1
INTRODUCTION
Mercury is one of the most pervasive environmental contaminants in aquatic
ecosystem around the world, and occurs in the environment both naturally and as a result
of anthropogenic activities such as fossil fuel combustion and waste disposal (Campbell
et al. 1992; Weiner et al. 2007). Mercury acts as a potent neurotoxin which elicits a host
of adverse behavioral effects; however, the most severe impacts of mercury exposure are
impaired fetal and infant development in humans (Watanbe and Satoh 1996), as well as
decreased reproductive success in wildlife including birds, mammals and fish
(Schwarzbach and Adelsbach 2003; Scheuhammer et al. 2007).
Historic mercury and gold mining activities in the Coast Range and Sierra
Nevada mountains of California have led to elevated levels of mercury contamination
throughout the State, in particular the San Francisco Estuary (Estuary). Inorganic
mercury continues to be transported downstream where it is deposited in Estuary
sediments. Once in the anoxic layer, inorganic mercury is converted to its most toxic
form, methylmercury. The conversion of inorganic mercury to methylmercury is carried
out through the activity of sulfate reducing bacteria found in sediments (Hamdy and
Noyes 1975), effectively linking methylmercury production to aquatic environments.
Methylmercury, which is organic and bioavailable, readily enters aquatic food-webs and
bio-accumulates to toxic concentrