV Vertical Depth Distributions. The vertical distribution of SAV beds
can be shifted into deeper waters due to wave energy. Waves can constantly shift sediments preventing the
colonization of the area or resuspend sediments, contributing to increased total suspended solid (TSS)
concentrations, which leads to reduced light levels. The zone where waves do not allow SAV to colonize is
defined as the depth equivalent to half the wavelength.
-------
80 SAV TECHNICAL SYNTHESIS II
4 6
Wave period (s)
10
B
0.2
0.4 0.6
Wave period (s)
FIGURE VI-3. Wave Period/Depth of Wave Mixing
Relationship. Depth of wave mixing (or minimum
depth of distribution of SAV) under a variety of wave
conditions. Wind-generated waves are typical for
relatively exposed areas (A) while ripples are typical for
shallow coastal areas. SAV occur in habitats character-
ized by both wave types. Depth of wave mixing for
waves periods more characteristic of SAV habitats in
Chesapeake Bay are illustrated in B.
Wave Exposure Habitat Requirements
Wave mixing depth and tides have a potentially con-
founding effect on the minimum depth of SAV distri-
bution. As will be discussed below (see "SAV and
Tides"), due to the lack of resistance of most SAV
species to dessication, tides tend to force SAV to colo-
nize deeper waters, where exposure to the air is less
likely. If waves also force the SAV to inhabit deeper
waters due to sediment resuspension in areas
shallower than the wave mixing depth (Figure VI-2),
then the minimum depth of distribution of SAV beds
should be determined by the mean low water (tide)
plus the wave mixing depth (see "SAV and Tides").
This can be visualized by imagining the water level in
Figure VI-2 fluctuating vertically with the tides as the
waves continue to propagate onshore. No data are
available to verify this hypothesis.
Although waves have the potential to force SAV to
colonize deeper areas (Figure VI-2) and tides may
enhance this minimum limit of colonization, no 'wave
exposure habitat requirement' can yet be established
for SAV in Chesapeake Bay. Wave exposure indexes
(based on fetch and wind intensity) have been sug-
gested for lake environments (Keddy 1982) and
recently also for an estuarine area (Murphey and Fon-
seca 1995; Fonseca and Bell 1998), but further
research is needed to understand the effect of waves
on the ecology and distribution of SAV beds. As high
wave exposure has the potential to override other light
requirements (Kd and epiphyte biomass) or to com-
pletely eliminate SAV from high wave exposure areas,
it should be addressed as a SAV habitat requirement in
the near future. Fonseca and Bell (1998) were able to
determine the maximum wave exposure tolerated by
SAV in Pamlico Sound, North Carolina, but when the
same methodology was applied to Chesapeake Bay, no
conclusive results could be obtained (Chiscano, in
preparation). This difference could be due to the
higher fetches in Chesapeake Bay than in Pamlico
Sound, the presence of sand bars offshore from SAV
beds (the model used does not take into account
bottom bathymetry) or the erosion of the marshes
which changes the sediment characteristics in SAV
habitats, potentially limiting the distribution of SAV
(see "SAV and the Sediments It Colonizes").
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Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 81
SAV AND TURBULENCE
Turbulence consists of temporally and spatially irregu-
lar water motion superimposed on the larger flow pat-
tern. It forms at such boundaries as the sediment
surface or the surface of SAV leaves. It is then trans-
ferred from larger to smaller scales (eddy sizes). In
SAV beds, the distance between SAV shoots deter-
mines the size of the turbulence scale/eddies (Ander-
son and Charters 1982; Nowel and Jumars 1984;
Ackerman and Okubo 1993). Turbulence in these
plant communities can be generated and rescaled, i.e.,
shifting the scale of the eddies formed in a SAV bed
(Anderson and Charters 1982; Gambi et al 1990; Ack-
erman and Okubo 1993; Koch 1996). Since mass trans-
fer of nutrients and carbon in the water takes place by
eddy diffusion in turbulent flows (Sanford 1997), tur-
bulence has the potential to be of extreme ecological
importance in SAV beds. Turbulence also may affect
the dispersion of particles such as pollen, larvae, seeds
and spores in the beds. Unfortunately, the effect of
turbulence on these plants is poorly understood.
The observations of turbulence in SAV beds may seem
contradictory. A region of high turbulence levels can
be observed at the canopy-water interface (Gambi
etal. 1990). Increased turbulence within the vegetation
has also been observed during a monami, high-
amplitude blade waving (Grizzle et al. 1996). By con-
trast, reduced turbulent mixing also during a monami
in a Zostera marina bed has been reported by Acker-
man and Okubo (1993). Since turbulence depends on
the current velocity and the structure of the SAV bed
(Koch and Gust 1999), at low current velocities the
turbulence levels are expected to be low. As the cur-
rent velocity increases, turbulence levels also increase.
At the point where the vegetation begins to bend over
due to the current velocity, the water flow is redirected
over the vegetation and turbulence levels among the
plants may decrease again (Nepf et al. 1997).
Since mass transport of nutrients and carbon in SAV
beds depends on turbulence levels, it can be predicted
that SAV can benefit from turbulence in the water.
The optimal turbulence levels for SAV is yet unknown.
What is known is that SAV beds rescale turbulent
energy from larger to smaller eddies. This process
depends on the architecture of the SAV bed (Koch
1996; Koch and Gust 1999). Epiphytes colonizing SAV
blades decrease the distance between "obstructions to
the flow" (like blades and shoots) and rescale turbu-
lence to even smaller eddies than those found among
blades without epiphytic growth (Koch 1994,1996).
Rescaling of turbulence occurs at the individual plant
level (Anderson and Charters 1982) and at the canopy
level (Gambi et al. 1990; Koch 1996) and may be a
mechanism for creating mixing lengths of biological
importance (i.e., mixing of the water that results in
increased productivity). Until turbulence in SAV beds
is better understood, few predictions regarding the
importance of turbulence for the health and distribu-
tion of SAV can be made.
SAV AND TIDES
Most SAV species are not tolerant of dessication
because they lack the waxy cuticle found in terrestrial
plants and, thus cannot grow in the intertidal zone.
Small SAV species that are found in intertidal pools
(like plants from the genus Halophila) and SAV beds
that retain water between their leaves at low tide, can
colonize the intertidal area. These are the exceptions.
Additionally, plants that colonize the intertidal area in
temperate zones often are removed by shifting ice dur-
ing the winter. Consequently, the minimum depth of
distribution of most SAV species is limited to the mean
low water level while the maximum depth of distribu-
tion is limited by the light availability (Figure VI-4).
As mentioned above, waves may also limit the mini-
mum depth of SAV distribution (see Figure VI-2),
therefore, tides and waves need to be considered as
confounding factors when analyzing the vertical distri-
bution of SAV. As waves and tides co-occur in many
SAV habitats, tides will constantly change the wave
mixing depth (see above). Therefore, theoretically, the
minimum depth of distribution should be at a depth
below the mean low water (MLW) line-the MLW level
plus the wave mixing depth (Figure VI-5).
Minimum Depth of Distribution
The minimum depth of distribution based on tides
alone can be defined as half the tidal amplitude (A in
m) below mean tide level (MTL) (see Figure VI-4). In
areas with diurnal tidal cycles, this will be {MHW-
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82 SAV TECHNICAL SYNTHESIS II
A. Small tidal range
B. Large tidal range
MHW
MTL
LW
FIGURE VI-4. Tidal Range Influence on Vertical SAV Depth Distribution. The vertical range of distribution of SAV
beds can be reduced with increased tidal range. The minimum depth of SAV distribution (Zmin) is limited by the low
tide (T), while the maximum depth of SAV distribution (Zmax) is limited by light (L). The SAV fringe (arrow) decreases
as tidal range increases. A small tidal range results in a large SAV depth distribution (A), whereas a large tidal range
results in a small SAV depth distribution (B). Mean high water (MHW), mean tide level (MTL) and mean low water
(MLW) are all illustrated.
S°
S2
faj
ea
X 3
£
w ,
TIDAL RANGE (m)
123456
increased light attenuation
MAXIMUM DEPTH OF SAV DISTRIBUTION (m)
FIGURE VI-5. Minimum and Maximum Depth of SAV
Distributions as a Function of Tidal Range. The
minimum depth of SAV distribution defined as the mean
low water (MLW) line decreases with wave exposure
(see Figure VI-2), while the maximum depth of SAV
distribution decreases with increasing light attenuation
coefficients. MTL is the mean tide level.
MLW}/2, while in areas with semi-diurnal tides it will
be {MHHW-MLLW}/2, where MHW is mean high
water, MHHW is mean higher high water and MLLW
is mean lower low water. A method to calculate the
minimum depth of distribution (Zmin) including the
wave mixing depth has been suggested by Chambers
(1987) and is described above. It should theoretically
be defined as:
rr
Z. =: •
mm ,,
(VI-4)
2 2
where the first term of the equation refers to the tidal
amplitude and the second term refers to the wave mix-
ing depth (see Equation VI-2). Equation VI-4 suggests
that in areas of high tidal amplitude and high wave
exposure, SAV will be forced to colonize relatively
deep waters. Its success in colonizing such areas will
depend on their maximum depth of distribution.
Maximum and Vertical Distributions
The maximum depth of distribution of SAV depends
on the light attenuation in the water column (Kd) as
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 83
well as on the water depth (which is a function of
tides). Therefore, tides and the maximum depth of dis-
tribution of SAV are confounding factors (Carter and
Rybicki 1990; Koch and Beer 1996). In areas with high
tidal amplitude: 1) SAV is forced into deeper areas
due to dessication and freezing (Figure VI-4); and 2)
the water column is deeper during high tide than in an
area with a smaller tidal amplitude (i.e., there is more
water to attenuate light). This will reduce the light
available to the plants as well as the number of hours
of saturating light levels (Koch and Beer 1996). There-
fore, the SAV bed is limited by the upper (determined
by tides and waves) and lower (determined by light
penetration) depths of distribution (Figure VI-5).
The maximum depth of distribution (Zmax) can be cal-
culated based on the Lambert-Beer equation:
Z=.
max
IP
(VI-5)
Kd
where Iz /I0 is the percent light required by the species
under consideration or the percent light at the maxi-
mum depth of distribution of the plants. From Equa-
tion VI-5, it is evident that, as Kd increases, the
maximum depth of distribution becomes shallower,
which further restricts the vertical distribution of the
plants (Figure VI-6).
No SAV species can survive if
(VI-6).
This shifts the focus from considering how deep SAV
can grow to how narrow their depth distribution can
be in order to sustain healthy beds. For Z. marina to
successfully colonize an area in Long Island Sound,
Koch and Beer (1996) found that
(VI-7)
was a necessary condition for the existence of this SAV
species. This 1-meter potential vertical depth distribu-
tion below Zmin is necessary as a buffer when, during
storm events, the shallower portion of the SAV bed is
exposed to air, rain or ice. The deeper portions of this
fringe can provide the necessary energy to allow the
shallower portion to recover from the stress of expo-
sure (Koch and Beer 1996).
TIDAL RANGE (m)
23456
W
09
a 3
£
MAXIMUM DEPTH OF SAV DISTRIBUTION (m)
FIGURE VI-6. Effect of Increased Light Attenuation
on Maximum Depth of SAV Distribution. The magni-
tude of the effect of increased light attenuation (Kd) on
the maximum depth of SAV distribution as determined
based on the equations presented in the text, assuming
a SAV minimum light requirement of 13 percent. MIL is
the mean tide level.
For the mixture of estuarine species in Chesapeake
Bay, the vertical depth of distribution seems to be
smaller than that found for Long Island Sound, but
this value still needs to be defined.
The management implication of not only considering
the maximum depth of distribution for SAV but the
vertical depth range that they can colonize is that, in
areas with high tidal ranges, testing attainment of min-
imum light requirements needs to be adjusted to
account for tidal ranges. The reason for this is that if
the tidal range is large (i.e., Zmin is relatively deep) and
the light availability is low (i.e., Zmax is relatively shal-
low), SAV may be restricted to such a narrow vertical
depth that its long-term survival is not viable (Koch
and Beer 1996).
Figure VI-7 indicates how Kd in combination with tidal
range and depth can be used to predict the vertical dis-
tribution of SAV in an area. In Figure VI-7, a tidal
range of 0.8 meters (see x-axis), a minimum light
requirement of 14 percent (Iflj and a Kd=1.5 m'1
(see horizontal dashed lines) are assumed. A line is
drawn vertically from the 0.8-meter tidal range. The
depth at which it intersects the diagonal line deter-
mines Zmin while the depth at which it intersects the
horizontal dashed line for the selected K, value
-------
84 SAV TECHNICAL SYNTHESIS II
TIDAL RANGE (m)
0.0 0.2 0.4 0.6 0.8 1J) 1.2 1.4 1.6 1.8 2.0
MAXIMUM DEPTH OF DISTRIBUTION (m)
FIGURE VI-7. Area-Specific Prediction of Vertical
SAV Depth Distribution. An example of how this type
of graph can be used to predict the vertical distribution
of SAV in a certain area. A tidal range of 0.8 m (see
x-axis), a minimum light requirement of 13 percent
(part of the equation to determine Zmax) and a Kd =
1.5 m~1 (see horizontal dashed lines) are assumed.
A line is drawn vertically from the 0.8 m tidal range.
The depth at which it intersects the diagonal line
determines Zmin while the depth at which it intersects
the horizontal clashed line for the selected Kd value
determines Zmax. In this case, SAV has the potential
to grow in a fringe between 0.4 and 1.3 m deep.
MTL is the mean tide level.
0.0-
0.4-
Q.
«
O
1.3-
Tide
SAV
MTL
linimum
depth of SAV
distribution
based a tidal
range of 0.8 m
Maximum depth of SAV distribution based on
a KJ = 1.5 m-1 and a minimum light requirement
of 13 percent of the light Incident on the surface
FIGURE VI-8. Illustration of Tidal Range Influences
on Vertical SAV Depth Distribution. Illustration of the
SAV vertical depth distribution fringe determined in
Figure VI-7. The fringe will occur in a 0.9 m depth
interval (1.3-0.4 m) due to tidal limitations at the top
and light limitations at the bottom.
determines Zmax. In this case, SAV has the potential to
grow in a fringe between 0.4 and 1.3 m depth (vertical
bar in Figure VI-7 and SAV arrow in Figure VI-8) . In
this case, the SAV fringe will be 0.9 meters deep
(1.3 meters-0.4 meters).
Tidal amplitudes in Chesapeake Bay (MHHW-
MLLW) range from nontidal in some dammed tribu-
taries to 50 cm in the upper Potomac River,
Washington, DC, 60 cm in the Patuxent River, Mary-
land and 64 cm in the Nansemond River, Virginia.
SAV light requirements of Kd 1.5 and 2 m'1 were estab-
lished in 1992 for Chesapeake Bay, based on a 1-meter
restoration depth (Batiuk et al 1992). With this rela-
tively high light attenuation, SAV would probably not
exist if tidal ranges were higher than 1 meter (this
allows SAV to colonize a fringe between 75 and 40 cm
in depth, respectively). From Figure VI-9a, it can be
seen that, when the low tide at the Nansemond River
occurs at noon, the maximum light intensity resembles
that of full sunlight but, the hours of saturating light
(Hsat) decrease with increasing Kd. Figure VI-9b shows
the dramatic effect that a high tide at noon has on the
light availability to SAV in the Nansemond River. The
higher the Kd, the less light is available to the SAV
when the high tide is at noon. Figures VI-9a and VI-9b
are based on a clear sunny day. If the day is cloudy, the
effect of a high tide at noon could lead the SAV to be
exposed to light levels below saturation while a low
tide at noon could reduce the number of hours of sat-
urating light that the plants receives even on a sunny
day by reducing the light available to the plants in the
early morning and late afternoon (high tides).
Tides have a significant effect on the light available to
SAV in Chesapeake Bay, where tidal amplitudes are
relatively small but Kd values are relatively high. In
areas with lower Kd values in the past, an increase in
Kd combined with high tidal amplitudes, can jointly
contribute to the decline of SAV distribution (Koch
and Beer 1996).
The SAV light requirements presented in the first SAV
technical synthesis (Batiuk et al. 1992) were estab-
lished based on a restoration depth of 1 meter MLW
(mean low water) without considering tidal range lev-
els. This overestimated Zmeiv and underestimated the
lUdA
water quality necessary to allow SAV to recolonize
areas down to a depth of 1 meter (Zmax = 1 meter).
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 85
A (Low tide at noon)
B (High tide at noon)
t/5
C
0}
~O
E
13
I
CD
2000
1800
1600
1400
1200
1000
800
600
400
200
0
V)
ex/
03
o
E
X
CD
2000
1800
1600
1400
1200
1000
800
600
400
200
0
0 2 4 6 4 10 12 14 16 1» 20 22 24
light at surface
kd=-2.Dm-1
kd=-1.Sm-1 *
kd=-1.Dm-1
6 8 10- 12 1* 16 14 20 22 24
TIME
TIME
FIGURE VI-9. Simulated Diurnal Light Curves for Different Light Attenuation Coefficients. Simulated diurnal
light curves for different light attenuation coefficients (Kd) in the Nansemond River, Virginia, with a 64 cm tidal range
during a clear, cloudless day, when the low tide occurs at noon (A) and when the high tide occurs at noon (B). The
horizontal line indicates the light level that saturates photosynthesis. The number of hours of saturating light (Hsat)
decrease with increasing Kd values when low tide occurs at noon (A) Although the number of hours of saturating
light are not affected when high tide occurs at noon (B), the higher the Kd, the lower the light levels available to the
SAV (B).
Table VI-4 summarizes the differences between the
1992 SAV light requirements based on MLW and the
revised light requirements taking the tidal amplitudes
into account. These values were obtained by changing
Z in Lambert-Beer's equation to Z 4- A/2, where A is
the tidal amplitude:
species. From Figure VI-7, it can be concluded that
SAV will not occur in areas where Kd=2 m"1 if the tidal
range is equal or higher than 1.8 meters. Under these
conditions Zmax=Zmin. The true tidal requirement can
only be determined once the minimum fringe of distri-
bution for the SAV species in question is determined.
Kd = -
IP
(VI-8).
In summary, tidal amplitude and Kd have a strong con-
founding effect on the distribution of SAV. This has
been conclusively demonstrated in the literature, and
simple equations exist to predict the SAV distribution
based on the interaction between tides and Kd (Koch
and Beer 1996). In order to incorporate tidal ampli-
tude as an SAV habitat requirement in Chesapeake
Bay, it is necessary to determine the minimum vertical
distribution for marine, estuarine and freshwater SAV
SAV AND THE SEDIMENTS IT COLONIZES
Sediments are important in determining the growth,
morphology and distribution of SAV (Short 1987; Liv-
ingston et al. 1998) due to the availability of nutrients
and phytotoxins as well as erosional/deposition
processes (Marba et al. 1994; Dan et al. 1998; Koch
1999). Extreme events causing massive erosion or dep-
osition of sediments can cause the death of entire SAV
populations. The sediment underlying an SAV bed in
Florida was completely eroded away and redeposited
elsewhere (Hine et al. 1987). The massive destabiliza-
tion of this population may have been caused by heavy
grazing of the plants.
-------
86 SAV TECHNICAL SYNTHESIS II
TABLE VI-4. Light attenuation requirements neces-
sary for the recovery of SAV down to the 1 meter
depth contour, taking tides into account. l^ (PLW)
is assumed to be 13 percent for tidal fresh and
oligohaline plants and 22 percent for mesohaline
and polyhaline plants.
Salinity
Regime
Tidal fresh
and
oligohaline
Mesohaline
and
polyhaline
Mean
Tidal
Range
0.0m
O.lm
0.2m
0.3m
0.4m
0.5m
0.6m
0.0m
O.lm
0.2m
0.3m
0.4m
0.5m
0.6m
SAV K,, (m1) habitat
requirement
for 1 m restoration target1
based on MLW2 + tidal range
1.97
1.87
1.79
1.71
1.64
1.57
1.51
1.51
1.44
1.37
1.32
1.26
1.21
1.16
SAV habitat requirements from Batiuk et al. 1992.
Mean low water.
On the other extreme, high sedimentation rates can
also be responsible for the decline of SAV populations.
Moderate depositional rates can stimulate the growth
of Thalassia testudinum (Gallegos et al. 1993) and
Cymodocea nodosa (Marba and Duarte 1994), but
high depositional rates can lead to the disappearance
of these plants.
Seedlings are more susceptible to high burial rates
than established SAV beds (Marba and Duarte 1994).
Therefore, the season of depositional events is impor-
tant in determining the chances of survival of SAV
beds. The deposition of more than 10 cm of sediment
on top of V americana tubers reduced their chances of
becoming mature plants and establishing a meadow
(Rybicki and Carter 1986). Such high depositional
rates can occur during severe storms. In contrast, Z.
marina seeds need to be buried at least 0.5 cm, where
conditions are anoxic, to promote germination (Moore
etal 1993).
During less extreme conditions, SAV can modify the
characteristics of the sediment it colonizes by reducing
current velocity and attenuating waves within its beds
(see the review by Fonseca, 1996). This leads to the
deposition of small inorganic and light organic parti-
cles (Kenworthy et al. 1982). The suitability of fine sed-
iments and sediments with high organic content for
SAV growth are addressed below.
Grain Size Distribution
Sediments within SAV beds are finer than those in
adjacent unvegetated areas (Scoffin 1970; Wanless
1981; Almasi et al. 1987). As SAV density increases,
the ability to accumulate fine particles is also
enhanced (due to the reduction in current velocity and
wave energy). As grain size distribution becomes
skewed toward silt and clay, the porewater exchange
with the overlying water column decreases. This may
result in increased nutrient concentrations (Ken-
worthy et al. 1982) and phytotoxins such as sulfide in
marine sediments (Holmer and Nielsen 1997). At the
other extreme, if SAV colonizes coarse sand, the
exchange of porewater with the overlying water col-
umn will be enhanced and nutrient availability in the
sediment may be lower than that of finer sediments.
In an experiment using different grain sizes of ground
glass (to avoid adsorbed nutrients), Ruppia maritima
was found capable of colonizing sediments from a
silt/clay mixture to coarse sand. Maximum growth was
observed in fine and medium sand particles (Seeliger
and Koch, unpublished data).
Table VI-5 lists quantitative and qualitative data on
the silt and clay amount found in healthy SAV beds.
The values range from 0.4 to 90.1 percent. The highest
values seem to be associated with beds in lower salin-
ity environments with the exception of a Zostera muel-
leri bed. Perhaps, in such environments, the plants are
able to colonize sediment with reduced porewater
exchange with the water column because sulfide does
not occur at the same levels as in marine/higher salin-
ity estuarine systems. In higher salinity environments,
it appears that plants need sediments that are more
oxygenated and in which sulfide levels can be reduced
via higher porewater advection rates. Therefore,
SAV growth may be limited by the physical and
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 87
TABLE VI-5. Percent fine sediment (< 63 ^urn) or sediment type found in healthy SAV beds1
Percent fines/sediment type Species
Source
1.9
4.8
0.4 - 9.0
1.8-9.2
6-10
14
14.6
0.8 - 14.7
15
8.1-28.8
2.8 - 30.9
1-34
2 - 39 % clay
40
4.3 - 47
58
4 - 62 % silt
48 % sUt and 14% clay
0.5 - 72
4.7-90.1
"Sat loving"
Syringodium filiforme
Thalassia testudinum
Posidonia oceanica
T. testudinum, S. filiforme and
Halodule wrightii
Chesapeake Bay SAV
Zostera marina
T. testudinum
Thalassia and Halodule
Z. marina
Halodule and Zostera
Heterozostera tasmanica
T. testudinum
Tidal Potomac River SAV
Hydrilla and Vallisneria
No SAV (Thalassia,
Syringodium and Halodule)
Hydrilla verticillata
Tidal Potomac SAV
Vallisneria americana
Zostera muetteri
Mixture of Vallisneria,
Potamogeton perfoliatus,
P. pectinatus, Ruppia maritima
P. pectinatus
Woodetal. 1969
Vfoodetal. 1969
Edgar and Shaw 1995
Livingston et al. 1998
Batiuke/a/. 1992
Marshall and Lucas 1970
Scoffin 1970
Grady 1981
Orth 1977
Murphey and Fonseca 1995
Edgar and Shaw 1995
Burrell and Schubel 1977
Carter etal 1985
Poseyetal. 1993
Livingston et al 1998
Poseyetal. 1993
Carter etal. 1985
Hutchinson 1975
Edgar and Shaw 1995
Pascals al. 1982
Sculthrope 1967;
Hasiam 1978
Saty substrate P. perfoliatus Hasiam 1978
1 Arranged in ascending order of maximum percentage of fine sediments.
continued
-------
88 SAV TECHNICAL SYNTHESIS II
TABLE VI-5. Percent fine sediment (< 63 (Jim) or sediment type found in healthy SAV beds1 (continued)
Percent fines/sediment type Species
Source
Silty substrate
Mud
Organic ooze
Soft or sandy mud
Muds or sand
Clay and sand
Medium-grained substrate
Medium-grained substrate
Sandy
Sagittaria sagittifolia
Ceratophyllum demersus
Myriophyllum spicatum
Myriophyllum spicatum
Ruppia maritima
Zannichellia palustris
Myriophyllum spicatum
Ranunculus
Najas guadalupenses
Haslam 1978
Hutchinson 1975
Patten 1956
Springer 1959
Anderson 1972
Stevenson and Confer 1978
Haslam 1978
Haslam 1978
Martin and Uhler 1939
1 Arranged in ascending order of maximum percentage of fine sediments.
geochemical processes associated with a certain sedi-
ment type and not by the grain size per se (Barko and
Smart 1986). Data in Table VI-5 are not sufficient to
establish the 'best' sediment types for SAV growth at
this time.
Sediment Organic Content
SAV tends to accumulate organic particles due to a
reduction in current velocity and wave energy within
the meadows and canopies. Organic matter can also be
accumulated in SAV colonized sediments through the
burial of rhizomes and roots produced over time. The
age of the organic deposits beneath a Posidonia ocean-
ica bed were found to be up to 3,370 years old (Mateo
et al. 1997). High burial rates and/or low decomposi-
tion rates may account for the accumulation of organic
matter over such long periods.
The organic carbon content of sediments from the
mainstem Chesapeake Bay has increased two to three
times over the last 80-100 years (Cornwell et al. 1996).
This was attributed to changes in inorganic matter
deposition due to increased phytoplankton biomass
(Harding and Perry 1997) as well as time-dependent
changes in organic matter decomposition. If the
organic content of the sediments in shallow areas in
Chesapeake Bay SAV habitats are also increasing, they
could limit the distribution of SAV. Barko and Smart
(1983) and Koch (unpublished data) concluded that
the growth of SAV is limited to sediments containing
less than 5 percent (dry weight) organic matter.
This is also supported by other data summarized in
Table VI-6.
The mechanism behind this limitation of high sedi-
ment organic content on SAV growth is not well
understood. It may be due to nutrient limitation in
very fine sediments associated with high organic
deposits (Barko and Smart 1986) or due to high sulfide
concentrations in marine sediments, known to be toxic
to high salinity SAV (Carlson et al. 1994).
The data in Table VI-6 lists organic contents of less
than 12 percent for SAV colonized sediments. The
higher values (6.5 to 12 percent) are mostly associated
with SAV species that have large leaves. Perhaps these
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 89
TABLE VI-6. Sediment organic matter as percent of dry weight in healthy SAV beds.1
Percent Organics
Species
Source
1.25
1.25
0.41 -1.38
<2
2.5
3.25
0.77 - 3.62
3.5-4.9
<5
<5
1-5.3
2.6 - 5.3
<6.5
0.8 - 7.3
8
1.6-12
<26mgCg'1
Zoster a marina
Z. marina
Z. marina
Ruppia maritima
Syringodiumfiliforme
R. maritima
Halodule and Zostera
Thalassia testudinum
Z. marina
Hydrilla and Potamogeton nodosus
Chesapeake Bay SAV
Heterozostera tasmanica
Vallisneria americana
Zostera muelleri
Z. marina
Posidonia spp.
Potamogeton pectinatus
Marshall and Lukas 1970
Orth 1977
Dan etal. 1998
Ward etal 1984
Wood et al 1969
Kemp etal. 1984
Murphey and Fonseca 1995
Wood era/. 1969
Koch 1999
Barko and Smart 1983
Batiuketal. 1992
Edgar and Shaw 1995
Hutchinson 1975
Edgar and Shaw 1995
Short (personal communication)
Edgar and Shaw 1995
vanWijkefa/. 1992
1 Arranged in ascending order of maximum percentage of organic matter.
plants can colonize sediments with higher organic con-
tent due to a large oxygen production in the leaves
and, consequently, also a higher transport of oxygen to
the roots. If the rhizosphere is well-oxygenated, the
detrimental effects associated with high organic con-
tent in the sediments may be neutralized. The distri-
bution of Potamogeton spp. in English Lakes (Pearsall
1920; summarized in Hutchinson 1975) was directly
correlated with sediment organic content (X) and min-
imum light requirement (Y) where Y=0.70 + 0.65 X
(r2=0.90). Therefore, plants growing in more organic
sediments with higher concentrations of phytotoxic
metabolites require more light to support greater
release of oxygen from their roots to the rhizosphere.
This mechanism has been used to explain the decline
in abundance of SAV populations in eutrophic regions
that have experienced an increase in sediment organic
content (Nienhuis, 1983).
Due to the large numbers of studies that observed
the percent organic matter in SAV beds to be below
-------
90 SAV TECHNICAL SYNTHESIS I
5 percent, it is recommended that caution should be
taken when transplanting SAV into areas where the
sediment organic content is higher than that value.
Additional studies are needed to define the SAV habi-
tat requirement for organic matter for different SAV
species in Chesapeake Bay.
SAV AND SEDIMENT GEOCHEMISTRY
Nutrients in Sediments
Nutrients in the sediment can be limiting to the
growth of SAV (Short 1987; Agawin et al. 1996) but do
not seem to eliminate it from colonizing certain areas.
In marine siliceous sediments, nitrogen may limit SAV
growth (Short 1987; Alcoverro et al. 1997) while in
marine carbonate sediments, phosphorus may be lim-
iting to SAV growth (Wigand and Stevenson 1994).
Potassium has been suggested to be limiting to the
growth of freshwater SAV (Anderson and Kalf 1988).
Mycorrhizae have been found to facilitate the phos-
phorus assimilation in V americana (Wigand and
Stevenson 1997), but little or no information is avail-
able on mycorrhizae in the rhizosphere of marine SAV
(Wigand and Stevenson 1994).
Although light seems to be more limiting to SAV
growth than sediment nutrient concentrations, excep-
tions can be found. In tropical SAV beds, light and
temperature are limiting in the winter while nutrients
are limiting in the summer (Alcoverro et al. 1997b).
Additionally, ammonium concentrations as low as
25 /im (in the seawater) can be toxic to Z. marina
and ultimately lead to its decimation (van Katwijk
et al 1997).
Microbial-Based Phytotoxins
A wide variety of potentially phytotoxic substances is
produced by bacterial metabolism in anaerobic sedi-
ments, including phenols and organic acids, reduced
iron and manganese and hydrogen sulfide (Yoshida
1975; Gambrell and Patrick 1978). In many aquatic
environments, sulfide probably constitutes the most
important of these toxic bacterial metabolites and has
been shown to be toxic to estuarine and marine SAV
species (van Wijck et al 1992; Carlson et al 1994).
Sulfide is generated by sulfate reducing bacteria dur-
ing organic carbon oxidation and nutrient remineral-
ization in anoxic sediments (Howarth 1984; Pollard
and Moriarty 1991). A high remineralization rate leads
to high nutrient availability and favors plant growth
but can also lead to the accumulation of sulfide, which
is detrimental to plant growth. Sulfate remineraliza-
tion depends on the temperature and amount of
organic matter in the sediment. In freshwater sedi-
ments, sulfate reduction is less important than
methanogenesis due to the lower sulfate availability.
As SAV tends to accumulate more organic and inor-
ganic particles than unvegetated areas, sulfate reduc-
tion rates can be expected to be higher within the
vegetation than outside it (Isaksen and Finster 1996;
Holmer and Nielsen 1997). This difference could also
be due to the excretion of organic compounds through
the roots (Blackburn et al. 1994).
Table. VI-7 summarizes sulfide levels observed in
healthy as well as deteriorating SAV beds, and Table
VI-8 lists sulfate reduction rates in healthy SAV beds.
While eutrophication can fuel sulfide production via
increased organic matter in the sediments, sulfide pro-
duction can also fuel eutrophication. The sulfide pro-
duced inhibits nitrification (NH4+ -> NO3") and,
consequently, increases ammonium fluxes to the water
column, which could act as a positive feedback to
eutrophication (Joye and Hollibaugh 1995).
A parallel mechanism also applies to the release of
phosphorus from the sediments. When sulfides react
with iron in the sediment, iron sulfide is formed and
phosphorus is released (Lamers et al. 1998). If the
phosphorus is not taken up by the plants, it may be
released back into the water column and, as stated
above for nitrogen, may be a positive feedback to
eutrophication.
The toxicity of sulfide to plants can also be enhanced
by eutrophication. Oxygen released from SAV roots is
needed to oxidize the sulfide and reduce its toxic
effects (Armstrong 1978). The release of oxygen by the
roots depends on the photosynthetic rates of the plant.
Therefore, if eutrophication leads to a reduction in
light availability, photosynthetic rates will be lower,
the amount of oxygen released from the roots will also
be reduced and sulfide toxicity may be enhanced
(Goodman et al. 1995).
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 91
TABLE VI-7. Sulfide levels in the sediments of healthy and dying SAV beds.
Sulfide concentration 1 Species
Plant Status
Source
350 to 1,000
88 m mol m'2 AVS
0.48 to l
> 400
> 2,000
^ote the different units.
Thalassia testudinum Healthy
Halodule beaudetti Healthy
Halophila engeltnanii Growth
Carlsonef a/. 1994
Blackburn etal. 1994
Pulich 1983
Potamogeton pectinatus Declining growth van Wijck et al. 1992
Zostera marina
T. testudinum
Reduced
photosynthesis
Dead
Goodman etal. 1995
Carlson etal. 1994
TABLE VI-8. Sulfate reduction in healthy SAV beds.
Sulfate reduction
Species
Source
20 n mol ml"1 fr1
87 to 445 n mol cm'3 d"1
7 to 7.9 m mol m'2 d'1
9.4 m mol m"2 d'1
12 to 16.8 m mol m"2 d'1
16.3 m mol m'2 d'1
27.9 to 33. 4m mol m-'d
59.1mmolnV2d-1
Zostera noltii
Z. noltii
Syringodium isoetifolius
Halophila
Zostera capricorni
Halodule uninervis
Halodule beaudetti
Cymodocea serrulata
Zostera marina
Welsh etal. 1996
Isaksen and Finster 1996
Perry and Dennison submitted
Perry and Dennison submitted
Perry and Dennison submitted
Perry and Dennison submitted
Blackburn et al. 1994
Perry and Dennison submitted
Holmer and Nielsen 1997
'Note the different units.
-------
92 SAV TECHNICAL SYNTHESIS II
Sulfide concentration in the sediment is an important
SAV habitat requirement. Correlations between pore-
water sulfide concentrations and the growth of several
SAV species have indicated that concentrations above
1 mM may be toxic (Pulich 1989; Carlson et al. 1994).
Direct manipulations of sulfide concentrations
revealed a negative effect on photosynthesis (Good-
man et al. 1995) and growth (Kuhn 1992) when levels
were higher than 1 to 2 mM. Sulfide thresholds for dif-
ferent SAV species (in combination with different light
levels) still need to be determined. Until such data are
available, critical sulfide concentrations cannot be
specified as an SAV habitat requirement.
CHEMICAL CONTAMINANTS
This review is intentionally confined to the broad
issues of the potential roles contaminants may have in
limiting the size, density and distribution of SAV pop-
ulations. Literature values pertaining to the relation-
ships between SAV and chemical contaminants are
derived from three diverse lines of inquiry: contami-
nant studies, phytoremediation efforts (Garg et al
1997; Peterson et al. 1996; Ramanathan and Burks
1996; Salt et al. 1995), and recommendations for
aquatic weed control (Anderson and Dechoretz 1982;
Anderson 1989; Nilson and Klaassen 1988). Appendix
B summarizes some of the more recent work devoted
to contaminant issues.
Most of the chemical contaminant studies have evalu-
ated the effects of herbicides on SAV growth (Fleming
et al. 1993). A few have examined other pesticides or
heavy metals (Garg et al. 1997; Gupta and Chandra
1994; Gupta et al. 1995). Thus, the vast majority of com-
pounds known to have toxic effects on biological sys-
tems remain untested (Van Wijngaarden et al 1996)
and only a few efforts have been made to systematically
evaluate additive, cumulative and synergistic effects of
multiple contaminants (Fairchild et al. 1994; Huebert
and Shay 1992; Sprenger and Mclntosh 1989).
Nonetheless, the following conclusions can be drawn
from these accumulated data.
• Herbicides have been shown to be phytotoxic to
SAV. Toxicity is somewhat species-dependent and
chemical-specific. Table VI-9 depicts the toxicity
range of the most widely used herbicides in the
TABLE VI-9. Relative effects of herbicides on net photosynthesis in Potemogeton pectinatus. The IC50 is
the predicted concentration that inhibits photosynthesis by 50%. Photosynthesis was determined by
measuring O2 production by plants over 3 hours at 20-22°C and about 58% p10,000
>1,000;<10,000
29
32
>10,000
70
240
>10,000
8
164
>10,000
95% Confidence
Interval
20-42
21-48
43-112
130 - 420
5-12
82 - 327
Slope
-50.97
-42.91
-59.14
-36.04
-41.25
-88.80
R2
0.81
0.88
0.64
0.68
0.86
0.58
-------
Chapter VI - Beyond Light: Physical, Geological and Chemical Habitat Requirements 93
United States onPotamogetonpectinatus. Inhibit-
ing concentrations range from 8 ppb to 10,000
ppb. These concentrations are consistent with
those observed when aquatic weed control is the
management objective, as well as in environ-
ments where the protection of aquatic plants is
the management objective.
• Pesticides other than herbicides have been shown
to have a phytotoxic effect on SAV, although only
a few have been evaluated.
• Heavy metals at levels corresponding to some
ambient conditions have inhibiting effects on
SAV in test systems where the variety of essential
plant nutrients has been experimentally factored.
• The environments holding the greatest potential
for pesticide suppression of SAV populations are
headwaters and shallow waters immediately adja-
cent to the urban, forest and agricultural areas
where pesticides are most widely used and acute
concentration level exposures are most likely
to occur.
• The environments holding the greatest potential
for adverse effects of heavy metals are those
where clay and organic sediments chemically
concentrate both metals and plant nutrients for
extended periods.
• The utility of ambient testing of contaminant
concentrations is highly controversial. For pesti-
cides, the constraint of monitoring frequency and
location are limiting factors for accurate ambient
assessment of contaminant presence. Assessment
of heavy metals and other contaminants is con-
founded by the difficulty of distinguishing
between the concentration of biologically active
forms and total concentration (Liang and Schoe-
nau 1996).
PHYSICAL AND GEOLOGICAL SAV HABITAT
REQUIREMENTS
In order to fully define the SAV habitat requirements
in Chesapeake Bay, parameters other than light and its
modifiers need to be taken into consideration. Some
physical, geological and geochemical parameters have
the potential to override established SAV light
requirements. Where field and laboratory experimen-
tal data were sufficient, physical and geological SAV
habitat requirements were identified (Table VI-10).
-------
94 SAV TECHNICAL SYNTHESIS II
TABLE VI-10. Summary of physical and geological SAV habitat requirements for Chesapeake Bay.
Parameter
SAV Habitat
Requirement
Observations
Current velocity
Oligohaline Habitats
Polyhaline Habitats
Minimum depth of
SAV distribution
More data are needed (specially for canopies and
0 to ? cm s'1 meadows; plants with polar/non polar leaves) to
>10 and < 100 cm s"1 further define this SAV habitat requirement.
A is the tidal amplitude ({MHW-MLWJ/2 for
diurnal tides and (MHHW-MLLW}/2 for semi-
diurnal tides); g is the acceleration of gravity (9.805
m s"1); and T is the wave period.
Z . =4+J*.
mm f\ />
Maximum depth of
SAV distribution
Should be defined by IJ10 is the minimum percent light required by the
species under consideration. Kd is the light
-ln(—) attenuation coefficient. This calculation should use
I0 MWL (mean water level) as a reference. Using
- MHW (mean high water) may underestimate
d and using MLW (mean low water) may
overestimate Zm,T.
max
Tides
Sediment grain size
Oligohaline Habitats
Polyhaline Habitats
Sediment organic
content
SAV can be expected
to successfully
colonize areas where
No restrictions?
<20% silt and clay
0.5 m has been chosen as a conservative value of
the smallest vertical depth of colonization observed
for SAV in Chesapeake Bay. This value was found
to be 1 m for eelgrass in Long Island Sound and
needs to be adjusted for the species and sites in
question. Ztt6a is the minimum depth of distribution
and depends on the tidal range and wave mixing
depth (see Equation VI-4).
These preliminary SAV habitat requirements are
based on compilations of data from the literature.
Specific studies are needed to confirm these
preliminary requirements.
This SAV habitat requirement is based on a
compilation of data from the literature. Although all
literature values converge to the suggested value,
specific studies are needed to further verify the
requirement.
-------
CHAPTER V||
Setting, Applying and Evaluating
Minimum Light Requirements for
Chesapeake Bay SAV
"Phis chapter defines the two types of minimum light
I requirements for Chesapeake Bay SAV, and
explains how to test their attainment using two new
percent-light parameters that can be calculated from
water quality monitoring data. It compares the five
1992 habitat requirements from the first SAV technical
synthesis (Batiuk et al. 1992, Dennison et al. 1993) to
the new minimum light requirements, in terms of
attainment of habitat requirements and their ability to
predict SAV presence. The chapter also describes the
adjustment of the new percent-light parameters to
account for tidal range, and examines the relationships
between the two percent-light parameters and the aver-
age depth at which SAV is growing in Chesapeake Bay.
DEFINING AND APPLYING THE
MINIMUM LIGHT REQUIREMENTS
AND WATER-COLUMN LIGHT REQUIREMENTS
Evaluation of ambient water quality conditions sup-
porting the required amount of light reaching SAV can
be viewed in terms of two percent-light parameters.
• Percent light through water (PLW) is the amount
of ambient sub-surface light absorbed or
reflected by water itself, water color caused by
dissolved organic materials, suspended organic
and sediment particles, and phytoplankton in the
water column down to the sediment surface at
the restoration depth selected.
• Percent light at the leaf (PLL) is the amount of
ambient light that actually reaches the SAV leaf
after penetrating the overlying water column and
that is further reduced after being absorbed or
reflected by epiphytic material growing (attached
algae) or settled (organic and inorganic solids) on
the SAV leaf surface at the restoration depth
selected.
The percent light through water parameter is a
component of the percent light at the leaf parameter
(Figure VII-1). To reflect these two ways of evaluating
percent light, we define a "minimum light require-
ment" with attainment tested by the percent light at
the leaf, or PLL parameter (Table VII-1). We also
define a related "water-column light requirement"
with attainment tested, as described above, by the per-
cent light through the water or PLW parameter. Note
that in the original SAV technical synthesis, published
by Batiuk et al. (1992), the habitat requirements had
the same names as the parameters used to test their
attainment, but now they have different names.
The 1992 water quality-based habitat requirements for
Chesapeake Bay SAV were applied separately to test
their attainment (Batiuk et al. 1992). The light attenu-
ation coefficient (Kd) habitat requirement, applied
alone, was roughly the equivalent to the water-column
light requirement defined here and evaluated by the
percent light through the water parameter. Evaluated
collectively, the attainment of the five 1992 SAV habi-
tat requirements provided the best estimate at the
time for defining the water column conditions neces-
sary to achieve sufficient light at the SAV leaf surface.
This estimate is replaced with the attainment of the
minimum light requirements defined here.
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 95
-------
96 SAV TECHNICAL SYNTHESIS II
Percent
Light at
the Leaf
(PLL)
100% Ambient Light of Water Surface
Percent Light
Through Water
(PLW)
SAV Leaf
I
3
o
g_
c
3
CT
O
S
r+
O
V)
00
»
a
Suspended Solids Settled on Epiphytes
Epiphytes Growing on SAV Leaf
FIGURE VIM. Two Percent Light Parameters for Evaluating Ambient Conditions. Illustration of the relationship of
the two percent light parameters and the water quality conditions influencing both of them.
Water-Column Light Requirements
The water-column light requirements are the same as
the two light requirements derived from the in-depth
review and analysis of a wide variety of data modeling
and research findings documented in Chapter III: 13
percent for tidal fresh and oligohaline areas and 22
percent for mesohaline and polyhaline areas. Since
most of the SAV light requirement studies summa-
rized in Chapter III had epiphytes on the SAV, but the
light measurements in those studies did not estimate
light attenuation due to epiphytes, we used these light
requirements to set the water-column light require-
ments. The attainment of the water-column light
requirements is tested using the percent light through
the water (PLW) parameter.
Minimum Light Requirements
Minimum light requirements were determined by
comparing the results of three lines of evidence.
1. Calculation using the 1992 SAY habitat
requirements.
One line of evidence was derived by applying the
salinity regime-based values for the 1992 SAV habi-
tat requirements for Kd, dissolved inorganic nitro-
gen, dissolved inorganic phosphorus and total
suspended solids (Table VII-1) into the equation
for determining PLL (Equation V-l) (Table V-l),
PLL= [e-^X^fe-^WjlOO.
Using this equation, a PLL value of 8.3 percent was
calculated for tidal fresh and oligohaline salinity
regimes. In mesohaline regimes, the calculated
PLL value was 17.3 percent, while it was 13.5 per-
cent in polyhaline regimes. The mesohaline and
polyhaline PLL values differed, even though they
had the same 1992 Kd and dissolved inorganic
nitrogen SAV habitat requirements, because they
had different dissolved inorganic phosphorus SAV
habitat requirements. This difference influenced
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 97
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-------
98 SAV TECHNICAL SYNTHESIS II
the limiting nutrient and, therefore, the resulting
calculated PLL value. From application of the 1992
SAV habitat requirements, minimum light require-
ments of 8 percent for tidal fresh/oligohaline
regimes and 15 percent (the average of 17.3 and
13.5) were derived from this line of evidence.
2. Accounting for Epiphytic Light Attenuation
As discussed in Chapter III, the scientific studies
used to derive the water-column light targets did
not factor in the shading effects of epiphytes, which
grow on SAV leaves at all depths and on experi-
mentally shaded plants in the field. Several studies
in various estuarine habitats indicate that light
attenuation by epiphytic communities tends to con-
tribute an additional 15 to 50 percent shading on
SAV (e.g., Borum and Wium-Andersen 1980,
Bulthuis and Woelkerling 1983, van Dijk 1993).
One recent detailed study of turtlegrass beds in
Florida coastal waters (Dixon 2000) showed that,
while light levels at the maximum depth of seagrass
colonization averaged about 22 percent of surface
irradiance, epiphytic attenuation reduced this to
approximately 14 percent of the surface light that is
actually available for plant photosynthesis. This
represents an average of approximately 35 percent
additional shading by epiphytes.
Light attenuation by epiphytic material appears to
be generally important throughout Chesapeake
Bay, contributing 20 to 60 percent additional atten-
uation (beyond PLW) in the tidal fresh and oligo-
haline regions, where nutrient and total suspended
solids concentrations were highest, and 10 to 50
percent in the less turbid mesohaline and polyha-
line regions (Figure V-ll). These calculated contri-
butions of epiphyte shading are consistent with the
values derived for PLW and PLL by applying the
1992 SAV habitat requirement values in equations
II-1 and V-l, respectively, where PLL represents
approximately 30 percent additional light reduction
from PLW (Table VII-1).
Based on literature values for seagrass minimum
light requirements, where epiphyte effects were
either avoided with experimental manipulation
(e.g., Czerny and Dunton 1995) or taken into
account with direct measurement (e.g., Dixon
2000), and results from analysis of Chesapeake Bay
data, epiphytic material was assumed to make a 30
percent additional contribution to light attenuation
throughout Chesapeake Bay shallow water habi-
tats. Accounting for the epiphytic contribution to
light attenuation, minimum light requirements for
mesohaline/polyhaline and tidal fresh/oligohaline
habitats, respectively, were calculated to be 15 per-
cent and 9 percent of surface irradiance. These val-
ues, which represent the actual minimum light
needed to support SAV growth at the leaf surface,
include the additional 30 percent epiphytic light
attenuation beyond the respective water-column
light targets derived in Chapter III. For mesoha-
line/polyhaline habitats, factoring the additional 30
percent epiphytic light attenuation into the 22 per-
cent water-column light target yields a 15 percent
minimum light requirement as 30% = 100(22-
15)/22. A 9 percent minimum light requirement for
tidal fresh/oligohaline habitats was derived by fac-
toring the additional 30 percent epiphytic light
attenuation into the 13 percent water-column light
target, as 30% = 100(13-9)/13.
The derived SAV water-column light requirement
and minimum light requirement for Chesapeake
Bay's mesohaline and polyhaline habitats, 22 per-
cent and 15 percent surface light, respectively
(Table VII-1), are remarkably close to the respec-
tive values of 22 percent and 14 percent surface
light derived through field experimentation for
turtlegrass in Florida (Dixon 2000) through this
second line of evidence.
3. Comparisons of Field Conditions and
SAV Growth Gradients
Medians of nearshore water quality data (from the
Choptank and York rivers) and Chesapeake Bay
Monitoring Program midchannel data were
assessed for relationships between calculated PLL
values, SAV growth categories and the proposed
mesohaline/polyhaline and tidal fresh/oligohaline
minimum light requirements of 15 percent and 9
percent, respectively. As described in detail in the
section "Comparing Water Quality Medians over
Categories of SAV Growth," the calculated PLL
values from observed water quality conditions asso-
ciated with "persistent" and "fluctuating" SAV beds
were either all very close to or well above the mini-
mum light requirement, or the limited set of devia-
tions could be readily explained, confirming the
proposed values through the third line of evidence.
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 99
From these three lines of evidence, minimum light
requirements of 15 percent surface light for mesoha-
line/polyhaline habitats and 9 percent surface light for
tidal fresh/oligohaline habitats were established (Table
VII-1). The attainment of the minimum light require-
ment is tested using the PLL parameter.
Primary and Secondary Habitat Requirements
The minimum light requirement is considered the
"primary habitat requirement" (Table VII-1). All the
other requirements, including the water-column light
requirement, are called "secondary habitat require-
ments." This nomenclature was chosen because testing
the attainment of the minimum light requirement is
the primary means for assessing whether an area of
shallow water has water quality adequate to support
SAV growth, whenever the data needed to calculate
PLL are available: light attenuation coefficient (Kd) or
Secchi depth, dissolved inorganic nitrogen, dissolved
inorganic phosphorus and total suspended solids
measurements. SAV habitat quality should be evalu-
ated using the water-column light requirement as a
substitute for the minimum light requirement only if
the data needed to calculate PLW are available (light
attenuation coefficent or Secchi depth) and the
parameters needed to calculate PLL, dissolved
inorganic nutrients and total suspended solids, are
unavailable.
Once the attainment of the minimum light require-
ment has been tested, the attainment of the secondary
habitat requirements should be tested only if the min-
imum light requirement is not met. Testing attainment
of the secondary requirements can suggest possible
reasons for non-attainment of the minimum light
requirement. The secondary requirements should only
be used as diagnostic tools for research or manage-
ment purposes. See Chapter V for a description of a
diagnostic tool based on the total suspended solids and
chlorophyll a secondary habitat requirements.
If the minimum light requirement is met, but SAV is
absent from or sparse in the nearby area, a review of
the many factors that can prevent SAV growth should
be undertaken (see Chapter VI).
Calculating Percent Light Parameters
Building on their initial descriptions in Chapter V,
those applying the minimum light requirements need
to understand the following terms.
Z: SAV restoration depth. This is measured from
just below the water surface to the sediment-water
interface, which is where a submerged plant must
start growing. Z is used in formulae for PLW and
PLL to specify the path length for light passing
through water. This depth is referenced to MLLW
or MTL (see below).
PLW: percent light through water. The percent of
the light level measured just below the surface of
the water that reaches the restoration depth Z, after
passing through the overlying water column but not
through any epiphytes or associated material on an
SAV leaf surface. When Z = 1 meter below MLLW
with no tidal range adjustment, PLW is equivalent
to Kd (light attenuation coefficient) in the 1992 SAV
habitat requirements (Batiuk et al. 1992). In this
document, both of those conditions are relaxed for
PLW as well as PLL: here Z is varied from 0 to
1 meters, and is referenced to mean tidal level with
a tidal range adjustment.
PLL: percent light at the leaf. This refers to the per-
cent of light measured just below the surface of the
water that reaches the surface of an SAV leaf grow-
ing at restoration depth Z (at the sediment-water
interface), after passing through the water column
and any epiphytes and associated material on an
SAV leaf.
MLLW: mean lower low water. This is the mean ele-
vation over time of the lower of the two daily low
tides, where there are mixed tides, as in most of
Chesapeake Bay. Mixed tides occur when the two
high and two low tides each day occur at different
elevations. MLLW is used as the reference for the
bathymetry on nautical charts to minimize the risk
of boats running aground, but it does not estimate
the average depth of water (and thus the average
path length for light attenuation) above a sub-
merged plant through the day.
MTL: mean tidal level. The midpoint between high
and low tides; where there are mixed tides, the mid-
point between MHHW (mean higher high water)
and MLLW. MTL estimates the average depth of
-------
100 SAV TECHNICAL SYNTHESIS II
water above a submerged plant (see Chapter VI for
details).
PLL calculations require measured values of light
attenuation coefficient, total suspended solids, dis-
solved inorganic nitrogen and dissolved inorganic
phosphorus (Figure VII-2). The value for Kd can be
based on a direct measure of light attenuation calcu-
lated by lowering a light meter down through the water
column or converted from Secchi depth data using the
conversion factor Kd = 1.45/Secchi depth (see Chapter
III). However, in some cases there will not be enough
monitoring data to calculate PLL, but there will be Kd
or Secchi depth data that can be used to calculate
PLW. PLW, Kd and PLL are related as follows.
Light measurements (of photosynthetically active radi-
ation, PAR, using a flat cosine sensor) that are needed
for PLL and PLW calculations include:
I0 = light level just below surface of the water
(usually at 0.1-meter depth); and
Iz = light level at depth Z (often measured at
1.1-meter depth for SAV monitoring; in this
example,
z = 1.1-0.1 = 1.0).
Note that when calculating Kd or PLW from pairs of
light measurements, Z represents the difference
between the depth of the subsurface light measure-
ment (Io) and the depth of the deeper light measure-
ment (Iz). Otherwise it represents the restoration
depth chosen.
Another light value needed to calculate PLL, Ize
(which is not measured directly, but is calculated from
Kd, Z, total suspended solids, dissolved inorganic
phosphorus and dissolved inorganic nitrogen) is light
level at the leaf surface (after passing through the
water column as well as any epiphytes and associated
material).
Percent Light through Water (PLW)
Percent Light at the Leaf (PLL)
100% Ambient Light of Water Surface
Inputs
•Kd measured directly
or
•Kd calculated from
Secchi depth
Calculation
•Total suspended solids
•Dissolved inorganic
nitrogen
•Dissolved inorganic
phosphorus
Calculation
Evaluation
PLW vs. Water-Column
Light Requirement
•Ke = Epiphyte attenuation
•Be " Epiphyte biomass
Evaluation
PLL vs. Minimum Light
Requirement
Approach followed when
only Secchi depth/direct
light attenuation data are
available
Recommended approach for
best determination of the
amount of light reaching SAV
leaves
FIGURE VII-2. Calculation of PLW and PLL and Comparisons with their Respective Light Requirements.
Illustration of the inputs, calculation, and evaluation of the two percent light parameters: percent light through water
and percent light at the leaf.
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 101
PLW and PLL can then be expressed as:
PLW = IJ10 x 100
PLL = Ize/Io x 100
or
PLL = [e -] [e
from Chapter V, Table V-l.
x 100
(VIM)
(VII-2)
(VII-3)
See Appendix C for the SAS code used to calculate
PLL from Kd, total suspended solids, dissolved inor-
ganic nitrogen and dissolved inorganic phosphorus. Kd
can be converted to PLW, and PLW to Kd as follows:
yi0 (PLW/100) =
(VII-4)
where Z is in meters and the units for Kd are m1
(Batiuk et al. 1992, page 17; Equation VII-2). This
would be written in SAS or other programming lan-
guage to calculate PLW from Kd as:
PLW = exp(-Kd*Z) * 100
(VII-5).
Taking the natural log of both sides of this equation,
Kd can also be calculated from PLW:
In(PLW) = -Kd*Z + ln(100), or
Kd = -ln(PLW/100)/Z
(VII-6).
Because the calculation of PLL includes total sus-
pended solids and dissolved inorganic nutrient data, it
cannot be converted to an equivalent Kd value.
Adjusting Percent Light Parameters for
Tidal Range and Different Restoration Depths
All of the percent light formulae used in this chapter
were adjusted to account for the tidal range. The 1992
Kd requirements (2 or 1.5 nr1 depending on salinity,
Batiuk et al. 1992) used Z = 1 with no adjustment
because Batiuk et al. (1992) assumed a new plant was
growing at the sediment surface with 1 meter of water
above it. They assumed that there was (on average) 1
meter of water above the sediment surface at the 1-
meter mean lower low water (MLLW) contour, which
was called "MLW" in that document. This assumption
was incorrect. At the 1-meter MLLW contour there
will be 1 meter of water above the plant only once a
day, when the lower low tide occurs (assuming mixed
tides). Thus, on average, there will be more than 1
meter of water above this point, and the light reaching
the sediment surface will be less than what was
expected when the 1992 requirements were set.
This greater depth of water above the plant would be
offset somewhat as the plant grew and the upper parts
were closer to the surface, but the intent of the Chesa-
peake Bay SAV habitat requirements has always been
to predict conditions needed to establish new plants,
which must start growing at the sediment surface. The
habitat requirements were also intended to predict the
conditions needed to restore SAV to a particular shal-
low water area of the tidal Chesapeake, as defined by
the Tier II and Tier III restoration targets (see Chap-
ter VIII). These are the areas of potential SAV habitat
to the 1- and 2-meter depth contours, respectively
(Batiuk et al. 1992). Because those contours were
mapped relative to MLLW, the restoration depth (Z)
in the percent light formulae must be adjusted with the
tidal range to reflect the light conditions at the sedi-
ment surface at those depths more accurately. This
essentially changes the tidal elevation reference to the
mean tidal level (MTL) instead of MLLW. However,
NOAA does not provide contours referenced to MTL,
since they would be useless for navigation. If the user
of these requirements is not interested in predicting
water quality conditions needed to restore SAV to a
particular restoration depth, they could ignore the
tidal range adjustment. For this reason, any users of
these percent-light parameters should state in their
methods section what Z value(s) were used, and
whether any adjustment to Z was made for tidal range.
Before Z can be adjusted for tidal range, value (s) for
Z must be chosen. In Batiuk et al. (1992), Z = 1 meter
was used for the 1-meter restoration requirements and
all the Kd calculations, and Z = 2 meters was used only
for the 2-meter restoration requirement (Kd < 0.8 m"1),
(Table 1, page iii, and Table I V-l, page 27). SAV
growth to waters 1-meter deep was thought possible
under current light conditions, while growth to the 2-
meter depth was judged likely only under greatly
improved light conditions, thought to have existed in
the 1950s and before, based on the documented
deeper growth of SAV in Chesapeake Bay in these
time frames.
In this chapter, Z was set to 2,1, 0.5, 0.25 and 0 meters
MLLW, before adjusting it for the tidal range. Some
analyses used only some of these depths. The Z value
was varied to as low as 0 meters (representing
the intertidal zone) because in some segments,
-------
102 SAV TECHNICAL SYNTHESIS II
particularly the upper tidal Patuxent River, SAV cur-
rently grows intertidally. Intertidal growth enables
SAV to grow in areas where light conditions are cur-
rently too poor to allow SAV growth in deeper water
(see below). In other estuaries, Z could be set to any
other value where there are different light or tidal con-
ditions or different restoration targets.
Accounting for the tidal range was done by adding half
of the tidal range to the value used for Z above (see
Appendix D for details on tidal range estimation). It is
recognized that this is not the most accurate method to
estimate the average light over a tidal cycle, but was
selected for simplicity, and any introduced errors are
small. Where there are mixed tides as in most of
Chesapeake Bay, the tidal range needed is the "diur-
nal" or "greater tropic" range, defined as (MHHW-
MLLW). To show that Z has been varied and has had
half the tidal range added to it, the variable names
used for calculated values of PLW and PLL are:
PLW(2+) and PLL(2+): Z is set to 2 meters and
half the tidal range is added to it;
PLW(1+) and PLL(1+): Z is set to 1 meter and
half the tidal range is added to it;
PLW(0.5+) and PLL(0.5+): Z is set to 0.5 meters
and half the tidal range is added to it;
PLW(0.25+) and PLL(0.25+): Z is set to 0.25
meters and half the tidal range is added to it; and
PLW(0+) and PLL(0+): Z is set to 0 meters and
half the tidal range is added to it.
Although Kd values could also be adjusted using the
method described here for PLW and PLL, this was not
done to maintain a clear distinction between the 1992
light requirements described here (using Kd assuming
Z = 1 meter and no tidal adjustment) and the minimum
and water-column light requirements described here
(using PLL or PLW respectively, to test their attainment,
with a range of Z values and a tidal range adjustment).
All PLL or PLW values are compared to the same
respective light requirements (listed in Table VII-1)
regardless of which depth Z is used and whether or not
they have been adjusted to account for tidal range.
That is because the light requirements represent how
much light is needed by the plants, regardless of where
they are growing. The adjustments to Z to account for
tidal range are done to better predict how deep SAV
are likely to be able to grow under current light condi-
tions averaged over tidal depth.
Changing the Z values compared to what was used in
Batiuk et al. (1992) raises the question of what Z value
with tidal range adjustment is most comparable to the
Z = 1 meter with no tidal range adjustment estab-
lished in the original SAV technical synthesis in 1992.
Since half the tidal range is close to or above
0.5 meters in many segments (see Appendix D, Table
D-4), setting Z to 0.5 meters is roughly equivalent to
analyses using Z = 1 with no adjustment. This
assumption was verified by the analysis of medians
over SAV growth categories (see tables VII-2 and VII-
4). Thus, PLW(0.5 + ) will give medians and attain-
ment results most similar to the equivalent Kd values
using the 1992 requirements, while PLW(1+) will give
a more accurate estimate of the light reaching the 1-
meter MLLW depth contour, or a leaf of SAV at that
depth contour.
Recommendations for Applying Percent Light
Variables and Other Habitat Requirements
Calculate the percent-light variables (PLL or PLW)
for a chosen restoration depth Z. If Z is referenced to
a depth contour and thus to a depth below MLLW, add
half the diurnal tidal range to Z. If Z is referenced to
mean tidal level (MTL), do not add half the tidal range
to Z. (This assumes that Kd has already been calcu-
lated, or Secchi depth has been used to estimate Kd;
see above for how to do this.)
Choose the restoration depth(s) Z based on local con-
ditions. In Chesapeake Bay we have targets to restore
SAV to shallow water areas to 1- and 2-meter depths
below MLLW (see Chapter VIII), but in some cases
we have set Z to shallower depths (see above).
Use (or collect) Kd data rather than Secchi depth if
both are available. Kd is based on the light wave-
lengths used in photosynthesis and Secchi depth uses
visual light. Also, Kd can be measured more accurately
in clear, shallow waters where the Secchi disk is still
visible on the bottom.
Calculate PLL whenever the data needed for it are
available (Kd or Secchi depth, total suspended solids,
dissolved inorganic nitrogen and dissolved inorganic
phosphorus). Since the minimum light requirement
(which is tested with PLL data) is the primary or most
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 103
useful single habitat requirement, PLL data are needed
to test it. Calculate PLW when Kd or Secchi data are
available, but one or more of the other parameters
needed for PLL calculations are not available.
Use water quality measurements from the "surface"
layer (usually 0.1- to 0.5-meters deep, sometimes 1-
meter deep). These measurements, along with Kd or
Secchi depth data, should come from water quality
monitoring station(s) as close as possible to the poten-
tial habitat or actual SAV bed of interest.
Collect water quality data for calculating parameters
used to test minimum light requirement attainment
during the SAV growing season only. In Chesapeake
Bay this is April-October (7 months) for tidal fresh,
oligohaline and mesohaline areas. In polyhaline areas,
where eelgrass (Zostera marina) is dominant,
the growing season is March-May and September-
November (6 months), due to summer dieback of eel-
grass. Sampling frequency should be at least monthly.
If there are missing data, no tests of attainment should
be done unless there are data from at least four differ-
ent months during the SAV growing season.
Where eelgrass is present in mesohaline areas, or
extensive widgeongrass (R. maritimd) is present in
polyhaline areas, it may be informative to calculate
medians using both growing seasons. In other estuar-
ies, the local growing season should be determined for
each of the dominant species from SAV biomass and
water temperature measurements over at least one
year.
Test the attainment of PLL values by comparing their
growing season medians to the minimum light
requirement for the applicable salinity regime (9 per-
cent or 15 percent; see Table VII-1). Attainment of
PLW values is tested by comparing them to the water-
column light requirement for the applicable salinity
regime (13 percent or 22 percent; see Table VII-1).
Test the attainment of the minimum light requirement
or the water-column light requirement in one of two
ways: by calculating PLL or PLW medians and deter-
mining if they are greater than or less than the mini-
mum light requirement or water-column light
requirement, respectively, or by a nonparametric
statistical test. If software to perform the statistical
test is available, it should be used, rather than the
median comparison, since it uses more of the informa-
tion in the data. The two methods are:
1. To compare median PLL or PLW values over the
SAV growing season to the minimum light
requirement or the water-column light require-
ment. If the medians are greater than the mini-
mum light requirement or water-column light
requirement, the requirement is "Met"; if the
median is less than or equal to the minimum
light requirement or water-column light require-
ment, the requirement is "Not Met."
2. To perform a statistical test by calculating the
difference between the individual measurements
of the percent light parameter used, PLL or
PLW, and the appropriate minimum light
requirement or water-column light requirement,
respectively, and running a nonparametric sign
test on the difference variable. This tests the null
hypothesis that the difference is zero, or that
there was no difference between the measured
data, PLL or PLW, and the minimum light
requirement or water-column light requirement,
respectively. See Appendix D for details; the
three outcomes are called "Met," "Borderline"
or "Not Met."
EVALUATING MINIMUM LIGHT REQUIREMENTS
USING CHESAPEAKE BAY WATER QUALITY
MONITORING DATA AND SAV SURVEY DATA
The next four sections of this chapter use Chesapeake
Bay water quality monitoring data and SAV distribu-
tion data to evaluate the minimum light requirements
that were set above and to see how useful the percent
light at the leaf parameter is in testing their attainment.
These evaluations are not attempting to test the model
used to develop the percent light at the leaf calcula-
tions, since most of the monitoring data (both for water
quality and SAV area) available were collected over too
broad a spatial and temporal scale to be used for that
purpose. The goal of the next four sections was to see
how well the results of analyses of baywide monitoring
data fit with expectations in four different areas:
1. Was there better median water quality where
there was more SAV growth, and worse median
water quality where there was less or no SAV
growth?
2. Are there segments where the minimum light
requirements consistently failed, that had SAV
-------
104 SAV TECHNICAL SYNTHESIS I
growing in them? If so, can we determine rea-
sons for this apparent paradox?
3. Were the percent light parameters and other
SAV habitat requirements significantly corre-
lated with measures of SAV area by Chesapeake
Bay Program segment and year, regardless of the
depth at which it was growing?
4. Were the percent light parameters and other
SAV habitat requirements significantly corre-
lated with measures of SAV area by Chesapeake
Bay Program segment and year over four depth
categories?
Where there are discrepancies, these may be produc-
tive areas for future research, which usually would
involve more detailed monitoring of both water qual-
ity and SAV abundance. There are many other similar
questions that could be asked. The water quality and
SAV area data are available through the Chesapeake
Bay Program and Virginia Institute of Marine
Science web sites, (www.chesapeakebay.net and
www.vims.edu/bio/sav, respectively) for researchers
and managers who want to explore other questions.
The two key fields linking the two data sets are Chesa-
peake Bay Program segment and year. Data for both
water quality and SAV area are available for each year
from 1985 onward, except for 1988, when the SAV sur-
vey was not conducted due to budget constraints.
There are 78 Chesapeake Bay Program segments, but
only 69 of them have water quality data available.
These segments vary in overall size and in the extent of
shallow water habitat. For that reason, in many of the
analyses described below, the SAV area measured in a
segment was divided by the extent of shallow water
habitat in that segment, measured as the "Tier II" area
(see Chapter VIII for a definition). Most of the water
quality data were measured at midchannel stations,
often in fairly deep water, which may limit their use-
fulness in these types of analyses (see Chapter IX).
COMPARING WATER QUALITY MEDIANS OVER
CATEGORIES OF SAV GROWTH
For this section, medians of nearshore water quality
data (from the Choptank and York rivers) and Chesa-
peake Bay Monitoring Program midchannel data were
assessed for empirical relationships with SAV growth
categories. It was expected that medians would be bet-
ter where there was more SAV growth and worse
where there was less growth. "Better" here means
lower levels for Kd, total suspended solids, chlorophyll
a, dissolved inorganic phosphorus, dissolved inorganic
nitrogen and higher levels (more light) for PLW and
PLL. The point at which the habitat requirements fell
among the medians over the different growth cate-
gories was used as an empirical check on the values
chosen for the habitat requirements. The comparisons
using Chesapeake Bay Program midchannel water
quality data are a less rigorous test of the habitat
requirements than those using nearshore data, since
the Bay water quality monitoring stations may be sev-
eral kilometers or more from shallow water SAV habi-
tat, and Bay water quality monitoring stations were not
placed along gradients of SAV growth. The 1992 SAV
habitat requirements were based on the nearshore
data analyzed here, but have not been checked with
Bay water quality monitoring data using this method.
Methods
Three growth categories were used for analyses using
nearshore water quality data and five growth cate-
gories for analyses using midchannel water quality
data; see Appendix D for definitions of the categories.
The tidal fresh and polyhaline salinity regimes had
segments falling in only four of the five growth
categories.
The ranges of annual seasonal median nearshore
water quality over SAV growth categories were used in
Batiuk et al. (1992) to help determine the habitat
requirements. The authors of that document examined
the maximum medians at monitoring stations near
healthy or fluctuating SAV beds and used those to help
set the habitat requirement (it was not always set at
the maximum). The assumption was that if some SAV
were growing near the station with the maximum
median, then SAV should be able to grow at similar
sites where that median water quality occurs. This
approach was used here as an empirical check or veri-
fication of the 1992 habitat requirements, rather than
as a way to derive the requirements.
For the nearshore data, maxima of the annual growing
season medians by station were used, as in Batiuk et al.
(1992) (or minima for PLL/PLW), while for the
Chesapeake Bay Program midchannel monitoring
data, medians of the annual growing season medians
by segment were used. The midchannel stations are
not as close to SAV beds as the nearshore stations, and
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 105
the SAV growth categories for CBP midchannel data
are based on aerial survey data, not on transplant suc-
cess nearby. The more general nature of these data
argued for using medians instead of maxima. Maxima
could be used for the nearshore data because the sta-
tions were near SAV beds. In the midchannel data, if
maxima are used, they are generally worse than the
habitat requirements, even in segments with SAV. This
is probably because midchannel stations tend to be far-
ther from SAV than the nearshore stations.
Kruskal-Wallis nonparametric ANOVA was used to
compare differences among the median water quality
values over different categories of SAV growth, using
the NPAR1WAY procedure in SAS. This analysis tests
the null hypothesis that all the SAV growth categories
had the same median water quality. Statistically signif-
icant differences (ANOVA P' < 0.05 in the following
tables, shown in bold) show that water quality differed
among segments in the different SAV growth cate-
gories, which is the expected outcome for water qual-
ity parameters that affect SAV growth.
Results and Discussion
Minima of annual growing season medians of nearshore
monitoring data from the Choptank and York rivers
(mesohaline and polyhaline, respectively) for the per-
cent light habitat requirement parameters are shown in
Table VII-2. Maxima of annual growing season medians
for the secondary habitat requirement parameters other
than PLW are shown in Table VII-3. Both tables group
the data using three categories of SAV growth based on
the persistence of SAV transplants near the monitoring
stations. Data from 1986-1989 were used in both tables;
these are the same data that were analyzed by Batiuk et
al. (1992) to set the original SAV habitat requirements
for Chesapeake Bay.
Medians of annual growing season median values from
Chesapeake Bay Program midchannel water quality
monitoring stations by salinity regime for the percent
light habitat requirement parameters are shown in
Table VII-4. Medians of annual growing season
median values for the secondary habitat requirement
parameters other than PLW from the same stations
TABLE VII-2. Minima of annual SAV growing season medians of percent-light parameters from Choptank
and York River nearshore monitoring stations by salinity regime and nearby SAV growth category, com-
pared to the minimum light requirement and water column light requirement values shown, and Kruskal-
Wallis ANOVA P for differences among categories, using 1986-1989 data and adding half the tidal range
to the restoration depth Z value listed for the percent light through water (PLW) and percent light at the
leaf (PLL).
Salinity
Mesohaline
(Choptank)
ANOVA P
Polyhaline
(York)
ANOVA P
SAV
Growth
Persistent
Fluctuating
None
Persistent
Fluctuating
None
PLW
3.2%
1.5%
0.0%
0.0001
5.2%
3.5%
0.5%
0.0001
PLW
(1+)
WCLR
14.5%
9.3%
0.4%
0.0001
WCLR
17.8%
14.0%
4.1%
0.0001
PLW
= 22%
31.1%
22 9%
3.3%
0.0001
= 22%
32.3%
oc oo/
t f fl
12.2%
0.0001
PLW
45.4%
36 1%
9.3%
0.0001
44.7%
40 0%
21.2%
0.0001
PLL
3.0%
1.4%
0.0%
0.0001
4.0%
2.4%
0.4%
0.0001
PLL
MLR
13.5%
7.9%
PLL
= 15%
28.8%
16 1%
0.4% 2.0%
0.0001 0.0001
MLR =15%
12.8%
9.1%
3.1%
0.0001
22.2%
161%
8.2%
0.0001
PLL
42.1%
259%
4.6%
0.0001
30.7%
207%
13.4%
0.0001
N
11
19
18
11
7
7
Note: Double lines separate values above and below the habitat requirement. For all of these parameters, less is
worse (less light). P values from Kruskal-Wallis nonparametric one-way ANOVA showing significant differences
among growth categories (at P < 0.05) are in bold. WCLR = water-column light requirement. MLR = minimum
light requirement. N is the number of station x year combinations in that category.
-------
106 SAV TECHNICAL SYNTHESIS II
TABLE VII-3. Maximum of annual SAV growing season medians compared to secondary habitat require-
ments, other than PLW, from Choptank and York River nearshore monitoring stations by salinity regime
and SAV growth category, and Kruskal-Wallis ANOVA results, using 1986-1989 water quality data for total
suspended solids (TSS), chlorophyll a (CHLA), dissolved inorganic phosphorus (DIP) and dissolved
inorganic nitrogen (DIN).
Salinity
Reefime
Mesohaline
(Choptank)
ANOVA P
Polyhaline
(York)
ANOVA P
SAV TSS CHLA DIP DIN
Growth
Persistent 140 11.5 00055 0167
Fluctuating 22.0 13.2 00136 0.223
None 36.5 38.5 0.0498 1.55
0.0001 0.0038 0.0001 0.01
Persistent HR no 00260 0128
Fluctuating 21.1 134 00369 0.173
None 25.6 20.6 0.0499 0.182
0.0011 0.42 0.0004 0.101
N
11
19
26
11
7
7
Note: Double lines separate values above and below the habitat requirement. For all of these parameters, more is
worse. P values from Kruskal-Wallis nonparametric one-way ANOVA showing significant differences among
growth categories (at P < 0.05) are in bold. N is the number of station x year combinations in that category.
are shown in Table VII-5. Both tables group the data
using five categories of SAV growth based on the
median, minimum and maximum SAV area mapped in
the same CBP segment from 1978-1997 (see Appendix
D for methods). Water quality data from 1985-1998
were used in both tables.
ANOVA significance levels (P values) show that almost
all of the medians of these parameters differed signifi-
cantly among the categories of SAV growth, which was
expected. The exceptions were chlorophyll a and dis-
solved inorganic nitrogen in the York River (Table VII-
3) and total suspended solids and chlorophyll a in
polyhaline areas (Table VII-5). Significant differences
among growth categories were not expected in two of
these four cases because all the medians for total sus-
pended solids and chlorophyll a in polyhaline areas in
Table VII-5 were better than the total suspended solids
and chlorophyll a habitat requirements set in Batiuk et
al. (1992). The polyhaline regime also has fewer seg-
ments and thus had among the smallest sample sizes in
Table VII-5, making it more likely that ANOVA results
will not be significant.
The point at which the established SAV habitat
requirements fell among the medians over growth cat-
egories is shown with a double line under or over those
cells in tables VII-2 through VII-5 and as horizontal
lines for the minimum light requirement in Figure
VII-3. In most salinity regimes, segments with median
water quality better than the habitat requirements had
some SAV, while those with median water quality
worse than the habitat requirements had less or no
SAV. This was the expected pattern, which can also be
seen in Figure VII-3 for PLL wherever at least one bar
crosses the dashed line representing the minimum
light requirements. Note that the y-axis scales are
different in low- and high-salinity regimes in Figure
VII-3. The median of PLL(0.5+) was greater (better)
than the minimum light requirement for the Always
Abundant growth category in all salinity regimes
except oligohaline, but the PLL(1+) median was
greater (better) than the minimum light requirement
for the Always Abundant category in mesohaline and
polyhaline regimes only. The PLL(1+) median was
also near or above the minimum light requirement for
the Sometimes None and Always Some categories in
mesohaline and polyhaline regimes.
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 107
TABLE VII-4. Medians of annual SAV growing season medians of percent light parameters from
Chesapeake Bay Program midchannel water quality monitoring stations, by salinity regime and SAV
growth category compared to the minimum light requirement and water column light requirement values
shown, and Kruskal-Wallis ANOVA P for differences among categories, using 1985-1998 data and adding
half of the tidal range to Z for the percent light through water (PLW) and percent light
at the leaf (PLL) parameters.
Salinity
Regime
Tidal fresh
ANOVA P
Oligohaliiu
ANOVA P
Mesohaline
ANOVA P
Polyhaline
ANOVA P
SAV
Growth
AA
SN
UN
AN
AA
AS
SN
UN
AN
AA
AS
SN
UN
AN
AA
AS
SN
AN
PLW
(2+)
1.4%
0.1%
0.0%
0.1%
0.0001
0.5%
0.3%
0.0%
0.0%
0.0%
0.0001
10.9%
6.4%
3.9%
1.7%
0.1%
0.0001
11.1%
6.7%
5.7%
2.9%
0.0001
PLW
fl+*
WCLR
8.5%
1.7%
0.3%
2.5%
0.0001
WCLR
5.4%
3.3%
0.9%
0.7%
0.5%
0.0001
WCLR
"294%
21.6%
15.9%
10.3%
1.9%
0.0001
_ WCLR
"285%
20.5%
18.4%
12.2%
0.0001
PLW
(O.S+\
= 13%
PLW
rt).25+*
209% 330%
7.5% 1 5 4%
2.2% 5.7%
9.8%
0.0001
= 13%
18 2%
11.1%
5.4%
4.5%
3.3%
0.0001
= 22%
47.8%
39.5%
32.9%
24 9%
8.0%
0.0001
= 22%
44.8%
36.1%
33.3%
25.1%
0.0001
20.1%
0.0001
=33.2%
21.3%
134%
11.1%
9.3%
0.0001
60.9%
53.5%
47.7%
39 9%
16.1%
0.0001
57.4%
47.8%
44.8%
36.1%
0.0001
PLL
(2+)
PLL
n+\
PLL
rt).5+*
PLL
f0.25+*
N
MLR =9%
1.3%
0.1%
0.0%
0.1%
0.0001
7.8%
1.4%
0.3%
1.9%
0.0001
181% 273%
5.6% 111%
1.3% 3.0%
6.6%
0.0001
12.9%
0.0001
14
=53
14
81
MLR =9%
0.4%
0.3%
0.0%
0.0%
0.0%
0.0001
3.2%
2.3%
0.8%
0.6%
0.3%
0.0001
8.5%
7.1%
4.3%
3.8%
2.2%
0.0001
14.1%
11.6%
13
42
101% 56
7.5% 25
5.5%
0.0001
92
MLR =15%
10.1%
5.8%
3.5%
1.5%
0.1%
0.0001
"25.8%
41.3%
184% 327%
14.1% 27.9%
78% 194%
1.5%
0.0001
5.3%
0.0001
53.0%
44.4%
38.8%
307%
10.3%
0.0001
28
125
95
98
47
MLR =15%
««•••«•••••«
9.8%
5.0%
4.4%
2.1%
0.0001
"24 8% 40 1%
13.2% 22.4%
13.1%
8.1%
0.0001
21.9%
15.0%
0.0001
50.9%
29.6%
28.5%
20.5%
0.0001
42
14
14
14
Note: Double lines under a cell separate values above and below the water-column light requirement (WCLR) or
minimum light requirement (MLR) shown. Numbers in parentheses are the restoration depth (Z) to which half
the tidal range was added before calculations. For all parameters, less is worse (less light). P values from
Kruskal-Wallis nonparametric one-way ANOVA showing significant differences among growth categories (at P
< 0.05) are in bold. N is the number of segment x year combinations in that category. AA = always abundant;
AS = always some; SN = sometimes none; UN = usually none; and AN = always none.
-------
108 SAV TECHNICAL SYNTHESIS I
TABLE VII-5. Medians of annual SAV growing season medians of parameters with secondary SAV habitat
requirements other than PLW, from Chesapeake Bay Program midchannel water quality monitoring
stations by salinity regime and SAV growth category, and Kruskal-Wallis ANOVA P for differences among
categories, using 1985-1998 water quality data for total suspended solids (TSS), chlorophyll a (CHLA), dis-
solved inorganic phosphorus (DIP) and dissolved inorganic nitrogen (DIN).
Salinity
Regime
Tidal fresh
ANOVA P
Oligohaline
ANOVA P
Mesohaline
ANOVA P
Polyhaline
ANOVA P
SAV
Growth
AA
SN
UN
AN
AA
AS
SN
UN
AN
AA
AS
SN
UN
AN
AA
AS
SN
AN
TSS CHLA DIP
9 975 8 825 0 006
DIN N
0.9409 14
20 23.8 001 5 06643 53
24.025 19.375 0.0328
17 7.6942 0.02
0.0001 0.0001 0.0001
17 4.65 0.0465
18.5 8175 00139
25 28.65 0005
27.3 17.37 0.0234
32.75 13.03 0.02
0.0001 0.0001 0.0001
7.95 8.1 0.004
10.5 9.15 0.007
11 10 0005
15 15.15 0.01
27 11.89 0.015
0.0001 0.0001 0.0001
10 6.3479 0.003
9.75 5.8613 0.01
1105 71324 00147
11.5 5.95 0.0245
0.50 0.59 0.0001
1.1708 14
0.37 81
0.0001
0.859 13
0.636 42
^0.115 56
0.146 28
0.2255 98
0.0001
0.079 28
0.105 125
0.082 95
0091 98
0.1765 47
0.0001
0.045 42
0.1175 14
0 1403 14
0.21 12
0.0001
Note: Double lines under a cell separate values above and below the respective habitat requirement; see Table
Vn-1 for values. For all parameters, more is worse. P values showing significant differences among growth
categories (at P < 0.05) are in bold. N is the number of segment x year combinations in that category. AA =
always abundant; AS = always some; SN = sometimes none; UN = usually none; and AN = always none.
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 109
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PLL indi
-------
110 SAV TECHNICAL SYNTHESIS II
There were a few deviations, however, from the
expected pattern in some segments.
• PLW(2+) and PLL(2+) medians were worse
than their applicable water-column and mini-
mum light requirements, respectively, in all cate-
gories (Table VII-4, Figure VII-3).
• The oligohaline midchannel medians for several
parameters—total suspended solids, PLW(1+),
PLL(1+) and PLL(0.5+)—in tables VII-4 and
VII-5 were worse than the habitat requirements
in all growth categories, even in the one segment
(POTOH) in the highest SAV growth category
(Always Abundant). However, the median for
PLW(0.5+) was better than the water-column
light target in the Always Abundant category, and
thus fit the expected pattern.
• The tidal fresh midchannel medians for
PLW(1+) and PLL(1+) were also worse than the
light requirements and targets in all growth cate-
gories, although the medians for PLW(0.5+) and
PLL(0.5+) were better than the respective water-
column and minimum light requirements in the
Always Abundant category, and thus fit the
expected pattern (Table VII-4).
• In the polyhaline nearshore data the maximum
median for dissolved inorganic phosphorus in the
'Persistent' category, 0.026 mg/1, was just above
the dissolved inorganic phosphorus habitat
equirement, 0.02 mg/1 (Table VII-3).
• In polyhaline segments using the Chesapeake
Bay Program water quality monitoring midchan-
nel data, the total suspended solids and chloro-
phyll a medians were all better (lower) than the
habitat requirements (Table VII-5).
• In the nearshore data, the minima of mesohaline
and polyhaline medians for PLW(1+) and
PLL(1+) were all worse than the light require-
ments (Table VII-2).
Of the six deviations described above, only the last one
appears to require further scrutiny, for the following
reasons:
• In the first deviation, we do not expect restora-
tion for 2 meters to be possible under current
conditions.
• The next two deviations are in tidal fresh and
oligohaline segments, where the match between
habitat requirement attainment and the presence
of SAV is not as close as in higher salinity seg-
ments, due partly to the very shallow depth dis-
tribution of SAV in some low salinity segments.
• The fourth deviation is minor, affecting only one
parameter-dissolved inorganic phosphorus.
• The fifth deviation was found in midchannel
monitoring data, which is often collected rela-
tively far from the SAV beds in polyhaline seg-
ments, given the relatively large size of these
higher salinity segments.
This leaves as a problem to be explained, the sixth
deviation, the fact that all the PLW(1+) and PLL(1+)
medians from nearshore data from the Choptank and
York rivers were worse than the respective light
requirements, including those from stations near "Per-
sistent" SAV beds (Table VII-2). Likely reasons for
these deviations are as follows.
As noted above, half the tidal range is close to or
above 0.5 meters in many segments (0.3 meters and 0.4
meters, respectively, in these Choptank and York seg-
ments), so setting Z to 0.5+ in these analyses (rather
than 1+) is closer to the analyses in Batiuk et al. (1992)
that were used to derive the 1992 Kd requirements, in
which Z = 1 with no tidal range adjustment. For
PLW(0.5+) and PLL(0.5+) in Table VII-2, the mini-
mum and water-column light requirements fell
between the medians for "Fluctuating" and "None"
categories, exactly as would be expected based on what
the requirements mean. Most of the PLW(0.5+) and
PLL(0.5+) medians in tidal fresh and oligohaline seg-
ments were also consistent with expectations (except
PLL(0.5+) in oligohaline segments) (Table VII-4).
In both rivers, the extreme values that set the minima for
PLW and PLL either came from a single station and year,
and the next larger median was consistent with the
requirement or target, or the minima were not that much
lower than the requirement. In the Choptank River
mesohaline data, many of the extreme values came from
a single station and year, Chapel Creek in 1986. Without
these anomalous data, in the "Persistent" category the
minimum PLW(1+) median was 22.1 percent instead of
14.5 percent, and the PLL(1+) median was 21.3 percent
instead of 13.5 percent, both of which are better than the
water-column light requirement and minimum light
requirement for that salinity regime (22 percent and 15
percent, respectively).
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 111
In the York River polyhaline data, the minimum for
PLW(1+) for "Persistent," 17.8 percent, was from a
single station and year (Guinea Marsh in 1987); the
next higher PLW(1+) values, 19.6 percent and 21.3
percent, were closer to the water column light require-
ment of 22 percent. The minimum for PLL(1+) in that
segment, 12.8 percent, was close to the minimum light
requirement for that salinity regime (15 percent); the
two next higher values in the "Persistent" category
were 13.3 percent and 14.1 percent.
Thus, it appears that the minimum and water-column
light requirements do not need to be adjusted to
account for any of the discrepancies noted above,
when comparing them to the medians over growth
categories from midchannel and nearshore water qual-
ity monitoring data.
Another more general comparison was done to check
if segments with better water quality tended to have
more SAV One would generally expect the best water
quality where there was the most SAV, and the worst
water quality where there is the least SAV, in this
sequence: Persistent > Fluctuating > None for
nearshore data, or Always Abundant (AA) > Always
Some (AS) > Sometimes None (SN) > Usually None
(UN) > Always None (AN) for midchannel data.
The maximum, minimum and median levels in tables
VII-2 through VII-5 were usually, but not always,
directly proportional to the amount of SAV present.
This pattern was seen most clearly in the nearshore
data (tables VII-2 and VII-3) and in the mesohaline
and polyhaline segments in tables VII-4 and VII-5.
IDENTIFYING SEGMENTS WITH PERSISTENT
FAILURE OF THE MINIMUM LIGHT
REQUIREMENTS AND CHECKING
THEM FOR SAV GROWTH
Each Chesapeake Bay Program segment with water
quality data was compared to determine whether con-
sistent failure of the minimum light requirement pre-
dicted a lack of SAV growth. This was done by looking
for any Chesapeake Bay Program segments with per-
sistent failure of the minimum light requirement that
contained appreciable amounts of SAV (over 35
hectares). In these segments, possible reasons for the
mismatch were examined. The corresponding analysis,
checking for segments with the minimum light
requirement usually met where there was little or no
SAV, would not be useful because SAV can be lacking
for a variety of reasons (lack of propagules, high wave
action, etc.).
Methods
Chesapeake Bay Program segments with PLL medians
failing the minimum light requirements more than half
of the years from 1992-1997 were tabulated using the
sign test at P = 0.05 and the FREQ procedure in SAS.
The restoration depth (Z) was varied over 1, 0.5, 0.25
and 0 meters plus half the tidal range. Segments were
identified as persistently failing the minimum light
requirements at the lowest (worst) depth if they had
failed half or more of the years. The 1997 SAV area
was checked for each of the segments with persistent
failure at 0.5 meters or less, and the segment was
flagged as a problem segment if the SAV area was over
35 hectares (86 acres). The expectation was that SAV
would not grow where the minimum light requirement
was failed persistently. This SAV hectare cutoff was
somewhat arbitrary, but was chosen to leave out some
small segments (such as the Northeast and Bohemia
rivers) that contain very small amounts of SAV,
sometimes a single bed. For each of the problem
segments, possible reasons for the presence of SAV
were examined.
Results and Discussion
Figure VII-4 shows the Chesapeake Bay Program seg-
ments with median PLL values failing the minimum
light requirement at different Z values half of the
years or more between 1992 and 1997. Those segments
failing at Z = 0.5 meters or less are identified by name.
Of that group, there were only two problem segments,
Patuxent tidal fresh and oligohaline. Both segments
failed the minimum light requirement at Z = 0.25
meters plus half the tidal range and had more than
35 hectares of SAV in 1997. It is likely that they con-
tain SAV in spite of the poor light conditions because
most of the SAV present in both segments is growing
in very shallow water, including some growing in the
intertidal zone.
COMPARING DIFFERENT SAV HABITAT
REQUIREMENTS AS PREDICTORS
OF SAV AREA
Correlations of annual estimated SAV area with annual
median water quality were used to test the ability of
each of the 1992 SAV habitat requirements and the two
-------
112 SAV TECHNICAL SYNTHESIS II
Northeast (C),
w
Bohemia (B)
(Q
Potomac
Tidal Fresh (C),
Oligohaline(C)
Gunpowder (
Back(B)
Chester
Oligohaline(A)
Choptank
.Oligohaline(B)
Patuxent
Tidal Fresh (B),
Oligohaline(B)
Nanbcoke
Tidal Fresh (C)
Mesohaline(B)
Wicomico
MesohalinefC)
Mattaponi
Oigohaline(C
Pamunkey
Qigohaline(B) -"^-
York
Mesohaline(B)~
Pocomoke
TWal Fresh (C)
FAILED AT LEAST HALF OF YEARS
A: Failed at 0.0 m MLLW + half TR
B: Failed at 0.25 m + half TR
C: Failed at 0.5 m + half TR
D: Failed at 1.0 m + half TR
E: Not Failed at any depth
East Branch Elizabeth (C)
South Branch Elizabeth (B)
FIGURE VII-4. Segments Failing PLL Requirements Half of the Years of More, 1992-97. Chesapeake Bay
Program segments with SAV growing season median PLL values failing the minimum light requirement half the
years or more between 1982 and 1997. Only the Chesapeake Bay Program segments in categores A, B, and C
are labeled. TR = diurnal tidal range. Segment names in bold indicate both the Patuxent tidal fresh and oligohaline
segments had PLL failed at 0.25 meter + half TR (category B) or less, AND appreciable amounts (>35 ha) of SAV.
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 113
percent-light parameters to predict SAV area. This abil-
ity is a desirable feature of any SAV habitat require-
ments, since it justifies using the habitat requirements
to set nutrient and sediment reduction targets and to
target the best areas for SAV restoration. However, any
lack of significant correlations in this analysis does not
invalidate the model used to derive PLL. Most of the
data used were collected in large-scale monitoring pro-
grams, and not over the small spatial and temporal
scales needed for a research project.
In this analysis it was expected that light parameters
(Kd, PLW and PLL) would have stronger and more
significant correlations with SAV area than other habi-
tat requirement parameters, since ecological models
show that light is the primary factor limiting SAV dis-
tribution in Chesapeake Bay (Batiuk et al. 1992; chap-
ters III and V).
Methods
Correlations were calculated with the SAS procedure
CORK between SAV area in hectares, by year, and
median water quality from the Chesapeake Bay Pro-
gram water quality monitoring stations, by year.
Nearshore water quality data were not used because
they do not have associated SAV area data. Spearman
rank correlation (nonparametric) was used rather than
Pearson (parametric) correlation, because SAV area is
not normally distributed, even with transformations,
due largely to the large number of zeroes. The zeroes
make SAV area a difficult variable to use in correla-
tions, even Spearman correlations. Another problem
is that in many salinity regimes most or all of the seg-
ments and years with high SAV area are in only one or
two segments, so the results may reflect conditions in
those segments rather than in the all the segments in
the salinity regime. In Spearman correlations, if the
two data sets are ranked exactly the same way, rs =
+ 1; if they have opposite ranks, rs = -1, and 0 means
no association. For example, to be ranked the same
way, the segment and year with the highest PLL
median would have the highest SAV area; the second
highest PLL would have the second-highest SAV area;
and so on. When there are ties (as with the many
zeroes) they all receive the same rank, making it
harder to find a significant correlation. When dis-
cussing correlations the terms "stronger" and
"weaker" are used to mean "larger (or smaller)
absolute value of rs" since for some water quality
parameters the expected correlations with SAV area
are positive (PLW and PLL, more light means more
SAV), and for others they are negative (all other
parameters, more pollution means less SAV).
The spatial units used for this analysis were the 69
Chesapeake Bay Program segments that have water
quality data, grouped into salinity regimes. The time
periods over which water quality medians were calcu-
lated were the SAV growing season (April-October
except in polyhaline where it is March-May and Sep-
tember-November), or in spring (April-June, except
March-May in polyhaline segments). Spring data were
tested separately to see if spring water quality was
more important than water quality over the whole
growing season. After spring, many species are grow-
ing at or near the surface, and thus their survival and
growth might be less sensitive to water quality condi-
tions in the summer and fall.
Three different measures were used for SAV area
measured in the same year as the water quality data:
SAV hectares (SAVH), SAVH as a percent of Tier II
area (PCT_T2), and SAVH as a percent of Tier III
area (PCT_T3). The latter two measures were calcu-
lated to correct for the differing amounts of potential
SAV habitat in different CBP segments, which differ
greatly in size.
Lagged effects were tested two ways, by replacing SAV
area from the current year (SAVH) with SAV area
from the following year (LAGSAVH) or replacing it
with the change in SAV area from this year to the fol-
lowing year (CHGSAVH). The expected correlations
were negative for Kd, total suspended solids, chloro-
phyll a, dissolved inorganic phosphorus and dissolved
inorganic nitrogen, since for these parameters higher
levels should lead to a reduction in SAV area, and pos-
itive for PLW and PLL. Both Kd and PLW were
included because PLW calculations included the tidal
range adjustment for Z, while those for Kd did not.
Results and Discussion
Results are summarized in Table VII-6, and complete
results are given in Appendix E, tables E-l, 3, 5 and
7 over the whole growing season and in Appendix E,
tables E-2, 4, 6 and 8 over the spring portion of the
growing season. These correlations show the following
over the whole SAV growing season:
-------
114 SAV TECHNICAL SYNTHESIS I
TABLE VII-6. Salinity regimes that had statistically significant (P < 0.05) Spearman rank correlation
coefficients in expected directions, between water quality parameters from Chesapeake Bay Program
midchannel water quality stations and measures of SAV area over Chesapeake Bay Program segments
(see Appendix E, tables E-1 through E-8 for correlations, P values and sample sizes).
Water Quality
Parameter
Ka
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
PCT_
OH+
MH+
PH+
OH+
MH+
PH+
OH+
MH+
PH+
TF
OH+
MH+
MH+
OH#
MH+
PH+
MH#
PH
SAV Area
T2 PCT_
OH+
MH+
PH+
OH+
MH+
PH+
OH+
MH+
PH+
TF
OH+
MH+
MH+
TF+
OH#
MH+
PH+
MH+
PH
Parameter
T3 SAVH
OH+
MH+
PH+
OH+
MH+
PH+
OH+
MH+
PH+
OH+
MH+
OH
MH+
TF+
OH#
MH+
PH+
MH+
PH+
LAGSAVH CHGSAVH
OH+
MH+
PH+
OH+
MH+
PH+
OH+
MH+ MH
PH+
TF
OH+
MH+
OH
MH+
PH#
TF+
OH#
MH+
PH+
MH+
PH+ PH+
KEY. PCT_T2 = SAVH/Tier H area* 100, PCT_T3 = SAVH/Tier m area* 100, SAVH=SAV hectares for same year as
water quality data; LAGSAVH=SAV hectares for following year; CHGSAVH=change in SAV hectares from that year to
next. TF = tidal fresh, OH = oligohaline, MH = mesohaline, and PH= polyhaline (see Table VII-1 for saUnities).
Light attenuation coefficient = Kj; percent light through water = PLW; percent light at the leaf = PLL; total suspended
solids = TSS; chlorophyll a = CHLA; dissolved inorganic phosphorus = DIP; and dissolved inorganic nitrogen = DIN.
Regimes in bold had correlations >+/- 0.5 over the whole growing season; some were > 0.5 over the spring also.
+ Also had significant correlation over spring part of growing season (if no symbol, significant over whole year).
# Significant correlation over spring part of growing season only (April-June except March-May in polyhaline).
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 115
In oligohaline, mesohaline and polyhaline segments,
four of the five SAV area parameters (all but
CHGSAVH) showed the expected statistically signifi-
cant correlations (P < 0.05) with the three light
parameters: more light meant more SAV (negative
correlations for Kd, positive for PLW and PLL). The rs
values were near 0.35 for oligohaline and 0.55 for
mesohaline and polyhaline segments.
Most of the other habitat requirement parameters
tested had weaker but significant correlations in these
three salinity regimes, but the correlations were not
significant for dissolved inorganic nitrogen in oligoha-
line or total suspended solids and chlorophyll a in
polyhaline segments (except that chlorophyll a had a
significant correlation with LAGSAVH in the spring).
Oligohaline dissolved inorganic nitrogen was not
expected to correlate with SAV area because there is
no habitat requirement for dissolved inorganic nitro-
gen in tidal fresh and oligohaline segments.
Tidal fresh segments showed weaker correlations but
they were statistically significant for most area param-
eters for total suspended solids and dissolved inor-
ganic phosphorus.
The expected significant relationships were stronger
for PLL than for PLW in polyhaline segments, and
almost the same in other segments where they were
significant (oligohaline and mesohaline segments).
Correlations for Kd differed from those for PLW
because Kd was not adjusted for tidal range.
The three different measures of SAV area in the same
year as the water quality data (PCT_T2, PCT_T3, and
SAVH) usually had similar, significant correlations
with water quality variables over the whole growing
season, with a few exceptions. In tidal fresh segments,
SAVH did not have significant correlations with total
suspended solids while the other measures did, and
PCT_T2 lacked significant correlations with dissolved
inorganic phosphorus (Table VII-6). In oligohaline
segments, PCT_T2 and PCT_T3 lacked any significant
correlations with chlorophyll a, while these were found
for SAVH. In all segments with significant correla-
tions, the correlations were slightly stronger with
SAVH than with the other two parameters. Thus,
based on the correlations, there did not seem to be a
compelling reason to correct the SAVH variable with
the Tier II or Tier III areas, since correlations with
SAVH were slightly stronger (Appendix E).
Lagged SAV hectares (the area mapped the following
year, LAGSAVH) showed similar correlations with the
SAV habitat requirements as were found for SAV area
mapped the same year, except for chlorophyll a in
polyhaline habitats, which was significantly correlated
with lagged SAV area but not correlated with SAV
area the same year. Most of the significant polyhaline
correlations with SAV habitat requirements were
slightly higher for lagged SAV area (except for dis-
solved inorganic phosphorus), compared with SAV
area mapped the same year. This tends to support the
hypothesis that water quality in the current year affects
SAV area in the next year, but the effect was not a
dramatic one. Thus, unlagged SAV area (SAVH)
seems adequate for most correlative analyses.
Change in SAV hectares (the change from the area
this year to the area next year) did not have any signif-
icant expected correlations with water quality except
for PLL(1 + ) in mesohaline segments and dissolved
inorganic nitrogen in polyhaline segments.
Comparisons of significant correlations with water
quality over the whole growing season (Appendix E,
tables E-l, 3, 5 and 7) to significant correlations with
spring water quality (Appendix E, tables E-2, 4, 6 and
8) showed they differed in the following cases.
Spring correlations were weaker in tidal fresh seg-
ments compared to correlations over the whole grow-
ing season for total suspended solids and dissolved
inorganic phosphorus (the only two parameters that
had significant correlations over the whole year in tidal
fresh segments). Spring correlations were stronger in
oligohaline segments for Kd, PLW, PLL and dissolved
inorganic phosphorus and weaker for total suspended
solids and chlorophyll a. Whole-year correlations were
stronger or very similar in mesohaline segments for all
but one of the parameters with significant correlations
(Kd, PLW, PLL, chlorophyll a and dissolved inorganic
phosphorus). Spring correlations were slightly
stronger for total suspended solids. Spring correlations
were weaker in polyhaline segments for Kd, PLW, PLL,
dissolved inorganic phosphorus and dissolved inor-
ganic nitrogen. Spring correlations for chlorophyll a in
polyhaline segments were barely significant, but were
not significant over the whole growing season.
Spring correlations were weaker in 12 cases, and
stronger in six cases. Thus, there does not seem to be
a compelling reason to use spring water quality in SAV
-------
116 SAV TECHNICAL SYNTHESIS I
habitat requirements in place of water quality over the
whole growing season.
Now, as expected, the light parameters (PLL, PLW
and Kd) usually had the strongest correlation. In oligo-
haline segments, total suspended solids, PLW and PLL
had the strongest correlations with unlagged SAV area
over the whole growing season, while dissolved inor-
ganic phosphorus and total suspended solids had the
strongest correlations in tidal fresh segments. In meso-
haline and polyhaline segments, PLL, PLW and/or Kd
had the strongest correlations with unlagged SAV area
over the whole growing season.
Scatter plots for each salinity regime are graphed in
figures VII-5 through VII-8 for the four salinity
regimes, respectively. PLL(1+) was used on the x-axis
in all segments for consistency. PLL had the strongest
correlation with SAV area in polyhaline segments, and
the third-strongest correlation in oligohaline and
mesohaline segments. PLL did not have significant
correlations with SAV area in tidal fresh segments.
Note the large numbers of segments and years with
zero or very low SAV areas in all salinity regimes,
which make correlation analysis difficult. However,
the general pattern that can be seen is the expected
one: more SAV where PLL is higher.
CORRELATING SAV DEPTH WITH
MEDIAN WATER QUALITY FOR HABITAT
REQUIREMENT PARAMETERS
The same correlations done in the last section using
SAV area and water quality medians were repeated
using SAV depth by year, in place of SAV area by year.
The assumptions in this analysis were that SAV would
grow deeper where water quality was better, and shal-
lower where water quality was worse. In general, cor-
relations with light parameters (Kd, PLW and PLL)
were expected to be stronger than those with other
parameters, since light most directly determines the
depth at which SAV can grow. Correlations between
SAV depth and water quality were expected to be
weaker than those between SAV area and water qual-
ity because SAV depth is only available for segments
that had some SAV that year. This both reduces the
sample size for comparisons with SAV depth and
reduces the range in the associated water quality, since
generally the worst water quality occurs in segments
with no SAV. Both changes reduce the likelihood that
there will be statistically significant correlations
between SAV depth and water quality.
Factors other than water quality also affect the depth
at which SAV grows, however. Some SAV species have
a lower light requirement than others (see Chapter
III), and thus may be able to grow deeper than others,
which could increase the weighted mean SAV depth
where those species are dominant. Also, physical fac-
tors such as current, tides, sediment and wave action
could affect SAV depth distribution (see Chapter VI)
independently of water quality.
Methods
Methods are described here briefly; detailed methods
for calculating SAV depth and percent of SAV within
depth categories are given in Appendix E. SAV poly-
gons for each year were overlaid with depth contours
at 0.5, 1 and 2 meters MLLW. The area of SAV within
each Chesapeake Bay Program segment that fell
within four depth categories was calculated: less than
0.5 meters, 0.5 to 1 meter, 1 to 2 meters or greater than
2 meters deep. For this analysis, the four values of SAV
area over depth ranges were divided by the total area
to convert them to percentages of the total area in that
segment (PCT05, PCT1, PCT2 and PCTGT2) and also
to a single weighted mean depth (SAVDEP). Analysis
methods used were the same as in the previous sec-
tion, except that lagged depth and annual change in
depth were not examined, and correlations with spring
water quality were not examined due to small sample
sizes and relatively weak spring correlations in the
analyses done above.
Results and Discussion
Spearman rank correlations of percentages of SAV in
depth categories and weighted mean SAV depth with
SAV habitat requirement parameters are shown in
Appendix E, tables E-9 through E-12 for the four
salinity regimes. Results are summarized in Table VII-
7. For most parameters (Kd, total suspended solids,
chlorophyll a, dissolved inorganic phosphorus and dis-
solved inorganic nitrogen), negative correlations were
expected because more pollution should yield shal-
lower SAV, and positive correlations were expected for
PLW and PLL, since more light should yield deeper
SAV. Both expectations are reversed (positive for
most, negative for PLW/PLL) for the shallowest depth
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 117
140%
U)
5% 10% 15% 20% 25% 30% 35% 40% 45%
Growing season median PLL(H-) (% light at the leaf)
FIGURE VII-5. Tidal Fresh SAV Area vs. Percent Light
at the Leaf. Tidal fresh SAV area as a percent of the
Tier II SAV distribution restoration target by year and
Chesapeake Bay Program segment vs. median percent
light at the leaf [PLL(1 +)] by year and Chesapeake Bay
Program segment, using 1985-1998 data (no data from
1988). Spearman rs = 0.14, N = 124, P = 0.12.
100% i
0) 90%
|2 80%
jj 70%
^ 60%
°m 50%
10 40%
£
J5 30%
J 20%
5 10%
CO
0% .
0"
•
« .
• •
**
> •
/. 5% 10% 15% 20% 25% 30% 35% 40% 45%
Growing season median PLL(1+) (% light at the leaf)
FIGURE VII-6. Oligohaline SAV Area vs. Percent Light
at the Leaf. Oligohaline SAV area as a percent of the
Tier II SAV distribution restoration target by year and
Chesapeake Bay Program segment vs. median percent
light at the leaf [PLL(1 +)] by year and Chesapeake Bay
Program segment, using 1985-1998 data (no data from
1988). Spearman rs = 0.37, N = 182, P = 0.0001.
100% -,
0 90%
S>
£ 80% -
= 70% -
a
P 60%
« 50%
| 40%
3 30% -
a
•= 20% -
< 10%
0% -
0'
•
^«X *
\ » •• •
*^4 **»**^«»»* 1 t**t»»**»* *4^
*r** * -*-• ** "ftfftfvdt'ittitiiif* * «
H 5% 10% 15% 20% 25% 30% 35% 40% 45%
Growing season median PLL(1 +) (% light at the leaf)
FIGURE VII-7. Mesohaline SAV Area vs. Percent Light
at the Leaf Mesohaline SAV area as a percent of the
Tier II SAV distribution restoration target by year and
Chesapeake Bay Program segment vs. median percent
light at the leaf [PLL(1 +)] by year and Chesapeake Bay
Program segment, using 1985-1998 data (no data from
1988). Spearman rs = 0.51, N = 326, P = 0.0001.
100°'
0> 90%
ff
£ 80%
u 70%
o
^ 60%
850%
8 40%
•S 30%
£• 20%
>
< 10%-
0%
» •• • « •
• »
» ti 4, * * *
.* .*•;*•*
* A % *
** •• « «
. ^ „ „» ^ .»AA *.*^
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Growing season median PLL(1+) (% light at the leaf)
FIGURE VI1-8. Polyhaline SAV Area vs. Percent Light
at the Leaf. Polyhaline SAV area as a percent of the
Tier II SAV distribution restoration target by year and
Chesapeake Bay Program segment vs. median percent
light at the leaf [PLL(1 +)] by year and Chesapeake Bay
Program segment, using 1985-1998 data (no data from
1988). Spearman rs = 0.50, N = 60, P = 0.0001.
-------
118 SAV TECHNICAL SYNTHESIS II
TABLE VII-7. Salinity regimes that had statistically significant (P < 0.05) Spearman rank correlation
coefficients in expected directions, between water quality parameters from Chesapeake Bay Program
mid-channel water quality stations and measures of SAV depth over Chesapeake Bay Program segments
(see Appendix E, tables E-9 through E-12 for correlations, P values and sample sizes), using data from
whole growing season.
Water quality
naraineter
K,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
SAV depth
SAVDEP PCT05
TF
OH
PH
TF
OH
PH
TF
OH
PH
TF
OH
OH
MH
MH
PH
MH
PH
TF
OH
PH
TF
OH
PH
TF
OH
PH
TF
OH
TF
OH
MH
MH
PH
MH
PH
parameter
PCT1
TF
OH
PH
TF
OH
PH
TF
OH
PH
TF
OH
TF
OH
MH
MH
PH
MH
PH
PCT2
TF
OH
MH
TF
OH
MH
TF
OH
MH
TF
OH
OH
MH
MH
PH
MH
PH
PCTGT2
TF
OH
MH
PH
TF
OH
MH
TF
OH
MH
PH
TF
OH
OH
PH
MH
PH
KEY: SAV area that fell within four depth categories: less than 0.5 meters, 0.5 to 1 meter, 1 to 2 meters or greater than 2
meters deep, was mapped, and each area was divided by the total area over all four ranges to convert them to percentages of
the total area in that segment (PCT05, PCT1, PCT2 and PCTGT2, respectively) and also to a single weighted mean depth
(SAVDEP).
TF = tidal fresh, OH = oligohaline, MH = mesohaline, and PH= polyhaline (see Table VB-1 for salinities).
Light attenuation coefficient = K,,; percent light through water = PLW; percent light at the leaf = PLL; total suspended
solids = TSS; chlorophyll a = CHLA; dissolved inorganic phosphorus = DP; and dissolved inorganic nitrogen = DIN.
Regimes in bold had correlations >+/- 0.5 over the whole growing season.
-------
Chapter VII - Setting, Applying and Evaluating Minimum Light Requirements for Chesapeake Bay SAV 119
category, PCT05, since SAV is expected to be more
common in the shallowest depths when conditions are
worse because it cannot grow in deeper water.
Table VII-7 shows that, as expected, most of the light
parameters showed significant correlations with SAV
depth parameters in the expected directions, except
with some depth parameters in mesohaline and polyha-
line areas. In mesohaline areas, significant correlations
were found with the deeper percentages only (PCT2
and PCTGT2), and in polyhaline areas, significant cor-
relations were found with the shallower depths and the
weighted mean depth only (SAVDEP, PCT05, and
PCT1). Reasons for these differences are not known.
In examining correlations with the other water quality
parameters two patterns were seen. Correlations with
total suspended solids and chlorophyll a were stronger
in tidal fresh and oligohaline segments, while correla-
tions with nutrients were stronger in mesohaline and
polyhaline segments. Most of the correlations with
total suspended solids and chlorophyll a were signifi-
cant and in the expected directions in tidal fresh and
oligohaline segments, suggesting that they may affect
depth distributions along with light in lower salinity
areas. In mesohaline segments, only chlorophyll a had
significant correlations in the expected direction, and
in polyhaline segments, there were no significant cor-
relations with total suspended solids or chlorophyll a.
In tidal fresh and oligohaline areas, all of the correla-
tions between nutrients (dissolved inorganic phospho-
rus and dissolved inorganic nitrogen) and depth
parameters were either significant but in the wrong
direction, or not significant. This was expected for dis-
solved inorganic nitrogen, since there is no habitat
requirement for dissolved inorganic nitrogen in the
two lower salinity regimes. In mesohaline and tidal
fresh segments, all but one of the correlations with dis-
solved inorganic phosphorus and dissolved inorganic
nitrogen were significant and in the expected direc-
tions.
CONCLUSIONS
Comparisons of SAV area or depth to water quality
should always be done for salinity regimes separately.
None of the detailed relationships were consistent
across all salinity regimes.
In general, segments with median water quality better
than the SAV habitat requirements had some SAV,
while those with medians worse than the requirements
had less or no SAV. SAV also tended to grow at deeper
depths where water quality was better, and at shal-
lower depths where water quality was worse. This
provides empirical confirmation of the light require-
ments that were determined from research and
ecological modeling.
Segments that failed the minimum light requirement
in half of the past six years or more were identified and
their SAV area checked. There were two segments
with more than 35 hectares of SAV in 1997 that failed
PLL at Z = 0.25 plus half the tidal range or less. In
both cases, there were reasons why SAV could be
growing there even though monitoring data showed
the minimum light requirement was usually failed.
In the polyhaline regime, PLL was a better predictor
of SAV area and SAV depth than PLW, when there was
a significant relationship with SAV area. In other salin-
ity regimes, PLL and PLW were very similar as pre-
dictors, except in oligohaline segments, where PLW
was a slightly better predictor than PLL of SAV area
and depth.
PLL or PLW were often, but not always, the strongest
predictors of SAV area among all the SAV habitat
requirements. However, given the highly skewed dis-
tribution of SAV area data and differences in percent
light levels, these results are not really a test of the use-
fulness of these parameters.
In some cases, in all four salinity regimes, water qual-
ity showed slightly stronger correlations with the SAV
area mapped in the following year, compared to cor-
relations with SAV area in the current year. However,
the improvement with lagged SAV area did not appear
to be consistent enough or large enough to warrant
using the latter when calculating correlations with
water quality data, especially since lagging drops a year
off the sample size.
Spring median water quality did not appear to be con-
sistently better than growing season median water
quality in predicting SAV area, and in many cases
(especially in polyhaline segments) it was a worse pre-
dictor. Thus, we recommend using water quality data
over the whole growing season to assess attainment of
the SAV habitat requirements.
One or more of the three light parameters (Kd, PLW
and/or PLL) usually were the best predictors of SAV
-------
120 SAV TECHNICAL SYNTHESIS II
depth over most depth categories. In oligohaline seg-
ments, chlorophyll a had slightly stronger correlations
in three depth categories, and in polyhaline segments,
dissolved inorganic nitrogen and dissolved inorganic
phosphorus had stronger correlations than light with
the percent of SAV area in the two deeper categories.
Dissolved inorganic nitrogen and dissolved inorganic
phosphorus were also the strongest predictors of SAV
area in polyhaline segments.
Chesapeake Bay Program midchannel water quality
monitoring data often show the expected patterns in
analyses that compare SAV area and depth with water
quality, even though the stations are not located next
to SAV beds. This supports the continued use of these
data to assess attainment of SAV habitat require-
ments. However, care must be taken to omit data from
some stations where the SAV is in a different water
body from the monitoring stations (such as Little
Creek in segment CB8PH). In these cases, additional
water quality monitoring data from sites near the SAV
beds are needed.
-------
CHAPTER
Chesapeake Bay SAV Distribution
Restoration Goals and Targets
"T~he original tiered SAV distribution restoration targets
I for Chesapeake Bay were first published in the 1992
SAV technical synthesis in response to commitments set
forth in the Submerged Aquatic Vegetation Policy for the
Chesapeake Bay and Tidal Tributaries (Chesapeake Exec-
utive Council 1989). The Tier I SAV distribution restora-
tion target is the restoration of SAV to areas currently or
previously inhabited by SAV as mapped through regional
and baywide aerial surveys from 1971 through 1990
(Batiuk et al. 1992; Dennison et al. 1993). The Tier II and
Tier III distribution restoration targets are the restora-
tion of SAV to all shallow water areas delineated as exist-
ing or potential SAV habitat, down to the 1- and 2-meter
depth contours, respectively.
Baywide and Chesapeake Bay Program segment-based
target acreages were published in 1992 for the Tier I
and Tier III restoration targets (Batiuk et al. 1992). The
lack of sufficient Bay bottom bathymetry data to create
a 1-meter depth contour prevented delineation of the
Tier II restoration target at that time. In 1993 the
Chesapeake Executive Council formally adopted the
Tier I SAV distribution restoration target as the Chesa-
peake Bay Program's first quantitative living resource
restoration goal (Chesapeake Executive Council 1993).
The refined baywide and regional SAV distribution
restoration goals and targets presented here are critical
in assessing the success of efforts to restore SAV in
Chesapeake Bay and its tidal tributaries.
DISTRIBUTION TARGETS DEVELOPMENT
APPROACH
The tiered Chesapeake Bay SAV distribution restor-
ation targets were originally developed in 1992 by
mapping potential SAV habitat on U.S. Geological
Survey (USGS) quadrangles; removing shallow water
habitat areas where SAV were not expected to revege-
tate; and comparing these areas with historical survey
data and the most current distribution data. Compos-
ite SAV maps were plotted by USGS quadrangles from
all available computerized digital SAV bed data from
Chesapeake Bay aerial surveys from 1971 through
1990. The 1- and 2-meter depth contours at mean low
water (MLW) were digitized from National Oceanic
Atmospheric Administration (NOAA) bathymetry
maps. Because the NOAA bathymetry maps are rela-
tively inaccurate in small tidal creeks and rivers where
depth contours generally were not present, an overes-
timate of an area within a certain depth contour can
occur. These maps were overlaid at the 1:24,000 scale
to produce composite maps of known and documented
SAV distribution since the early 1970s, with the outline
of potential SAV habitat initially defined by the 1- and
2-meter depth contours.
Potential habitat was initially defined as all shoal areas
of Chesapeake Bay and tributaries less than 2 meters.
Although historically SAV in Chesapeake Bay probably
grew down to depths of 3 meters or more, the 2-meter
depth contour was chosen because it was the best com-
promise of the anticipated maximum depth penetration
of most SAV species. For several SAV species (notably
Myriophyllum spicatum and Hydrilla verticillata), maxi-
mum depth penetration might be greater than 2 meters,
but it was felt that this would be an exception.
Areas that were highly unlikely to support SAV were
annotated on the composite maps. Criteria for exclud-
ing certain areas from the maps were based primarily
on habitat areas exposed to high wave energy and that
have undergone physical modifications that prevented
them from supporting SAV growth. The absence of
documentation on the historical presence of SAV in a
Chapter VIII - Chesapeake Bay SAV Distribution Restoration Goals and Targets 121
-------
122 SAV TECHNICAL SYNTHESIS II
certain region of a tributary, embayment or the main-
stem was not used as a reason to delineate and exclude
the shallow water habitats in these regions as unlikely
to support future SAV growth. For example, some
areas that have not supported SAV in the recent past
(such as the tidal fresh and oligohaline areas of the
James, York and Rappahannock rivers) were included
in the distribution restoration targets. This distinction
was based on the following assumption: since the
upper Potomac River near Washington, D.C. sup-
ported these stands of SAV in the early 1900s (Gum-
ming et al. 1916), there should be no reason to assume
that SAV was not present in similar areas in the tidal
fresh and oligohaline reaches of other river systems in
Chesapeake Bay. The anecdotal evidence from dis-
parate regions of the Bay, as well as aerial photo-
graphic evidence for some areas in the 1930s, indicate
the major areas where SAV grew in the early part of
the 20th century. In addition, many small tidal creeks in
tidal fresh and oligohaline areas throughout the Bay
and its tidal tributaries today contain small pockets of
a variety of SAV species. It is assumed that these are
the last remnants of what were once large expansive
stands in earlier periods in the upper sections of these
tributaries. The seed and pollen records (Brush and
Hilgartner 1989) support the line of evidence that SAV
was once significantly more abundant than it is today.
The areas annotated as highly unlikely to support SAV
were digitized and deleted from the ARC/INFO files
of potential SAV habitat delineated by the 2-meter
depth contour. A second level of habitat restriction
was considered in those areas where SAV was
presently found or had the potential to grow in the 2-
meter contour. This habitat restriction was considered
in areas where wave exposure is highly likely to pre-
vent SAV from growing down 2 meters in depth but
would be dampened enough to allow SAV to grow
closer inshore (less than 1 meter). Assessment of areas
that would fall into this category was based on the
same criteria used to generate the composite maps for
the 2-meter restricted areas. The complete, detailed
description of the original process for developing the
tiered restoration goals and targets is found on pages
109-119 in Batiuk et al. (1992).
TIERED SAV DISTRIBUTION RESTORATION
GOALS AND TARGETS
To provide incremental measures of progress, a tiered
set of SAV distribution restoration targets have been
established for Chesapeake Bay. Each target repre-
sents expansions in SAV distribution that are antici-
pated in response to improvements in water quality.
These water quality improvements will be measured as
achievement of the minimum light requirements at
1- and 2-meter restoration depths. Progress toward the
SAV distribution restoration targets will continue to be
measured through the annual Chesapeake Bay SAV
Aerial Survey Monitoring Program.
Refinements have been made to the Tier I restoration
goal as a result of a reevaluation of the historical SAV
aerial survey digital data sets, including a thorough qual-
ity assurance evaluation, which resulted in corrections to
the original data. The revised Tier I restoration goal
areas are presented by Chesapeake Bay Program seg-
ments in Table VIII-1 and illustrated in Figure VIII-1.
The Tier II SAV distribution restoration target is the
restoration of SAV to all shallow water areas delin-
eated as existing or potential SAV habitat down to the
1-meter depth contour. Building from the recent com-
pletion of a synthesis of all available Bay bathymetry
data (Chesapeake Bay Program 1997), a 1-meter
depth contour along the entire Chesapeake Bay and
tidal tributaries shoreline was developed. The Tier II
target includes all areas of past SAV habitat delineated
in the Tier I goal, as well as shallow water habitats
delineated within this 1-meter depth contour (Figure
VIII-2; Table VIII-1). Tier II excludes areas where
SAV is considered unlikely to survive and grow due to
the direct and indirect adverse effects of high wave
action. These "exclusion zones" used for the Tier II
and Tier III targets described here were the same ones
used in defining Tier III areas in Batiuk et al. (1992).
The Tier III SAV distribution restoration target is the
restoration of SAV to all shallow water areas delin-
eated as existing or potential SAV habitat down to the
2-meter depth contour. A new 2-meter depth contour
along the entire tidal Bay shoreline was developed
through contouring the expanded Bay bottom bathym-
etry database. The revised Tier III target includes all
areas in the Tier I goal and Tier II target, as well as
shallow water habitats delineated within this new 2-
meter depth contour (Figure VIII-3; Table VIII-1).
The Tier III target excludes areas where SAV is con-
sidered unlikely to survive and grow due to the direct
and indirect adverse effects of high wave action.
Figure VIII-4 illustrates the Chesapeake Bay Program
Segmentation Scheme, and Table VIII-2 provides the
tiered SAV distribution restoration goals and targets in
terms of hectares.
-------
Chapter VIII - Chesapeake Bay SAV Distribution Restoration Goals and Targets 123
TABLE VIII-1. Chesapeake Bay SAV distribution restoration Tier I goal,
Chesapeake Bay Program segment in acres.
CBP
Segment
CB1TF
NORTF
ELKOH
C&DOH
BOHOH
SASOH
CB2OH
CB3MH
BSHOH
GUNOH
MIDOH
BACOH
PATMH
CHSMH
CHSOH
CHSTF
MAGMH
SEVMH
SOUMH
RHDMH
WSTMH
EASMH
CB4MH
CHOMH1
CHOMH2
CHOOH
CHOTF
LCHMH
PAXMH
PAXOH
PAXTF
WBRTF
HNGMH
FSBMH
NANMH
NANOH
NANTF
WICMH
MANMH
Chesapeake Bay Program
Segment Name
Northern Chesapeake Bay
Northeast River
Elk River
Chesapeake & Delaware Canal
Bohemia River
Sassafras River
Upper Chesapeake Bay
Upper Central Chesapeake Bay
Bush River
Gunpowder River
Middle River
Back River
Patapsco River
Lower Chester River
Middle Chester River
Upper Chester River
Magothy River
Severn River
South River
Rhode River
West River
Eastern Bay
Middle Central Chesapeake Bay
Mouth of the Choptank River
Lower Choptank River
Middle Choptank River
Upper Choptank River
Little Choptank River
Lower Patuxent River
Middle Patuxent River
Upper Patuxent River
Western Branch of the Patuxent River
Honga River
Fishing Bay
Lower Nanticoke River
Middle Nanticoke River
Upper Nanticoke River
Wicomico River
Manokin River
Tier I
Goal
7,690
20
1,105
2
42
408
660
1,725
57
865
860
0
124
3,751
0
0
586
465
52
15
116
6,126
376
7,388
462
0
0
1,522
356
2
15
0
3,951
32
0
0
0
0
682
, and tiers II
TierH
Target
13,714
934
2,785
124
1,132
2,614
5,439
4,702
2,068
2,896
1,431
1,302
1,984
6,990
1,663
766
1,500
1,347
1,485
623
1,092
13,091
7,949
12,968
3,771
852
0
8,102
5,155
1,436
504
32
10,645
6,939
3,188
1,522
598
3,442
6,336
and III targets by
Tierm
Target
20,401
2,743
5,028
170
1,905
3,699
9,212
5,510
4,606
7,460
2,481
2,861
3,543
11,510
2,310
870
2,177
2,108
2,288
904
1,527
20,808
9,301
18,424
6,222
1,285
0
11,799
8,829
2,073
707
32
15,481
13,633
7,714
2,056
887
6,385
9,338
continued
-------
124 SAV TECHNICAL SYNTHESIS II
TABLE VIII-1. Chesapeake Bay SAV distribution restoration Tier I goal, and tiers II and III targets by
Chesapeake Bay Program segment in acres (continued)
CBP
Segment
BIGMH
POCMH
POCOH
POCTF
TANMH
POTMH
POTOH
POTTF
MATTF
PISTF
CB5MH
RPPMH
CRRMH
RPPOH
RPPTF
PIAMH
CB6PH
CB7PH
MOBPH
YRKMH
YRKPH
MPNOH
MPNTF
PMKOH
PMKTF
JMSPH
ELIPH
LAFMH
ELIMH
EBEMH
SBEMH
WBEMH
JMSMH
JMSOH
CHKOH
JMSTF
APPTF
LYMPH
CB8PH
Chesapeake Bay Program
Segment Name
Big Annemessex River
Lower Pocomoke River
Middle Pocomoke River
Upper Pocomoke River
Tangier Sound
Lower Potomac River
Middle Potomac River
Upper Potomac River
Mattawoman Creek
Piscataway Creek
Lower Central Chesapeake Bay
Lower Rappahannock River
Corrotoman River
Middle Rappahannock River
Upper Rappahannock River
Piankatank River
Western Lower Chesapeake Bay
Eastern Lower Chesapeake Bay
MobjackBay
Lower York River
Middle York River
Lower Mattaponi River
Upper Mattaponi River
Lower Pumunkey River
Upper Pumunkey River
Mouth of the James River
Mouth of the Elizabeth River
Lafayette River
Middle Elizabeth River
Eastern Branch of the Elizabeth River
South Branch of the Elizabeth River
Western Branch of the Elizabeth River
Lower James River
Middle James River
Chickahominy River
Upper James River
Appomattox River
Lynhaven & Back Bays
Mouth of the Chesapeake Bav
Tier I
Goal
902
2,078
0
0
19,899
988
4,265
6,405
133
835
4,776
2,471
541
0
0
1,994
1,265
12,081
13,744
54
1,401
0
0
0
0
40
0
0
0
0
0
0
0
0
225
0
0
175
0
Hern
Target
3,197
14,016
1,406
581
38,874
26,069
7,188
7,794
697
588
15,021
19,770
1,819
1,653
3,190
5,668
3,936
28,510
22,978
8,387
5,088
445
996
598
2,187
1,616
0
0
0
0
0
0
17,613
6,476
3,506
10,400
1,307
3,304
697
TierHI
Target
5,068
17,969
1,515
749
58,024
45,807
15,199
17,838
1,389
914
18,691
30,035
2,612
2,511
4,515
7,789
5,130
32,575
30,554
12,666
7,139
613
1,352
860
2,654
2,266
0
0
0
0
0
0
29,138
10,954
4,505
12,842
1,604
3,961
1.053
TOTAL Chesapeake Bay
113,720 408,689
618,773
-------
Chapter VIII - Chesapeake Bay SAV Distribution Restoration Goals and Targets 125
TABLE VIII-2. Chesapeake Bay SAV distribution restoration Tier I goal,
Chesapeake Bay Program segment in hectares.
CBP
Seement
CB1TF
NORTF
ELKOH
C&DOH
BOHOH
SASOH
CB2OH
CB3MH
BSHOH
GUNOH
MIDOH
BACOH
PATMH
CHSMH
CHSOH
CHSTF
MAGMH
SEVMH
SOUMH
RHDMH
WSTMH
EASMH
CB4MH
CHOMH1
CHOMH2
CHOOH
CHOTF
LCHMH
PAXMH
PAXOH
PAXTF
WBRTF
HNGMH
FSBMH
NANMH
NANOH
NANTF
WICMH
MANMH
Chesapeake Bay Program
Seement Name
Northern Chesapeake Bay
Northeast River
Elk River
Chesapeake & Delaware Canal
Bohemia River
Sassafras River
Upper Chesapeake Bay
Upper Central Chesapeake Bay
Bush River
Gunpowder River
Middle River
Back River
Patapsco River
Lower Chester River
Middle Chester River
Upper Chester River
Magothy River
Severn River
South River
Rhode River
West River
Eastern Bay
Middle Central Chesapeake Bay
Mouth of the Choptank River
Lower Choptank River
Middle Choptank River
Upper Choptank River
Little Choptank River
Lower Patuxent River
Middle Patuxent River
Upper Patuxent River
Western Branch of the Patuxent River
Honga River
Fishing Bay
Lower Nanticoke River
Middle Nanticoke River
Upper Nanticoke River
Wicomico River
Manokin River
Tier I
Goal
3,112
8
447
1
17
165
267
698
23
350
348
0
50
1,518
0
0
237
188
21
6
47
2,479
152
2,990
187
0
0
616
144
1
6
0
1,599
13
0
0
0
0
276
and tiers II and III targets by
Hern
Target
5,550
378
1,127
50
458
1,058
2,201
1,903
837
1,172
579
527
803
2,829
673
310
607
545
601
252
442
5,298
3,217
5,248
1,526
345
0
3,279
2,086
581
204
13
4,308
2,808
1,290
616
242
1,393
2,564
Tierm
Target
8,256
1,110
2,035
69
771
1,497
3,728
2,230
1,864
3,019
1,004
1,158
1,434
4,658
935
352
881
853
926
366
618
8,421
3,764
7,456
2,518
520
0
4,775
3,573
839
286
13
6,265
5,517
3,122
832
359
2,584
3,779
continued
-------
126 SAV TECHNICAL SYNTHESIS I
TABLE VIII-2. Chesapeake Bay SAV distribution restoration Tier I goal, and tiers
Chesapeake Bay Program segment in hectares (continued)
CBP Chesapeake Bay Program
Segment Segment Name
BIGMH Big Annemessex River
POCMH Lower Pocomoke River
POCOH Middle Pocomoke River
POCTF Upper Pocomoke River
TANMH Tangier Sound
POTMH Lower Potomac River
POTOH Middle Potomac River
POTTF Upper Potomac River
MATTF Mattawoman Creek
PISTF Piscataway Creek
CB5MH Lower Central Chesapeake Bay
RPPMH Lower Rappahatmock River
CRRMH Corrotoman River
RPPOH Middle Rappahannock River
RPPTF Upper Rappahannock River
PIAMH Piankatank River
CB6PH Western Lower Chesapeake Bay
CB7PH Eastern Lower Chesapeake Bay
MOBPH MobjackBay
YRKMH Lower York River
YRKPH Middle York River
MPNOH Lower Mattaponi River
MPNTF Upper Mattaponi River
PMKOH Lower Pumunkey River
PMKTF Upper Pumunkey River
JMSPH Mouth of the James River
ELIPH Mouth of the Elizabeth River
LAFMH Lafeyette River
ELMH Middle Elizabeth River
EBEMH Eastern Branch of the Elizabeth River
SBEMH South Branch of the Elizabeth River
WBEMH Western Branch of the Elizabeth River
JMSMH Lower James River
JMSOH Middle James River
CHKOH Chickahominy River
JMSTF Upper James River
APPTF Appomattox River
LYNPH Lynhaven& Back Bays
CB8PH Mouth of the Chesapeake Bav
Tier I
Goal
365
841
0
0
8,053
400
1,726
2,592
54
338
1,933
1,000
219
0
0
807
512
4,889
5,562
22
567
0
0
0
0
16
0
0
0
0
0
0
0
0
91
0
0
71
0
TierH
Tarcet
1,294
5,672
569
235
15,732
10,550
2,909
3,154
282
238
6,079
8,001
736
669
1,291
2,294
1,593
11,538
9,299
3,394
2,059
180
403
242
885
654
0
0
0
0
0
0
7,128
2,621
1,419
4,209
529
1,337
282
II and III targets by
Tier in
Target
2,051
7,272
613
303
23,482
18,538
6,151
7,219
562
370
7,564
12,155
1,057
1,016
1,827
3,152
2,076
13,183
12,365
5,126
2,889
248
547
348
1,074
917
0
0
0
0
0
0
11,792
4,433
1,823
5,197
649
1,603
426
TOTAL Chesapeake Bay
46,022
165,394 250,414
-------
SAV Distribution
Restoration Target
Figure VIII-2. Tier II SAV Distribution Restoration Target.
-------
•|Bog UOHBJ01S9H
H-IIIA 3Jn6y
-------
SAV Distribution
Restoration Goal
Figure VIII-3. Tier III SAV Distribution Restoration Goal.
-------
'f-IIIA ajnBy
HW3HS
HW383
it '
HdNAlJ*Jg^H»\ldVn
-------
CHAPTER |X
Comparing Nearshore and Midchannel
Water Quality Conditions
I
n the Chesapeake Bay region, most governmental
I agency tidal water quality monitoring programs sam-
ple only midchannel locations to reduce sampling time
and costs. This provides crucial water quality informa-
tion for determining status and long-term trends of the
Chesapeake Bay mainstem, its tidal tributaries and
embayments. However, does midchannel monitoring
provide adequate information to characterize the sta-
tus of biologically important nearshore areas? If the
nearshore values are found to be statistically similar to
the midchannel values, then resource managers could
make better informed decisions about nearshore areas
without requiring additional water quality monitoring
locations. Conversely, differing nearshore and mid-
channel conditions might require revision of existing
monitoring programs or the initiation of new ones.
Several studies have addressed the nearshore vs.
midchannel sampling issue in Chesapeake Bay (Table
IX-1; Stevenson et al. 1991; Batiuk et al. 1992; Chesa-
peake Bay Program 1993; Ruffin 1995; Bergstrom,
unpublished data; Parham 1996). While most studies
indicate that midchannel data can be used to describe
nearshore conditions, several suggest the opposite.
There is no doubt that demonstrable differences in
water quality can occur between nearshore and mid-
channel stations over varying temporal and spatial
scales, especially when submerged aquatic vegetation
is present (Ward et al. 1984; Moore et al. 1995; Moore
1996). Other possible causes of variability between
nearshore and midchannel environments include
localized resuspension of sediments, algal patchiness,
point source effluents or sediment chemistry variabil-
ity (Goldsborough and Kemp 1988; Moore 1996).
The findings presented in this chapter result from a
comprehensive analysis of directly accessible
nearshore and midchannel data in the Chesapeake
Bay collected since 1983, to determine whether mid-
channel water quality monitoring data are applicable
for characterizing nearshore environments. The full
report describing these analyses has been published by
Karrh (1999). Data for this study were incorporated
from all over Chesapeake Bay and its tidal tributaries,
including the upper Chesapeake Bay region; the Mid-
dle, Magothy, Rhode, Chester, Choptank, Patuxent,
Potomac, Rappahanock, Poquoson, York and James
rivers; and Mobjack Bay. Data were obtained from
state monitoring efforts, academic researchers and cit-
izen monitors. Most data used in the analyses came
from unvegetated areas.
METHODS
The parameters analyzed included Secchi depth, dis-
solved inorganic nitrogen, dissolved inorganic phos-
phorous, chlorophyll a, total suspended solids and
salinity. These are the parameters most relevant to the
survival of submerged aquatic vegetation (Batiuk et al.
1992; Dennison et al. 1993). Salinity was also included
as a diagnostic parameter in assessing the comparabil-
ity of sites. The study period was limited to the SAV
growing season (April to October in tidal fresh, oligo-
haline and mesohaline areas and March to May and
September to November in polyhaline areas). In some
datasets, it was necessary to sum the nitrite, nitrate
and ammonia fractions to obtain dissolved inorganic
nitrogen and to convert data to similar units in all
datasets. Since many of the citizen monitoring datasets
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 131
-------
132 SAV TECHNICAL SYNTHESIS II
TABLE IX-1. Summary of previous nearshore/midchannel comparisons. Area indicates tidal tributary or
mainstem Chesapeake Bay study area. Source indicates publication that the results appear in. Under Kd
(light attenuation coefficient), DIN (dissolved inorganic nitrogen), DIP (dissolved inorganic phosphorous),
TSS (total suspended solids) and chlorophyll a, the results are shown for each study, based upon whether
the midchannel data can be used to characterize the nearshore environment. Yes = midchannel data can
be applied to the nearshore areas, No = cannot be used and ND = no data.
Area
Source
DIN DIP TSS Chlorophyll a
Upper Bay
Chester
Choptank
Choptank
Choptank
Patuxent
Patuxent
Potomac
York
Mainstem
Bay
Batiukefa/. 1992
Ruffin 1996
Batiuketal. 1992
Stevenson et al. 1991
Parham 1996
Bergstrom unpublished
Parham 1996
Batiukefa/. 1992
Batiuke/a/. 1992
Chesapeake Bay Program
1993
Yes
No
Yes
No*
Yes
ND
Yes
Yes
Yes
No
Yes
ND
Yes
No*
ND
Yes
1SID
Yes
Yes
Yes
Yes
ND
Yes
No*
ND
Yes
ND
Yes
Yes
Yes
Yes
ND
Yes
No*
ND
ND
ND
Yes
Yes
Yes
Yes
ND
Yes
No*
ND
ND
ND
Yes
Yes
Yes
* The authors of this study concluded that the wide variation in the data was masking any
potential significant statistical result, therefore they based their conclusions on their correlational
analyses.
report Secchi depth and not Kd, Kd values given in
some datasets were converted to Secchi depth using
the appropriate conversion factor (see Chapter III;
Batiuk et al. 1992).
Data Sources
Data for this study were obtained from many sources.
Only datasets with two or more years were used for the
analysis. Citizen monitoring data were from the
Alliance for the Chesapeake Bay, the Magothy River
Association and the Anne Arundel County Volunteer
Monitoring Program. Nearshore water quality data
used in this analysis were obtained from George
Mason University, the University of Maryland, the
Virginia Institute of Marine Science, the Smithsonian
Environmental Research Center and Harford Com-
munity College. Midchannel water quality data were
synthesized from monitoring programs run by the
Maryland Department of the Environment, the Mary-
land Department of Natural Resources, the Virginia
Department of Environmental Quality and the
U.S. Geological Survey. All data have been placed
into SAS datasets for analysis and are directly accessi-
ble through the Chesapeake Bay Program web site at
www.chesapeakebay.net.
Station Selection
The stations used in the comparisons were selected
through the use of Arc View® desktop GIS software to
create maps showing the positions of the stations
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 133
(Figure IX-1). As most of the nearshore stations were
established for other purposes than a nearshore/mid-
channel comparison, stations were picked for compar-
ison based solely on their proximity to one another,
and to maximize the number of possible comparisons.
Stations compared were located less than 10 kilome-
ters apart, with most paired stations less than five kilo-
meters apart. The spatial relationship of stations to
one another was considered, so that a nearshore sta-
tion located far up a subtributary was not paired with
a midchannel station in the tributary's mainstem. In
this way, the stations did not differ dramatically in
chemical or physical nature. The distances between
paired stations were determined, allowing conclusions
to be drawn on how far away from a given midchannel
station the nearshore water quality conditions can still
be characterized using midchannel data.
Statistical Analysis
Individual nearshore/midchannel data were analyzed
using the Wilcoxon paired-sample test (Wilcoxon
1945; Zar 1984). This test examines differences
between two samples of the same observation (i.e., one
nearshore vs. one midchannel station sampled on the
same day). The actual daily values were used, not a
median. If all of the samples from the two stations
being compared had approximately the same number
and magnitude of positive and negative differences,
then the stations were considered similar in respect to
the parameter of interest. However, if one station had
a consistently higher or lower value than the other,
then the stations were considered significantly differ-
ent with respect to the parameter of interest.
The Wilcoxon paired-sample test is a nonparametric
analog to the paired-sample t-test, and is more appro-
priate to water quality data where the data cannot be
assumed to be normally distributed. The Wilcoxon
paired-sample test is 95 percent as powerful in detect-
ing differences between two sets of data as the t-test.
Significance was evaluated at an level of .05. The tests
were performed using a SAS program. For the pur-
poses of this report, the term, "comparison" refers to
a station A vs. station B statistical analysis. Figures IX-
2 and IX-3 show example box and scatter plots for the
York River, along with the results of the statistical
analyses. A complete set of similar figures for all the
nearshore/midchannel paired station comparisons are
published in Karrh (1999).
In order to perform the Wilcoxon test, the data from
two stations must have paired observations. Since
many of the stations were sampled on different dates,
the data were forced to match by date. A 10-day sam-
pling difference was used as the limit to keep temporal
differences between stations to a minimum while max-
imizing the number of paired stations for the compar-
ison. Most of the temporal differences were one to five
days.
Ideally, the stations compared would have data col-
lected within hours of one another. However, the data
used in this study were obtained from a variety of
sources, each with different sampling schedules and
protocols. In order to have sufficient observations to
perform some of the comparisons, it was necessary to
be fairly pragmatic about temporal differences. The
analyses were conducted using all available data for all
years. It has been argued that the data should be ana-
lyzed by year to account for interannual water quality
variability. However, the goal of this study was to
determine if midchannel data are applicable to
nearshore conditions overall. For example, if a six-year
dataset was analyzed by year for a parameter and there
were three significant and three nonsignificant results,
are these stations comparable or are they different?
RESULTS AND DISCUSSION
The results of this comprehensive study show that appli-
cability of midchannel data to nearshore environments
is very site-specific. There are wide variations in the
results within tributaries and between comparisons
using one midchannel station vs. multiple nearshore sta-
tions. Karrh (1999) describes the site-specific nature of
the results in more depth. Possible causes of this vari-
ability include localized resuspension of sediments,
algal patchiness, point source effluents or sediment
chemistry variability. Also, differences in sampling
schedule and protocols between midchannel and
nearshore sampling programs could contribute to
observed differences. Another confounding factor may
be the presence of SAV at certain sites, as the plants can
change total suspended solids, dissolved inorganic
nitrogen, dissolved inorganic phosphorus, chlorophyll a
concentrations and light penetration locally.
Table IX-2 summarizes the results of the statistical
analyses by tributary, expressed as the percentage of the
total number of comparisons that yielded a nonsignifi-
cant result (i.e., the nearshore and midchannel stations
-------
134 SAV TECHNICAL SYNTHESIS I
Midchannel stations
Nearshore stations
FIGURE IX-1. Nearshore and Midchannel Water Quality Monitoring Stations. Chesapeake Bay and tidal tributary
water quality monitoring stations used in the nearshore vs. midchannel analyses.
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 135
4
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-------
136 SAV TECHNICAL SYNTHESIS I
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v v
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60
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MidChannel Chlorophyll a u.g/1
MidChannel TSS mg/l
FIGURE IX-3. Representative Scatter Plot. Scatter plots showing the data from Figure IX-2. The Y axes are all the
nearshore data versus the X axes which are data from the corresponding midchannel station.
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 137
TABLE IX-2. Percentage of total comparisons by area showing nearshore and midchannel conditions
were similar by the Wilcoxon rank-pair test. The number in parentheses indicates the total number of
comparisons performed in that tributary (not the number of stations). ND indicates that no data were
available for the comparison. DIN = dissolved inorganic nitrogen; DIP = dissolved inorganic phosphorus;
and TSS = total suspended solids.
Tributary
Upper Bay
Middle River
Magothy River
Chester River
Rhode River
Choptank River
Patuxent River
Potomac River
Rappahannock
Secchi Depth Salinity
35% (20)
100% (1)
54% (26)
29% (14)
0%'
67% (12)
50% (14)
44% (16)
29% (7)
DIN
ND 42% (24)
ND ND
54% (26) 56% (23)
ND ND
0% (1)
75% (12)
27% (11)
ND
ND
ND
33% (12)
66% (9)
7% (14)
ND
DIP
ND
ND
85% (23)
ND
ND
16% (12)
66% (9)
33% (12)
ND
Chlorophyll
17% (24)
ND
92% (25)
ND
0% (1)
33% (12)
ND
19% (16)
ND
a TSS
ND
ND
92% (25)
ND
ND
75% (8)
ND
36% (14)
ND
River
Mobjack Bay
York River
Poquoson River
James River
0% (1)
19% (27)
0% (2)
20% (15)
0% (1) ND
16% (25) 20% (15)
0% (2) ND
13% (8) ND
ND ND ND
33% (15) 20% (15) 7 % (50)
ND ND ND
ND ND ND
Medians almost identical.
had similar values of the parameter of interest). The
parameters are discussed individually, summarizing
the results by mainstem Chesapeake Bay region and
tidal tributary, using the following categories as
descriptors based on the percentage of similar
nearshore and midchannel comparisons: excellent
(>75 percent), good (50-75 percent) and poor
(<50 percent). Only results on a mainstem Chesa-
peake Bay region and tidal tributary-wide basis are
discussed, however, the results at a specific mid-
channel station may differ from those of the mainstem
Bay region or tributary as a whole. More specific
results are expressed in the subsequent section.
Tributary Comparisons
Secchi Depth
Middle River showed excellent similarity (100 per-
cent) between the midchannel station and the
nearshore station, though it is important to note that
there was only one nearshore station there. The
Magothy, Choptank and Patuxent rivers showed good
similarity between the midchannel and nearshore
data (54, 67 and 50 percent similarity, respectively).
However, the Upper Bay area, the Chester, Potomac,
Rappahanock, York, Poquoson and James rivers, and
Mobjack Bay showed poor similarity between the
-------
138 SAV TECHNICAL SYNTHESIS I
midchannel and nearshore conditions. For the
Rhode River, there was only one comparison, which
shows a significant difference between the nearshore
and midchannel station, but the medians and
interquartile ranges were almost identical, indicating
that the stations were very similar, even though the
statistics indicate a significant difference.
Salinity
The Magothy and Choptank rivers showed good simi-
larity between the nearshore and midchannel stations
(54 and 75 percent similarity, respectively), while the
other comparisons that had salinity data—Rhode,
Patuxent, York, Poquoson and James rivers and
Mobjack Bay—have poor similarity (27 to 0 percent).
The overall poor similarity between nearshore and
midchannel salinities indicated that many of the
nearshore stations had different water masses present
than at the corresponding midchannel station. This
may be because the nearshore stations were located
slightly up or down the salinity gradient from the mid-
channel station.
Dissolved Inorganic Nitrogen
Of the comparisons that had dissolved inorganic nitro-
gen data, the Magothy and Patuxent rivers have good
similarity between the nearshore and midchannel sta-
tions (56 and 66 percent, respectively), while the Upper
Bay area, the Choptank, Potomac and York rivers have
poor similarity (<33 percent). There were gradients in
dissolved inorganic nitrogen-high values upstream and
lower values downstream- in the Patuxent and Chop-
tank rivers, which could explain some of the differences
between nearshore and midchannel dissolved inor-
ganic nitrogen data (Bergstrom, unpublished).
Dissolved Inorganic Phosphorus
Dissolved inorganic phosphorus showed a similar pat-
tern as dissolved inorganic nitrogen. The Magothy
River had excellent similarity between the nearshore
stations and the midchannel station (85 percent) and
the Patuxent River had good similarity between the
nearshore and midchannel stations (66 percent), while
the Upper Bay area, the Choptank, Potomac and York
rivers showed poor similarity (< 33 percent). Again,
longitudinal gradients could explain these differences.
Chlorophyll a
The Magothy River had excellent similarity between
the nearshore and midchannel stations (92 percent).
The Upper Bay area, the Choptank, Potomac and
York rivers had poor similarity between the nearshore
and midchannel environments (17, 33, 19 and 20 per-
cent, respectively).
Total Suspended Solids
Again, the Magothy River had excellent similarity
between the nearshore and midchannel data (92 per-
cent) as did the Choptank River (75 percent). The
Potomac and York rivers had poor similarity between
the nearshore and midchannel stations (36 and 7 per-
cent, respectively).
Overall Comparisons
The Magothy River midchannel station (MWT6.1)
seems adequately to describe most nearshore areas in
that river for all five SAV habitat parameters. How-
ever, this is a very short river, with tightly grouped sta-
tions. The midchannel stations in the Choptank seem
adequately to describe the light penetration and total
suspended solids conditions in the nearshore environ-
ment. The Patuxent River midchannel stations seem
adequately to describe the light and nutrient condi-
tions in the nearshore areas. In the Middle River, the
state water-quality monitoring light penetration data
can be applied to the adjacent nearshore areas. The
Upper Bay area, the Chester, Potomac, Rappahanock,
York, Poquoson and James Rivers, and Mobjack Bay
have more significant differences between the
nearshore and midchannel stations than the other
areas mentioned. The section below takes a more site-
specific look into these results, by determining dis-
tances from an individual midchannel station that
characterize the nearshore environment.
Spatial Similarities
One of the objectives of this study was to determine
the distance from midchannel stations over which
water quality data can be used to assess nearshore
conditions. The distances upstream and downstream
were estimated using the furthest distance from a mid-
channel station that yielded a nonsignificant result
between the nearshore and midchannel stations for
each parameter (Table IX-3).
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 139
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140 SAV TECHNICAL SYNTHESIS II
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Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 141
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142 SAV TECHNICAL SYNTHESIS II
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Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 143
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Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 145
If there was only one nearshore station compared to a
midchannel station, the distance is expressed as a
radius. For multiple nearshore stations per compari-
son, the distances upstream and downstream of the
midchannel station are noted. Where possible, cross-
stream distances are expressed as the cardinal direc-
tions (North = N, South = S, etc.). Figures IX-4a-o
are maps showing these distances in relation to the
midchannel station.
For each parameter, estimates were made of the dis-
tance from midchannel stations that nearshore condi-
tions could be characterized using midchannel data.
Using the values given in Table IX-3, the 10th and 25th
percentile and the median distances were determined
for each parameter. The assumption was that, without
nearshore data available, the percentile (or median)
distance for each parameter will describe how far from
a midchannel station its water quality data can charac-
terize the nearshore environment. The level of risk in
using the midchannel data to characterize the
nearshore area is equal to the percentile. For example,
if the 10th percentile distance is 1 kilometer, there is a
10 percent chance that data from a midchannel station
will not adequately describe the nearshore environ-
ment one kilometer away. Conversely, there is 90 per-
cent chance that the midchannel data will describe the
nearshore condition to at least one kilometer distance
from the midchannel station. These distances, by each
parameter tested, are listed in Table IX-4.
Attainment of Habitat Requirements
Another analysis was performed to examine the rela-
tionship of nearshore and midchannel water quality
data to the SAV habitat requirements. This was done
because even though nearshore and midchannel data
may not be statistically similar, they both may yield the
same conclusion relative to the SAV habitat require-
ments. Nearshore and midchannel paired data were
compared individually to the 1992 SAV habitat
requirements for 1-meter restoration (Batiuk et al.
1992) to include as many tidal tributaries as possible,
since each component (light penetration, dissolved
nutrients, chlorophyll a and total suspended solids)
could be considered separately. Many of the nearshore
stations used in this study do not have a complete suite
of parameters, and the new minimum light require-
ments described in this report require light penetra-
tion, dissolved nutrients and total suspended solids to
deliver an integrated answer to whether or not they
meet the minimum habitat requirement. Therefore,
the new minimum light requirement was inappropriate
to use for this analysis. The nearshore and midchannel
results were then compared to see if they agreed
(i.e., both either met or failed to meet the habitat
requirement) or disagreed, i.e., one met and one
failed. (Table IX-5).
Seech/ Depth
For most areas of the Bay, the agreement between the
nearshore and midchannel stations was good to excel-
lent ( 50 percent agreement), with the exception of the
Rappahanock and Poquoson rivers and Mobjack Bay
(41, 24, and 13 percent, respectively). With these
exceptions, it is possible to consider that the nearshore
environments will reflect the results of applying the
habitat requirements to the midchannel data.
Dissolved Inorganic Nutrients
In terms of the limited number of tidal tributaries that
have nearshore nutrient data, most have fairly good
agreement between nearshore and midchannel attain-
ment of the SAV nutrient habitat requirements (> 55
percent). The Patuxent River is the exception, with
less than 32 percent agreement. With this exception,
SAV habitat requirement analysis of the dissolved
inorganic nutrient conditions in the midchannel are
applicable to the nearshore environment.
Chlorophyll a
In terms of the areas for which nearshore chlorophyll
a data are available, the agreement is fairly good
between the nearshore and midchannel comparison to
the habitat requirement (> 64 percent). The excep-
tions are the Upper Bay area and the York River (47
and 44 percent, respectively).
Total Suspended Solids
Of the four areas that have total suspended solids
data—the Magothy, Choptank, Potomac and York
rivers—the Choptank River (68 percent) and the
Potomac River (75 percent) had good agreement,
while the Magothy (31 percent) and the York (40 per-
cent) rivers had poor agreement. Therefore, it is
appropriate to use the midchannel data to determine
if an area meets the SAV habitat requirements for
total suspended solids for the Choptank and Potomac
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146 SAV TECHNICAL SYNTHESIS I
TABLE IX-4. Percentile distances from midchannel water quality monitoring stations from which it is
possible to characterize the nearshore environment.
Parameter
Secchi Depth
Salinity
Dissolved Inorganic Nitrogen
Dissolved Inorganic Phosphorous
Chlorophyll a
Total Suspended Solids
Percentile distance
10th
1.0
1.6
1.1
1.2
2.4
1.7
(kilometers)
25th
2.1
2.2
1.8
2.1
3.6
2.7
Median
3.9
4.1
3.0
3.4
4.0
5.5
TABLE IX-5. Comparison of the 1992 SAV habitat requirements attainment between nearshore and
midchannel water quality monitoring data. Percent shown is the number of times both the nearshore
and midchannel stations meet or fail the respective habitat requirement. The number in parentheses is
the total number of paired observations. DIN = dissolved inorganic nitrogen; DIP = dissolved inorganic
phosphorus; and TSS = total suspended solids.
Area
Upper Bay
Middle River
Magothy River
Chester River
Rhode River
Choptank River
Patuxent River
Potomac River
Rappahanock
River
Mobjack Bay
York River
Poquoson River
James River
All tributaries
Secchi Depth
63% (294)
98% (42)
69% (835)
72% (251)
70% (47)
76% (369)
75% (970)
96% (1,400)
41% (140)
13% (16)
60% (1,398)
24% (79)
78% (651)
74% (6,492)
DIN
87% (182)
ND
62% (583)
ND
ND
77% (316)
31% (610)
64% (949)
ND
ND
63% (1,294)
ND
ND
61% (3,934)
DIP
ND
ND
65% (658)
ND
ND
70% (357)
32% (584)
81% (1,402)
ND
ND
55% (1,303)
ND
ND
63% (4,304)
Chlorophyll a
47% (258)
ND
64% (571)
ND
77% (47)
75% (365)
ND
90% (1,438)
ND
ND
44% (1,323)
ND
ND
67% (4,002)
TSS
ND
ND
31% (444)
ND
ND
68% (317)
ND
7556 (1,130)
ND
ND
40% (1,159)
ND
ND
54% (3,050)
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 147
Secchi depth Areas
Dissolved Inorganic Nitrogen Areas
Chlorophyll a Areas
Secchi depth Areas
FIGURE ix-4b. Map of Middle River, showing approximate
distance from a midchannel water quality monitoring station
(shown as a v), where it is possible to use midchannel water
quality data to characterize the nearshore environment. Only
Secchi depth data were available for comparison in this region
of Chesapeake Bay. Shaded area in the baywide map shows
area of enlargement.
FIGURE IX-4a. Maps of Upper Bay Region,
showing approximate distance from a mid-
channel water quality monitoring station
(shown as a v), where it is possible to use
midchannel water quality data to characterize
the nearshore environment. Only Secchi depth,
dissolved inorganic nitrogen and chlorophyll a
data were available for comparison in this
region of Chesapeake Bay. Shaded area in
the baywide map shows area of enlargement.
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148 SAV TECHNICAL SYNTHESIS II
Secchi depth Areas
Salinity Areas
Dissolved Inorganic Nitrogen Areas Dissolved Inorganic Phosphorus Areas
Chlorophyll a Areas
Kilometers
Total Suspended Solids Areas
FIGURE ix-4c. Maps of Magothy River, showing approximate distance from a midchannel water quality monitoring
station (shown as a v), where it is possible to use midchannel water quality data to characterize the nearshore
environment. All parameters of interest had data available for comparison in this region of Chesapeake Bay.
Shaded area in the baywide map shows area of enlargement.
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 149
Secchi depth Areas
Salinity Areas
Chlorophyll a Areas
202
Kilometers
Secchi depth Areas
Dissolved Inorganic Nitrogen Areas
Dissolved Inorganic Phosphorus Areas
4 0 4 8\_.
FIGURE IX-4d. Maps of Rhode River, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible
to use midchannel water quality data to characterize
the nearshore environment. Secchi depth, salinity, and
chlorophyll a data were available for comparison in this
region of Chesapeake Bay. Shaded area in the baywide
map shows area of enlargement.
FIGURE IX-4e. Maps of Upper Patuxent River, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible
to use midchannel water quality data to characterize the
nearshore environment. Only Secchi depth, dissolved
inorganic nitrogen, an dissolved inorganic phosphorus
data were available for comparison in this region of
Chesapeake Bay. Shaded area in the baywide map
shows area of enlargement.
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150 SAV TECHNICAL SYNTHESIS II
Secchi depth Areas
Salinity Areas
Dissolved Inorganic Nitrogen Areas
Dissolved Inorganic Phosphorus Areas
Kilometers
Chlorophyll a Areas
Total Suspended Solids Areas
FIGURE IX-4f. Maps of Choptank River, showing approximate distance from a midchannel water quality monitoring
station (shown as a v), where it is possible to use midchannel water quality data to characterize the nearshore
environment. All parameters of interest had data available for comparison in this region of Chesapeake Bay.
Shaded area in the baywide map shows area of enlargement.
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 151
Secchi depth Areas
Salinity Areas
Dissolved Inorganic Nitrogen Areas
Dissolved Inorganic Phosphorus Areas
Kj|ometers
Secchi depth Areas
FIGURE IX-4h. Maps of Upper Rappahannock River,
showing approximate distance from a midchannel water
quality monitoring station (shown as a v), where it is
possible to use midchannel water quality data to
characterize the nearshore environment. Only Secchi
depth data were available for comparison in this region
of Chesapeake Bay. Shaded area in the baywide map
shows area of enlargement.
FIGURE IX-4g. Maps of Lower Patuxent River, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible to
use midchannel water quality data to characterize
the nearshore environment. Secchi depth, salinity,
dissolved inorganic nitrogen, and dissolved inorganic
phosphorus data were available for comparison in this
region of Chesapeake Bay. Shaded area in the baywide
map shows area of enlargement.
-------
152 SAV TECHNICAL SYNTHESIS I
Secchi depth Areas
Dissolved Inorganic
Nitrogen Areas
Dissolved Inorganic
Phosphorus Areas
Chlorophyll a Areas
Total Suspended
Solids Areas
5 0 5 10
Kilometers
FIGURE IX-4L Maps of Upper Potomac, showing approximate distance from a midchannel water quality monitoring
station (shown as a v), where it is possible to use midchannel water quality data to characterize the nearshore
environment. All parameters had data available for comparison in this region of Chesapeake Bay. Shaded area in
the baywide map shows area of enlargement.
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 153
Secchi depth Areas
FIGURE ix-4j. Maps of Lower Rappahannock River,
showing approximate distance from a midchannel water
quality monitoring station (shown as a v), where it is
possible to use midchannel water quality data to
characterize the nearshore environment. Only Secchi
depth data were available for comparison in this region
of Chesapeake Bay. Shaded area in the baywide map
shows area of enlargement.
Secchi depth Area
Salinity Area
FIGURE lX-4k. Maps of Mobjack Bay, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible
to use midchannel water quality data to characterize the
nearshore environment. Only Secchi depth and salinty
data were available for comparison in this region of
Chesapeake Bay. Shaded area in the baywide map
shows area of enlargement.
-------
154 SAV TECHNICAL SYNTHESIS II
Secchi depth Areas
Salinity Areas
Dissolved Inorganic Nitrogen Areas
Dissolved Inorganic Phosphorus Areas
Chlorophyll a Areas
Total Suspended Solids Areas
20 0 20 40
Kilometers
FIGURE IX-4I. Maps of York River, showing approximate distance from a midchannel water quality monitoring station
(shown as a v), where it is possible to use midchannel water quality data to characterize the nearshore environment.
All parameters had data available for comparison in this region of Chesapeake Bay. Shaded area in the baywide
map shows area of enlargement.
-------
Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 155
Secchi depth Area
Salinity Area
Secchi depth Areas
FIGURE IX-4n. Map of Upper James River, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible
to use midchannel water quality data to characterize the
nearshore environment. Only Secchi depth data were
available for comparison in this region of Chesapeake
Bay. Shaded area in the baywide map shows area of
enlargement.
FIGURE IX-4m. Maps of Poquoson River, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible to
use midchannel water quality data to characterize the
nearshore environment. Only Secchi depth and salinity
data were available for comparison in this region of
Chesapeake Bay. Shaded area in the baywide map
shows area of enlargement.
-------
156 SAV TECHNICAL SYNTHESIS I
Secchi, depth Area
6 ° 6 12 Kilometers^
FIGURE IX-4o. Map of Lower James River, showing
approximate distance from a midchannel water quality
monitoring station (shown as a v), where it is possible
to use midchannel water quality data to characterize the
nearshore environment. Only Secchi depth and salinty
data were available for comparison in this region of
Chesapeake Bay. Shaded area in the baywide map
shows area of enlargement.
FINDINGS
The summarized results of the paired station statistical
analysis comparisons (Table IX-2) frequently exhibit a
different pattern than the attainment data (Table IX-
5). For Secchi depth, the Upper Bay area, the Chester,
Rhode, Choptank, Potomac, York and James rivers
show poor statistical similarity (< 50 percent of the
statistical analyses had a nonsignificant result), while
the SAV habitat requirement attainment agreement
for these same areas is much higher (> 60 percent).
For the nutrient data, the Upper Bay area, and the
Rhode, Choptank, Potomac and York rivers show sim-
ilar discrepancies, with statistical analyses showing low
similarity (< 50 percent), while SAV habitat require-
ment attainment analyses show much higher agree-
ment (> 50 percent). For the Patuxent River, this is
reversed. The statistics show a fairly high degree of
similarity (66 percent), but a low degree of SAV habi-
tat requirement attainment agreement (< 32 percent).
Chlorophyll a also has a discrepancy between the sta-
tistical and attainment analyses for the Upper Bay
area and the Rhode, Choptank, Potomac and York
rivers. The Magothy, Potomac and York rivers exhibit
different conclusions from the statistical and attain-
ment analyses for total suspended solids. The reason
for these discrepancies is that although the two sta-
tions being compared may not be statistically similar, if
both have a majority of their values for the parameter
of interest firmly on the "met" or "failed" side of the
habitat requirement, then the attainment results will
be consistent.
CONCLUSIONS
There are wide variations in the results of the statisti-
cal comparisons between nearshore and midchannel
data within the tidal tributaries and mainstem Chesa-
peake Bay. Decisions to use midchannel data to char-
acterize nearshore conditions should be done on a
site-by-site basis.
It is possible to determine a distance from a specific
midchannel station for which it is appropriate to use
the midchannel distance to characterize the nearshore
environment. Measurements between nearshore and
midchannel stations were comparable 90 percent of
the time, between 1 and 2 kilometers from the mid-
channel station, though this radius differs on a site-by-
site basis.
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Chapter IX - Comparing Nearshore and Midchannel Water Quality Conditions 157
With the exceptions noted above, the midchannel and
nearshore areas usually provide similar attainment/ non-
attainment of the 1992 SAV habitat requirements. It is
therefore possible to use the midchannel data to deter-
mine SAV habitat conditions for a majority of the tidal
tributaries and regions of the mainstem Bay analyzed in
this study. However, the exceptions in the text above
must be considered on a tributary-by-tributary basis.
-------
CHAPTER X
Future Needs for Continued
Management Application
This second technical synthesis, which brings togeth-
er another decade of monitoring and research find-
ings, advances the ability of managers and scientists to
assess and diagnose the health of Chesapeake Bay
SAV and its supporting habitats. At the same time, the
areas requiring further research, assessment and
understanding are also brought into sharper focus.
Organized by major chapter heading, the following
high-priority management needs require that research
efforts be directed toward them in the coming years, to
set the stage for the next scientific and management
synthesis.
MINIMUM LIGHT REQUIREMENTS
There is a general need for better understanding of the
minimum light requirements for survival and growth
of the diverse set of SAV species that occur in a wide
variety of Chesapeake Bay tridal habitats. A coordi-
nated combination of field and laboratory studies is
needed to ensure that results will be both precise and
representative of conditions in nature. A more in-
depth understanding is needed of how SAV minimum
light requirements vary with changes in environmental
conditions. The need for different sets of minimum
light requirements for recovery/recruitment of new
SAV beds versus maintenance and protection of exist-
ing SAV beds needs to be researched and clarified.
The short-term temporal applications of the minimum
light requirements need further study to determine the
critical length of time under which SAV can recover
when faced with extremely low light levels for short
periods of time.
WATER-COLUMN CONTRIBUTION TO
ATTENUATION OF LIGHT
Continued collection of monitoring data is necessary
to track recovery (or further degradation) of the sys-
tem with respect to the optical water quality targets
defined for the various regions using the diagnostic
tool. However, it is doubtful that additional monitor-
ing data will improve the ability to derive statistical
estimates of specific-attenuation coefficients by
regression analysis. Inherent variability in the spectral
absorption and scattering properties of the optical
water quality parameters, combined with normal
uncertainty associated with sampling and laboratory
analyses, probably account for the low coefficients of
determination and statistically insignificant estimates
of some specific-attenuation coefficients. Never-
theless, some attempt to determine regionally based
estimates of the water, colored dissolved matter and
total suspended solids specific-attenuation coefficients
should be made. This is needed because of the pro-
nounced changes in the nature of particulate material
that occur from the headwaters to the mouth of major
tidal tributaries as well as the mainstem Chesapeake
Bay itself. An approach based on direct measurement
of particulate absorption spectra and optical modeling
likely will be needed to obtain regionally customized
diagnostic tools.
EPIPHYTE CONTRIBUTION TO LIGHT
ATTENUATION AT THE LEAF SURFACE
While development of the percent light at the leaf sur-
face model was supported by a large data set, there is
Chapter X - Future Needs for Continued Management Application 159
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160 SAV TECHNICAL SYNTHESIS II
real need for more research information to support
this approach. Field and laboratory studies are needed
to better describe relationships among total suspended
solids; the biomass of epiphytic algae growing on SAV
leaves and the total dry weight of epiphytic material;
and between nutrient concentrations and epiphytic
algae biomass in various habitats. Further research is
also needed to describe the dynamics of how these
relationships vary with wind, tides and sediment re-
suspension. Finally, there is a substantial need for data
to allow field assessment of grazer abundance and
potential epiphyte grazing rates. Refined application
of this model to specific field sites must be attentive to
the nutrient-epiphyte relationship that may be af-
fected by other factors, such as grazing and flushing
rates. There is a pressing need for field data on these
factors to better calibrate these very site-specific appli-
cations. Obtaining such information is complicated by
the fact that most of these herbivorous grazers are
highly mobile, with flexible and diverse food habits.
While results reported here for Chesapeake Bay are
encouraging, it remains to be seen how useful the
model might be for analyzing other aquatic ecosys-
tems. The general applicability of this approach out-
side Chesapeake Bay needs to be tested.
PHYSICAL, GEOLOGICAL AND
CHEMICAL HABITAT REQUIREMENTS
In those areas where light attenuation remains the key
factor in defining potential habitats for the recovery of
SAV populations, the plants are largely restricted to
shallow water habitats of the Bay mainstem and tidal
tributaries as well as the headwaters of feeder streams.
Unfortunately, in these same areas the highest levels
of wave energy, sediment resuspension and chemical
contaminant exposure are most likely to occur. Thus,
the aquatic environments most favorable to SAV
growth from the perspective of water clarity are not
necessarily the most conducive to SAV reestablish-
ment because of these other factors. Therefore, more
attention needs to be given to the interactions among
the secondary stress factors described in Chapter VI if
the management objective of restoring SAV to its his-
toric range within Chesapeake Bay is to be achieved.
Finally, care must be exercised in extending the infer-
ence of chemical contaminant data generated with one
species of SAV to other SAV species. Preliminary evi-
dence is beginning to show significant differences in
contaminant sensitivity among the various SAV
species of the Bay watershed.
To further define new and refine existing physical, geo-
logical and chemical habitat requirements, future
research should be focused on:
• the maximum wave exposure tolerated by canopy
and meadow formers;
• the appropriateness of including wave mixing
depth in determining the minimum depth of dis-
tribution;
• possible restrictions imposed by sediment grain
size on SAV growth and distribution;
• the maximum amount of sediment organic mat-
ter tolerated by different SAV species;
• potential nitrogen toxicity in SAV beds;
• sediment sulfide maxima for the survival of SAV
exposed to different light levels; and
• the synergistic effect of multiple chemical con-
taminants on SAV.
SAV DISTRIBUTION RESTORATION GOALS
There is a need to complete work that is already under
way to more fully examine the effects of high wave
action on limiting SAV survival and growth within the
Chesapeake Bay's shallow water habitats. The results
of this research should then be applied to the pub-
lished Tier II and Tier III distribution restoration tar-
gets for making adjustments to any areas considered
unlikely to support SAV survival and growth due to
high wave action.
-------
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-------
APPENDIX A
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-------
Appendix A - Light Requirements for Chesapeake Bay and Other SAV Species 185
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Appendix A - Light Requirements for Chesapeake Bay and Other SAV Species 187
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APPENDIX
The Role of Chemical
Contaminants as
Stress Factors
Affecting SAV
-------
190 SAV TECHNICAL SYNTHESIS II
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Appendix B - The Role of Chemical Contaminants as Stress Factors Affecting SAV 191
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192 SAV TECHNICAL SYNTHESIS II
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Appendix B - The Role of Chemical Contaminants as Stress Factors Affecting SAV 193
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APPENDIX
SAS Code Used
to Calculate PLL
from Kd, TSS, DIN
and DIP
-------
196 SAV TECHNICAL SYNTHESIS II
SAS Code Used to Calculate PLL from Kd, TSS, DIN and DIP
"calculate PLL;
*Z IS RESTORATION DEPTH IN METERS, vary among 1, 0.5, 0.25, & 0;
*halfgtr is half the greater tropic or diurnal tidal range in meters,
see listing in other table;
Z =1 + halfgtr;
OD = KD*Z;
*CALC BEM;
IFOD NE.THEN DO;
IF OD < 5.8 THEN BEM = 2.2 - (0.251*(OD**1.23));
ELSE BEM = 0.01;
END;
IF din ne . and din<(dip*7.2) then nutr =(din*71.4);
IF dip ne . and din>=(dip*7.2) then nutr = (dip*515.9);
KNOD = -2.32 * (1 - 0.031*(OD**1.42));
EPBIOSAV = BEM / (1 + (208*(nutr**KNOD)));
MGCHL = (EPBIOSAV * 5);
EPDWSAV = (0.832 * MGCHL) + (0.107 * TSS);
*NExt line had to be edited to avoid Div. by 0 error;
if epdwsav > 0 then CHLDW = MGCHL / EPDWSAV;
CHLCMSQ = MGCHL/3.7;
KEXT = -0.07 - (0.322 * CHLDW**(-0.88));
PCT_REDU = EXP(KEXT * CHLCMSQ);
PLL = EXP((-Z * KD) + (KEXT * chlCMSQ));
*»need the if statement for comparing pll to plw, otherwise can use second equation;
if (din ne . and dip ne . and tss ne .) then PLW = EXP(-Z*KD);
*PLw = EXP(-Z*KD);
* END OF PLL. FRAG;
-------
APPENDIX Q
SAV Depth, Area and
Water Quality Data Used
and Details of Statistical
Analysis Performed
-------
198 SAV TECHNICAL SYNTHESIS II
SAV AREA BY DEPTH AND
WEIGHTED MEAN DEPTH
SAV area by depth data were examined to see if they
were correlated with water quality data. This was done
by generating depth contours at 0.5, 1 and 2 meters
MLLW and overlaying SAV polygons on them. The
SAV area within four depth ranges was calculated for
each Chesapeake Bay Program segment and year, and
the areas were converted to percentages by dividing
each by the total area in that segment and multiplying
by 100. The depth ranges, area variables and percent
variables for each are listed in Table D-l.
These areas were also used to calculate a weighted
mean SAV depth for each segment and year
(SAVDEP). This was done with the usual formula for
weighted mean, multiplying the area in each depth
range by the midpoint of each range, summing them
and dividing their sum by the total area:
SAVDEP = [(AREA05)*0.25 + (AREA1)*0.75
+ (AREA2)*1.5 + (AREAGT2)*2.5] /
(Total SAV area)
Since the Area > 2 category has no upper bound, 2.5
meters was chosen as the midpoint, assuming that very
little of the mapped SAV was growing in water more
than 3 meters deep MLLW. This assumption was
based on ground truth observations that SAV is rarely
found below this depth and the limited ability to see
below this depth in aerial photos taken in the normally
turbid Chesapeake. This mean depth was used in
Spearman rank correlations with water quality param-
eters, along with the four percentages in different
depth categories.
SAV AREA DATA AND GROWTH CATEGORIES
SAV area by Chesapeake Bay Program segment came
from the Chesapeake Bay SAV aerial survey. The lat-
est table of hectares by segment by year was down-
loaded from the VIMS web page (http://www.
vims.edu/bio). SAV growth categories were used for
some analyses, which represented average conditions
over all years with SAV area data. For the York and
Choptank rivers, the same 'Persistent,' 'Fluctuating'
and 'None' categories were used that were used before
(Batiuk et al. 1992). These categories were based on
observations of the persistence over time of either nat-
ural or transplanted SAV near the monitoring stations.
For the Chesapeake Bay SAV Aerial Survey data, the
three different categories for SAV area by USGS quad
were applied to SAV area by CBP segment instead of
quad, using 1978-97 SAV hectares by segment by year.
The three categories were expanded to five and were
considered equivalent to the categories used in Batiuk
et al. (1992), as show in Table D-2.
Adding two more categories to the ones defined by
Hagy (unpublished data) helped separate the 'best'
and 'worst' segments from the others. These were the
Always Abundant' and 'Always None' categories
respectively. The results of this analysis for each
Chesapeake Bay Program segment are shown in
Table D-3.
WATER QUALITY DATA USED
Data used for SAV habitat requirements were from
surface samples (Layer = 'S') from selected Chesa-
peake Bay Water Quality stations in each segment.
TABLE D-1. SAV depth ranges and variable names.
Depth range
Area variable
Less than 0.5 meters deep AREA05
0.5-1 meter deep AREA1
1-2 meters deep AREA2
Greater than 2 meters deep AREAGT2
Percent variable
PCT05
PCT1
PCT2
PCTGT2
-------
Appendix D - SAV Depth, Area and Water Quality Data Used and Statistical Analysis Performed 199
TABLE D-2. Five categories used for characterizing SAV growth status by segment based on 1978-1997
aerial survey data.
New category (based on aerial
survey data)
Always Abundant (AA)
Always Some (AS)
Sometimes None (SN)
Usually None (UN)
Always None (AN)
Criteria used for category (using SAV TSI growth
SAV area by year by segment) category (based
on transplant
success)
Minimum > 200 ha Persistent
Minimum > 0 Persistent
Minimum = 0 and Median > 0 Fluctuating
Median = 0 and Maximum > 0 None
Maximum = 0 None
When there was more than one station per segment,
stations that were too far from SAV were dropped from
the analysis (Table D-5). Nearshore data collected in
the York and Choptank rivers for the first SAV Techni-
cal Synthesis (Batiuk et al. 1992) were also used. Volun-
teer monitoring data were not used because they were
only available for a few years and segments.
Data were used only from the SAV growing seasons:
April-October for tidal fresh, oligohaline and mesoha-
line regimes and March-May and September-Novem-
ber in polyhaline. Raw data from all stations used in
each segment were used for the Wilcoxon test.
Monthly means were not calculated since each month
had two samples (where sampling is twice a month)
and this would reduce the sample size. For consis-
tency, light attenuation (Kd) was calculated from Kd =
1.45/Secchi even if Kd data from light measurements
were available.
TESTING ATTAINMENT OF
HABITAT REQUIREMENTS
The attainment of SAV habitat requirements was
tested by segment or station and year with the
Wilcoxon one-sample test, using the difference
between each observation and the habitat requirement
for that salinity regime as the data for the test. A cus-
tom SAS program to perform the test was written for
this application (see Appendix C). When done by seg-
ment, data from all the stations used in that segment
were used for the test without any averaging, so the
sample size was larger if there were more stations.
The results were classified in three categories using a
two-tailed significance level (P) of 0.05:
Met: median was significantly below the
requirement
Borderline: median did not differ significantly
from requirement
Not met: median was significantly above the
requirement
This test was more sensitive to the consistency of the
differences (positive or negative) than to their magni-
tude.
Tidal range data were used to adjust the restoration
depth (Z). This number is critical to both PLW and
PLL calculations since it determines the path length
for light passing through the water, and thus how much
the light is attenuated passing through the water. For
any two sites, the one with greater tidal range will on
average have more water above the 1 meter depth con-
tour which is referenced to MLLW, the bottom eleva-
tion for the tidal range used (semi-diurnal or greater
tropic range).
-------
200 SAV TECHNICAL SYNTHESIS II
TABLE D-3. New CBP segments classified according to SAV
1997 SAV area data: MAX = maximum, MED = median, MIN
SEGMENT
APPTF
BACOH
BIGMH
BOHOH
BSHOH
C&DOH
CB1TF
CB2OH
CB3MH
CB4MH
CB5MH
CB6PH
CB7PH
CB8PH
CHKOH
CHOMH1
CHOMH2
CHOOH
CHOTF
CHSMH
CHSOH
CHSTF
CRRMH
EASMH
ELIPH
ELKOH
FSBMH
GUNOH
HNGMH
JMSMH
JMSOH
JMSPH
JMSTF
LCHMH
LYNPH
MAGMH
MANMH
MATTF
MIDOH
MOBPH
SAVHMAX
0
0
192.12
15.09
39.04
0.62
2714.04
127.49
554.83
102.57
1666.81
512.84
4597.91
4.4
89.17
2792.59
94.31
0
0
1050.3
0
0
178.46
1848.32
0
355.81
25.88
637.36
1845.44
1.05
0
75.74
0
529.39
43.2
141.27
156.74
60.65
117.37
4465.86
SAVH MED SAYH MIN GROWTH
0
0
156.98
1.67
0.26
0
2076.51
17.69
327.82
5.63
751.63
367.43
3442.21
0
0
1168.68
0
0
0
309.18
0
0
36.85
781.91
0
81.01
1.32
74.95
893.09
0
0
3.68
0
102.7
29.65
7.3
90.73
33.24
16.47
4134.99
OAN
OAN
OSN
OSN
OSN
OUN
833.98 AA
4.02 AS
22.21 AS
OSN
275.12 AA
241.92 AA
2452.12 AA
OUN
OUN
57.75 AS
OUN
OAN
OAN
32.45 AS
OAN
OAN
OSN
67.93 AS
OAN
0.87 AS
OSN
OSN
13.42 AS
OUN
OAN
OSN
OAN
18.35 AS
OSN
OSN
OSN
OSN
OSN
2736.2 AA
growth category (GROWTH)
= minimum (hectares).
SEGMENT SAVH MAX
MPNOH
MPNTF
NANMH
NANOH
NANTF
NORTF
PATMH
PAXMH
PAXOH
PAXTF
PIAMH
PISTF
PMKOH
PMKTF
POCMH
POCOH
POCTF
POTMH
POTOH
POTTF
RHDMH
RPPMH
RPPOH
RPPTF
SASOH
SBEMH
SEVMH
SOUMH
TANMH
WICMH
WSTMH
YRKMH
YRKPH
0
0
0
0
0
7.96
48.96
53.74
40.08
63.93
438.2
319.35
0
0
783.8
0
0
666.84
1501.15
1874.69
5.92
348.69
0
0
179.79
0
133.78
20.59
7330.38
0
46.65
0
339.5
using 1978-
SAVH MED SAVH MIN GROWTH
0
0
0
0
0
0.17
0
1.27
0
0
143.16
54.3
0
0
597.92
0
0
109.42
1121.46
1209.05
0
31.44
0
0
36.75
0
0
0
5094.61
0
0
0
269.68
0 AN
0 AN
0 AN
0 AN
OAN
0 SN
OUN
0 SN
OUN
0 UN
10.23 AS
0 SN
0 AN
0 AN
87.3 AS
0 AN
0 AN
43.12 AS
217.09 AA
0 SN
OUN
7.75 AS
0 AN
0 AN
6.41 AS
0 AN
OUN
0 UN
2927.4 AA
0 AN
OUN
0 AN
106.68 AS
-------
Appendix D - SAV Depth, Area and Water Quality Data Used and Statistical Analysis Performed 201
The tidal range data used was obtained from the
"benchmark" data on the NOAA home page:
http://www.opsd.nos.noaa.gov/bench.html
The station listings for Maryland and Virginia on this
web page include the number we want, MHHW eleva-
tion, which is the same as the semi-diurnal range or
greater tropic range (MHHW-MLLW) since MLLW is
zero in the NOAA benchmark data. However, there
do not appear to be any benchmark MHHW data for
the following rivers or areas:
Upper Western Shore Maryland-Bush,
Gunpowder, Middle, Back rivers
Lower Western Shore Maryland-Rhode,
West rivers and Patuxent above Solomons,
Potomac-from Colonial Beach upriver to DC
Eastern Shore-Wicomico, Pocomoke rivers
(Maryland)
VA Western shore-Rappahannock and
York rivers above their mouths
The published commercial tide tables have data for at
least one site in or near all of these rivers. However
the tide table (Reed's) lists the "spring range," not the
semi-diurnal range. The spring range in this table dif-
fers from the semi-diurnal range at the benchmark sta-
tions as follows:
1. Spring range > semi-diurnal range, south of a
line running diagonally across the Bay, from
Fishing Bay (Eastern Shore) SW to Smith Point
(just South of Potomac). Differences about 0.2-
0.3 feet.
2. Semi-diurnal range > spring range, north of this
line, differences about 0.1-0.5 feet (larger differ-
ences farther north).
To fill in the spatial gaps in benchmark data, we
adjusted spring ranges to estimate semi-diurnal
ranges. Since the relationship varies spatially, but has
a strong positive correlation (R-square for linear
regression was 78 percent), we adjusted the spring
range to approximate the semi-diurnal range as
follows: Estimated semi-diurnal range at site without
benchmark data equal spring range at that site *
(semi-diurnal range at nearest benchmark site/spring
range at benchmark site).
For example, for the Gunpowder River, closest bench-
mark is Tolchester (Eastern shore mainstem), esti-
mated semi-diurnal range = 1.4 ft * (1.74/1.4) = 1.74
ft., since spring range is the same at both sites; for
West River, using South River benchmark, estimated
semi-diurnal range = 1.0 * (1.48/1.1) = 1.35 ft. We
used this method to estimate semi-diurnal range from
spring range for one point near the middle of any seg-
ments that lacked benchmark data. If no spring range
data were available (e.g. the Bush River) the closest
point with spring range data was used. The resulting
semi-diurnal tidal ranges in feet and half tidal range in
meters are listed in Table D-4.
-------
202 SAV TECHNICAL SYNTHESIS II
TABLE D-4. Semi-diurnal tidal range
SEGMENT
APPTF
BACOH
BIGMH
BOHOH
BSHOH
CB1TF
CB2OH
CB3MH
CB4MH
CB5MH
CB6PH
CB7PH
CB8PH
CHKOH
CHOMH1
CHOMH2
CHOOH
CHOTF
CHSMH
CHSOH
CHSTF
CRRMH
EASMH
EBEMH
ELIMH
ELIPH
ELKOH
FSBMH
GUNOH
HNGMH
JMSMH
JMSOH
JMSPH
JMSTF
LAFMH
LCHMH
LYNPH
MAGMH
MANMH
MATTF
Semi-diurnal
range (ft)
3.5475
1.58
2.16
2.68
1.99579
2.37
1.74
1.72
1.6
1.41867
1.86
2.53
3.255
2.3876
1.76
2.05
2.17059
2.29118
2.15
2.65588
3.54118
1.44
1.745
3.2455
3.15
2.9591
2.68
2.36
1.84333
1.57333
2.75
2.18
2.8
2.15
2.9591
1.87733
1.68
1.49
2.16
1.86632
for 77 CBP segments,
Half range
(meters)
0.54243
0.24159
0.33028
0.40979
0.30517
0.36239
0.26606
0.263
0.24465
0.21692
0.2844
0.38685
0.49771
0.36508
0.26911
0.31346
0.33189
0.35033
0.32875
0.4061
0.54146
0.22018
0.26682
0.49625
0.48165
0.45246
0.40979
0.36086
0.28186
0.24057
0.42049
0.33333
0.42813
0.32875
0.45246
0.28705
0.25688
0.22783
0.33028
0.28537
SEGMENT
MtDOH
MOBPH
MPNOH
MPNTF
NANMH
NANOH
NANTF
NORTF
PATMH
PAXMH
PAXOH
PAXTF
PIAMH
PISTF
PMKOH
PMKTF
POCMH
POCOH
POCTF
POTMH
POTOH
POTTF
RHDMH
RPPMH
RPPOH
RPPTF
SASOH
SBEMH
SEVMH
SOUMH
TANMH
WBEMH
WBRTF
WICMH
WSTMH
YRKMH
YRKPH
calculated from
Semi-diurnal
range (feet)
1.84333
2.65
3.4428
3.9724
2.34
2.17286
2.50714
2.45667
1.62
1.51
2.32308
3.25231
1.26
2.968
2.7366
2.8248
2.555
2.64625
1.825
1.755
1.3479
2.57818
1.43
1.74
1.62
1.98
2.15
3.3
1.43
1.43
2.04
2.9591
3.3
2.42357
1.3
3.0014
2.645
NOAA data.
Half range
(meters)
0.28186
0.4052
0.52642
0.6074
0.3578
0.33224
0.38336
0.37564
0.24771
0.23089
0.35521
0.49729
0.19266
0.45382
0.41844
0.43193
0.39067
0.40463
0.27905
0.26835
0.2061
0.39422
0.21865
0.26606
0.24771
0.30275
0.32875
0.50459
0.21865
0.21865
0.31193
0.45246
0.50459
0.37058
0.19878
0.45893
0.40443
-------
Appendix D - SAV Depth, Area and Water Quality Data Used and Statistical Analysis Performed 203
TABLE D-5. Mainstem Chesapeake Bay Water Quality Monitoring Program stations used in analysis of the
SAV habitat requirements.
Segment
CB6PH
Stations used
Notes
CB1TF CB1.1, CB2.1
CB2OH CB2.2, CB3.1
CB3MH CBS .2, CB3.3W, CB3.3E
CB4MH CB4.1W, CB4.2E, CB4.3E,
CB4.4
CB5MH CB5.1, CB5.2, CB5.3, CB5.4,
CB5.4W, CB5.5
FSBMH
TANMH
POCMH
WE4PH
EE3.0
EE3.1,EE3.2
EE3.3
WE4.LWE4.
WE4.4
CB6.3
CB7PH EE3.5, CB7.1, CB7. IS,
CB7.2E
Only stations in segment, all used
Only stations, all used
Dropped CB3.3C (see below)
Dropped all stations in center of Bay (ending in
'C') and all but one of the west ('W') stations,
because they do not characterize SAV habitat
Only stations (none are very close to SAV
habitat but no other data are available)
Only station
EE3.4 dropped because it does not
characterize SAV habitat
Only station in segment
Only stations, all used
CB6.1, CB6.2 and CB6.4 dropped because
they do not characterize SAV habitat
CB7.1N, CB7.2,, CB7.3E, CB7.3, and
CB7.4N dropped because they do not
characterize SAV habitat
Note: Data from all of the Chesapeake Bay Water Quality Monitoring Program's tidal tributary monitoring
stations were used. In addition, data from segment CB8PH, mouth of Chesapeake Bay, were dropped
from analyses relating SAV growth categories or SAV area to water quality, because none of the water
quality monitoring stations in that segment characterize the small tidal creek (Little Creek) that contains the
only SAV found in that segment.
-------
APPENDIX
Spearman Rank Correlations
between Chesapeake Bay
Water Quality Monitoring
Program Data and
Measures of SAV Area
-------
206 SAV TECHNICAL SYNTHESIS II
TABLE E-1 . Tidal fresh Spearman rank correlations between water quality parameters from Chesapeake
Bay Program midchannel water quality monitoring stations over the whole growing season, and measures
of SAV area over Chesapeake Bay Program segments, adding half tidal range for PLW and PLL.
Parameter
K,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Tidal fresh (April-October)
PCTJT2
-0.0596
0.4906
136
0.10356
0.2524
124
0.14137
0.1173
124
-0.77325
0.0543
124
0.08112
0.3425
139
-0.15833
0.0627
139
0.5474*
0.0001
139
PCT_T3
-0.06005
0.4874
136
0.10757
0.2344
124
0.14702
0.1032
124
-0.18466
0.0401
124
0.0791
0.3547
139
-0.18026
0.0337
139
0.53887*
0.0001
139
SAVH
-0.06537
0.4496
136
0.11983
0.185
124
0.16066
0.0747
124
-0.17423
0.053
124
0.09939
0.2444
139
-0.21387
0.0115
139
0.54511*
0.0001
139
LAGSAVH
-0.03866
0.6699
124
0.11201
0.2397
112
0.15532
0.102
112
-0.19696
0.0374
112
0.11388
0.2024
127
-0.20039
0.0239
127
0.54664*
0.0001
127
CHGSAVH
0.07773
0.3684
136
-0.06748
0.4564
124
-0.07284
0.4214
124
0.00593
0.9479
124
-0.04165
0.6264
139
0.10017
0.2407
139
0.08264
0.3335
139
KEY
i,
P
N
r,
P
N
t,
P
N
r,
P
N
rs
P
N
r,
P
N
r,
P
N
KEY: PCT_T2 = SAVH/Tier H area* 100, PCT_T3 = SAVH/Tier m area* 100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year, CHGSAVH = change in SAV hectares from that year to
next; Ka = light attenuation; PLW = percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; P r, =
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N = sample size,
number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less SAV) except
PLL/PLW (more light, more SAV). *Statistically significant but not in the expected direction; probably spurious (no DIN
requirement).
-------
Appendix E - Spearman Rank Correlations 207
TABLE E-2. Tidal fresh Spearman rank correlations between water quality from Chesapeake Bay Program
midchannel water quality stations over the spring, and measures of SAV area over Chesapeake Bay
Program segments, adding half tidal range for PLW and PLL.
Parameter
K,,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Tidal fresh (April-June) KEY
PCT_T2 PCTJT3 SAVH LAGSAVH CHGSAVH
0.00761 0.01365 0.01317 0.02831 0.02677 r.
0.9302 0.8752 0.8795 0.7559 0.7579 P
135 135 135 123 135 N
0.03689 0.03551 0.04127 0.03922 -0.02224 r,
0.6854 0.6966 0.6504 0.6828 0.8071 P
123 123 123 111 123 N
0.07907 0.08164 0.08834 0.08791 -0.016 r,
0.3847 0.3693 0.3312 0.3589 0.8606 P
123 123 123 111 123 N
-0.04713 -0.05391 -0.0369 -0.06151 -0.01242 r,
0.6047 0.5537 0.6853 0.5213 0.8915 P
123 123 123 111 123 N
0.15644 0.1566 0.17161* 0.15875 -0.02269 r,
0.0669 0.0666 0.0442 0.0758 0.7917 P
138 138 138 126 138 N
-0.14858 -0.17211 -0.2064 -0.19851 0.07019 r.
0.082 0.0435 0.0151 0.0259 0.4133 P
138 138 138 126 138 N
0.48438* 0.47468* 0.48123* 0.48596* 0.04703 r,
0.0001 0.0001 0.0001 0.0001 0.5839 P
138 138 138 126 138 0.01365
KEY: PCT_T2 = SAVH/Tier U area*100, PCT_T3 = SAVH/Tier m area*100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; K^ = light attenuation; PLW = percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; r, =
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N = sample size,
number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less SAV) except
PLL/PLW (more light, more SAV).
•"Statistically significant but not in the expected direction; probably spurious (no DIN requirement).
-------
208 SAV TECHNICAL SYNTHESIS II
TABLE E-3. Oligohaline Spearman rank correlations between water quality from Chesapeake Bay
Program midchannel water quality stations over the whole growing season, and measures of SAV area
over Chesapeake Bay Program segments, adding half tidal range for PLW and PLL.
Parameter
Kd
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Oligohaline (April-October)
PCT_T2
-0.32228
0.0001
191
0.38378
0.0001
182
0.36968
0.0001
182
-0.51599
0.0001
182
-0.13135
0.0701
191
-0.14066
0.0523
191
0.07611
0.2954
191
PCT_T3
-0.3198
0.0001
191
0.37999
0.0001
182
0.36543
0.0001
182
-0.52088
0.0001
182
-0.12639
0.0815
191
-0.14006
0.0533
191
0.07199
0.3224
191
KEY
SAVH LAGSAVH CHGSAVH
-0.3359
0.0001
191
0.4001
0.0001
182
0.38394
0.0001
182
-0.53017
0.0001
182
-0.15859
0.0284
191
-0.12059
0.0966
191
0.1047
0.1495
191
-0.30592
0.0001
175
0.36512
0.0001
166
0.35327
0.0001
166
-0.53113
0.0001
166
-0.16549
0.0286
175
-0.14075
0.0632
175
0.08701
0.2522
175
-0.03497
0.631
191
0.03749
0.6154
182
0.03471
0.6418
182
-0.0005
0.9947
182
0.03371
0.6434
191
-0.05054
0.4875
191
-0.10422
0.1513
191
r,
P
N
r.
P
N
r,
P
N
r,
P
N
r,
P
N
r.
P
N
r,
P
N
KEY: PCTJT2 = SAVH/Tier H area * 100, PCT_T3 = SAVH/Tier IH area * 100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; Kj = light attenuation; PLW = percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; r, -
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N = sample size,
number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less SAV) except
PLL/PLW (more light, more SAV). Correlations in bold were significant and > +/- 0.5.
-------
Appendix E - Spearman Rank Correlations 209
TABLE E-4. Oligohaline Spearman rank correlations between water quality from Chesapeake Bay
Program midchannel water quality stations over the spring, and measures of SAV area over Chesapeake
Bay Program segments, adding half tidal range for PLW and PLL.
Parameter
K,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Oligohaline (April-June)
PCTJT2
-0.35803
0.0001
186
0.41006
0.0001
177
0.41306
0.0001
177
-0.46473
0.0001
177
-0.03517
0.6337
186
-0.21619
0.003
186
0.11433
0.1202
186
PCTJT3
-0.35839
0.0001
186
0.40926
0.0001
177
0.41214
0.0001
177
-0.46895
0.0001
177
-0.03523
0.6331
186
-0.2141
0.0033
186
0.11214
0.1275
186
KEY
SAVH LAGSAVH CHGSAVH
-0.36803
0.0001
186
0.42278
0.0001
177
0.42422
0.0001
177
-0.46911
0.0001
177
-0.06136
0.4054
186
-0.20218
0.0056
186
0.13042
0.076
186
-0.38156
0.0001
170
0.43821
0.0001
161
0.44153
0.0001
161
-0.47071
0.0001
161
-0.05987
0.438
170
-0.18372
0.0165
170
0.11665
0.1298
170
-0.10025
0.1734
186
0.09315
0.2175
177
0.09762
0.1961
177
-0.09005
0.2333
111
0.06745
0.3603
186
-0.00215
0.9768
186
-0.05129
0.4869
186
rs
P
N
r,
P
N
r.
P
N
r.
P
N
Tt
P
N
r,
P
N
r>
P
N
KEY: PCTJT2 = SAVH/Tier H area * 100, PCT_T3 = SAVH/Tier IH area * 100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; r, = Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N =
sample size, number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less
SAV) except PLL/PLW (more light, more SAV).
-------
210 SAV TECHNICAL SYNTHESIS II
TABLE E-5. Mesohaline Spearman rank correlations between water quality from Chesapeake Bay
Program midchannel water quality stations over the whole growing season, and measures of SAV area
over Chesapeake Bay Program segments, adding half tidal range for PLW and PLL.
Parameter
Ka
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Mesohaline (April-October)
PCT_T2
-0.54526
0.0001
329
0.51652
0.0001
326
0.50671
0.0001
326
-0.21364
0.0001
326
-0.36311
0.0001
329
-0.3637
0.0001
329
-0.1055
0.0559
329
PCT_T3
-0.54944
0.0001
329
0.52111
0.0001
326
0.51195
0.0001
326
-0.22248
0.0001
326
-0.36567
0.0001
329
-0.37274
0.0001
329
-0.10975
0.0467
329
KEY
SAVH LAGSAVH CHGSAVH
-0.57505
ft 0001
338
0.55187
0.0001
335
0.54913
0.0001
335
-0.2087
0.0001
335
-0.35225
0.0001
338
-0.41021
0.0001
338
-0.13549
0.0127
338
-0.58657
0.0001
309
0.56544
0.0001
306
0.5671
0.0001
306
-0.20609
0.0003
306
-0.33616
0.0001
309
-0.41049
0.0001
309
-0.17604
0.0019
309
-0.09317
0.0872
338
0.10446
0.0561
335
0.11718
0.032
335
-0.03407
0.5343
335
-0.00415
0.9394
338
-0.03386
0.535
338
0.00965
0.8596
338
r,
P
N
r.
P
N
r,
P
N
r.
P
N
r,
P
N
r,
P
N
r.
P
N
KEY: PCT_T2 = SAVH/Tier H area * 100, PCT_T3 = SAVH/Tier in area * 100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; Kj = light attenuation; PLW - percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; r, =
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N = sample size,
number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less SAV) except
PLL/PLW (more light, more SAV). Correlations in bold were significant and > +/- 0.5.
-------
Appendix E - Spearman Rank Correlations 211
TABLE E-6. Mesohaline Spearman rank correlations between water quality from Chesapeake Bay
Program midchannel water quality stations over the spring, and measures of SAV area over Chesapeake
Bay Program segments, adding half tidal range for PLW and PLL.
Parameter
Ka
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Mesohaline (April-June) KEY
PCTJT2 PCT_T3 SAVH LAGSAVH CHGSAVH
-0.53585 -0.53914 -0.55853 -0.58494 -0.07911 r,
0.0001 0.0001 0.0001 0.0001 0.1467 P
329 329 338 309 338 N
0.50519 0.50852 0.53784 0.56452 0.08795 r,
0.0001 0.0001 0.0001 0.0001 0.1081 P
326 326 335 306 335 N
0.49739 0.50187 0.53661 0.56939 0.09797 r,
0.0001 0.0001 0.0001 0.0001 0.0733 P
326 326 335 306 335 N
-0.22213 -0.23046 -0.2184 -0.21714 -0.04769 r,
0.0001 0.0001 0.0001 0.0001 0.3842 P
326 326 335 306 335 N
-0.27328 -0.27456 -0.26687 -0.23615 -0.01718 r,
0.0001 0.0001 0.0001 0.0001 0.753 P
329 329 338 309 338 N
-0.27689 -0.28757 -0.32356 -0.31739 -0.01924 rs
0. 0001 0.0001 0.0001 0.0001 0.7245 P
329 329 338 309 338 N
-0.11043 -0.11041 -0.12817 -0.13662 0.0587 r.
0.0453 0.0454 0.0184 0.0163 0.2819 P
329 329 338 309 338 N
KEY: PCTJT2 = SAVH/Tier n area * 100, PCTJT3 = SAVH/Tier HI area * 100, SAVH = SAV hectares for same year as
water quality data; L AGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; Ka = light attenuation; PLW = percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; r, =
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics if? < 0. OS) and N = sample size,
number of segment-year combinations.
Expected correlations: Negative for all parameters (more pollution, less SAV) except PLL/PLW (more light, more SAV).
Correlations in bold were significant and > +/- 0.5.
-------
212 SAV TECHNICAL SYNTHESIS I
TABLE E-7. Polyhaline Spearman rank correlations between water quality from Chesapeake Bay Program
midchannel water quality stations over the whole growing season, and measures of SAV area over
Chesapeake Bay Program segments, adding half tidal range for PLW and PLL.
Parameter
K,,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Polyhaline (March-May, Sept.-Nov.)
PCTJT2
-0.42764
0.0007
60
0.39999
0.0015
60
0.50034
0.0001
60
0.13456
0.3054
60
-0.03031
0.8181
60
-0.76875
0.0001
60
-0.6857
0.0001
60
PCT_T3
-0.4409
0.0004
60
0.41133
0.0011
60
0.51095
0.0001
60
0.1296
0.3237
60
-0.02187
0.8683
60
-0.77338
0.0001
60
-0.68645
0.0001
60
KEY
SAVH LAGSAVH CHGSAVH
-0.59332
0.0001
72
0.58154
0.0001
70
0.65444
0.0001
70
0.01667
0.8911
70
0.09341
0.4351
72
-0.84381
0.0001
72
-0.77671
0.0001
72
-0.62176
0.0001
66
0.59571
0.0001
64
0.671
0.0001
64
-0.00031
0.9981
64
0.14063
0.2601
66
-0.83783
0.0001
66
-0.81424
0.0001
66
-0.14926
0.2108
72
0.10555
0.3845
70
0.15915
0.1882
70
0.07685
0.5272
70
0.21089
0.0754
72
-0.11284
0.3453
72
-0.32777
0.0049
72
r.
P
N
r,
P
N
r,
P
N
r,
P
N
r,
P
N
r.
P
N
r,
P
N
KEY: PCT_T2 = SAVHmer H area * 100, PCT_T3 = SAVH/Tier m area * 100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; Kj = light attenuation; PLW = percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; r, =
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N = sample size,
number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less SAV) except
PLL/PLW (more light, more SAV). Correlations in bold were significant and > +/- 0.5.
-------
Appendix E - Spearman Rank Correlations 213
TABLE E-8. Polyhaline Spearman rank correlations between water quality from Chesapeake Bay Program
midchannel water quality stations over the spring, and measures of SAV area over Chesapeake Bay
Program segments, adding half tidal range for PLW and PLL.
Parameter
K,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Polyhaline (March-May)
PCTJT2
-0.48878
0.0001
60
0.40515
0.0013
60
0.47708
0.0001
60
-0.14236
0.2779
60
-0.22172
0.0915
59
-0.63264
0.0001
60
-0.2457
0.0585
60
PCTJT3
-0.49618
0.0001
60
0.40959
0.0012
60
0.48209
0.0001
60
-0.14853
0.2574
60
-0.21318
0.105
59
-0.63744
0.0001
60
-0.24253
0.0619
60
SAVH
-0.56852
0.0001
72
0.50063
0.0001
69
0.57068
0.0001
69
-0.20028
0.0989
69
-0.23034
0.0551
70
-0.65205
0.0001
72
-0.34673
0.0028
72
LAGSAVH
-0.61765
0.0001
66
0.54172
0.0001
63
0.6122
0.0001
63
-0.14948
0.2423
63
-0.24705
0.0491
64
-0.60465
0.0001
66
-0.43067
0.0003
66
CHGSAVH
-0.0433
0.718
72
0.0179
0.8839
69
0.05376
0.6608
69
0.09456
0.4396
69
0.003
0.9803
70
0.00106
0.9929
72
-0.31952
0.0062
72
KEY
r.
P
N
r.
P
N
r,
P
N
r.
P
N
r,
P
N
r.
P
N
r,
P
N
KEY: PCT_T2 = SAVH/Tier H area * 100, PCT_T3 = SAVH/Tier m area * 100, SAVH = SAV hectares for same year as
water quality data; LAGSAVH = SAV hectares for following year; CHGSAVH = change in SAV hectares from that year to
next; K,, = light attenuation; PLW = percent light through water column; PLL = percent light at the leaf; TSS = total
suspended solids; CHLA = chlorophyll a; DIP = dissolved inorganic phosphorus; DIN = dissolved inorganic nitrogen; r, =
Spearman rank correlation coefficient, P = statistical probability (significant shown in italics ifP < 0.05) and N = sample size,
number of segment-year combinations. Expected correlations: Negative for all parameters (more pollution, less SAV) except
PLL/PLW (more light, more SAV). Correlations in bold were significant and > +/- 0.5.
-------
214 SAV TECHNICAL SYNTHESIS II
TABLE E-9. Spearman rank correlations between water quality over the whole growing season and
sighted mean SAV depth and percent of SAV in depth categories for tidal fresh salinity regime, using
Z = 1 + half tidal range.
Parameter
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Tidal fresh (April-October)
KEY
SAVDEP PCT05 PCT1 PCT2 PCTGT2
-0.47626 0.47844 -0.58376 -0.42696 -0.42813 r,
0.0004 0.0004 0.0001 0.0018 0.0017 P
51 51 51 51 51 N
0.49962 -0.48311 0.56117 0.46107 0.44245 r,
0.0002 0.0003 0.0001 0.0007 0.0011 P
51 51 51 51 51 N
ft50518 -0.48792 0.55483 0.46906 0.4318 r,
0.0002 0.0003 0.0001 0.0005 0.0016 P
51 51 51 51 51 N
-0.38127 0.40659 -0.53772 -0.32953 -0.27687 r,
0.0058 0.0031 0.0001 0.0182 0.0492 P
51 51 51 51 51 N
-0.21739 0.31774 -0.62429 -0.15157 -0.08892 r,
0.1254 0.0231 0.0001 0.2883 0.5349 P
51 51 51 51 51 N
0.01297 -0.1085 0.22781 -0.05524 -0.15452 r,
0.928 0.4485 0.1079 0.7002 0.279 P
51 51 51 51 51 N
0.42068* -0.45362* 0.57813* 0.36986* 0.43815* r,
0.0021
51
0.0008
51
0.0001
51
0.0076
51
0.0013
51
P
N
KEY: SAVDEP = weighted mean overall depth, PCT05 = % in water < 0.5 m deep, PCT1 = % in water 0.5-1 m deep,
PCT2 = % in water 1-2 m deep, PCTGT2 = % in water > 2 m deep, K,, = light attenuation, PLW = percent light through
water column, PLL = percent light at the leaf, TSS = total suspended solids, CHLA = chlorophyll a, DIP = dissolved
inorganic phosphorus, DIN = dissolved inorganic nitrogen, r, - Spearman rank correlation coefficient, P = statistical
probability (significant shown in italics if? < 0. OS) and N = sample size, number of segment-year combinations. * Spurious
correlations (significant but not in the expected direction). Expected correlations: Negative with all parameters except
positive for PLL and PLW; opposite for PCT05, since worse water quality should yield more SAV in the shallowest category,
because it can't grow in deeper water. Correlations in bold were significant and > +/- 0.5.
-------
Appendix E - Spearman Rank Correlations 215
TABLE E-10. Spearman rank correlations between water quality over the whole growing season and
weighted mean SAV depth and percent of SAV in depth categories for oligohaline salinity regime, using
Z = 1 + half tidal range.
Parameter
K-
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Oligohaline (April-October)
SAVDEP
-0.35629
0.0011
81
0.42664
0.0001
80
0.40273
0.0002
80
-0.31315
0.0047
80
-0.44713
0.0001
81
0.27848*
0.0118
81
0.50124*
0.0001
81
PCT05
0.35745
0.0011
81
-0.4328
0.0001
80
-0.40895
0.0002
80
0.31859
0.004
80
0.45038
0.0001
81
-0.28978*
0.0087
81
-0.51739*
0.0001
81
PCT1
-0.27214
0.014
81
0.36788
0.0008
80
0.3568
0.0012
80
-0.27976
0.012
80
-0.354
0.0012
81
0.27651*
0.0125
81
0.47143*
0.0001
81
PCT2
-0.38803
0.0003
81
0.45601
0.0001
80
0.43501
0.0001
80
-0.34897
0.0015
80
-0.46935
0.0001
81
0.26846*
0.0154
81
0.46858*
0.0001
81
PCTGT2
-0.28371
0.0103
81
0.35704
0.0011
80
0.3061
0.0058
80
-0.26981
0.0155
80
-0.37281
0.0006
81
0.46319*
0.0001
81
0.34033*
0.0019
81
KEY
Tt
P
N
r,
P
N
r,
P
N
r,
P
N
r>
P
N
r,
P
N
r.
P
N
KEY: SAVDEP = weighted mean overall depth, PCT05 = % in water < 0.5 m deep, PCT1=% in water 0.5-1 m deep,
PCT2 = % in water 1-2 m deep, PCTGT2 = % in water > 2 m deep, K,, = light attenuation, PLW = percent light through
water column, PLL = percent light at the leaf, TSS = total suspended solids, CHLA = chlorophyll a, DIP = dissolved
inorganic phosphorus, DIN = dissolved inorganic nitrogen, r, = Spearman rank correlation coefficient, P = statistical
probability (significant shown in italics ifP < 0.05) and N = sample size, number of segment-year combinations. * Spurious
correlations (significant but not in the expected direction). Expected correlations: Negative with all parameters except
positive for PLL and PLW; opposite for PCT05, since worse water quality should yield more SAV in the shallowest category,
because it can't grow in deeper water.
-------
216 SAV TECHNICAL SYNTHESIS I
TABLE E-11. Spearman rank correlations between water quality over the whole growing season and
weighted mean SAV depth and percent of SAV in depth categories for mesohaline salinity regime, using
Z = 1 + half tidal range.
Parameter
K-
PLW(1+)
PLL(1+)
TSS
CHLA
DD?
DIN
Mesohaline (April-October)
SAVDEP
-0.10712
0.1361
195
0.0638
0.3755
195
0.06224
0.3873
195
0.25752*
0.0003
195
-0.18377
0.0101
195
-0.18847
0.0083
195
-0.28457
0.0001
195
PCT05
0.0374
0.6037
195
0.00419
0.9536
195
0.00508
0.9438
195
-0.30892*
0.0001
195
0.16851
0.0185
195
0.2013
0.0048
195
0.30415
0.0001
195
PCT1
0.02493
0.7294
195
-0.0649
0.3674
195
-0.06511
0.3658
195
0.36127*
0.0001
195
-0.15538
0.0301
195
-0.23045
0.0012
195
-0.32576
0.0001
195
PCT2
-0.33465
0.0001
195
0.29527
0.0001
195
0.29925
0.0001
195
0.01639
0.8201
195
-0.20927
0.0033
195
-0.1571
0.0283
195
-0.1818
0.011
195
PCTGT2
-0.33469
0.0001
195
0.34068
0.0001
195
0.33304
0.0001
195
-0.04042
0.5748
195
-0.12626
0.0786
195
-0.07433
0.3018
195
-0.20125
0.0048
195
KEY
r§
P
N
r,
P
N
r,
P
N
r§
P
N
r.
P
N
r$
P
N
r>
P
N
KEY: SAVDEP = weighted mean overall depth, PCT05 = % in water < 0.5 m deep, PCT1 = % in water 0.5-1 m deep,
PCT2 = % in water 1-2 m deep, PCTGT2 = % in water > 2 m deep, r, = Spearman rank correlation coefficient, P = statistical
probability (significant shown in italics ifP < 0.05) and N = sample size, number of segment-year combinations. * Spurious
correlations (significant but not in the expected direction). Expected correlations: Negative with all parameters except
positive for PLL and PLW; opposite for PCT05, since worse water quality should yield more SAV in the shallowest category,
because it can't grow in deeper water.
-------
Appendix E - Spearman Rank Correlations 217
TABLE E-12. Spearman rank correlations between water quality over the whole growing season and
weighted mean SAV depth and percent of SAV in depth categories for polyhaline salinity regime, using
Z = 1 + half tidal range.
Parameter
K,
PLW(1+)
PLL(1+)
TSS
CHLA
DIP
DIN
Polyhaline (March-May, September-November)
SAVDEP
-0.40593
0.0026
53
0.39089
0.0038
53
0.45614
0.0006
53
-0.04574
0.745
53
-0.22067
0.1123
53
-0. 54431
0.0001
53
-0.58385
0.0001
53
PCT05
0.46252
0.0005
53
-0.46275
0.0005
53
-0.51691
0.0001
53
0.09946
0.4786
53
0.24866
0.0726
53
0.49792
0.0001
53
0.49099
0.0002
53
PCT1
-0.49876
0.0001
53
0.52564
0.0001
53
0.57619
0.0001
53
-0.15047
0.2822
53
-0.22397
0.1069
53
-0.48986
0.0002
53
-0.42222
0.0016
53
PCT2
-0.20957
0.132
53
0.20059
0.1498
53
0.24712
0.0744
53
-0.05782
0.6809
53
-0.23647
0.0882
53
-0.42408
0.0016
53
-0.55344
0.0001
53
PCTGT2
-0.31768
0.0205
53
0.21888
0.1153
53
0.27536
0.046
53
0.0346
0.8057
53
-0.0341
0.8085
53
-0.60252
0.0001
S3
-0.55444
0.0001
53
KEY
r,
P
N
r,
P
N
r,
P
N
r.
P
N
r,
P
N
rg
P
N
r,
P
N
KEY: SAVDEP = weighted mean overall depth, PCT05 = % in water < 0.5 m deep, PCT1 = % in water 0.5-1 m deep,
PCT2 = % in water 1-2 m deep, PCTGT2=% in water > 2 m deep, K* = light attenuation, PLW = percent light through water
column, PLL = percent light at the leaf, TSS = total suspended solids, CHLA = chlorophyll a, DIP = dissolved inorganic
phosphorus, DIN = dissolved inorganic nitrogen, r, = Spearman rank correlation coefficient, P = statistical probability
(significant shown in italics ifP < ft 05) and N = sample size, number of segment-year combinations. Expected correlations:
Negative with all parameters except positive for PLL and PLW; opposite for PCT05, since worse water quality should yield
more SAV in the shallowest category, because it can't grow in deeper water. Correlations in bold were significant and >
+/- 0.5.
-------
Chesapeake Bay Program
A Watershed Partnership
410 Severn Avenue
Suite 109
Annapolis, Maryland 21403
1-800-YOUR BAY
www chesapeakebay.net
-------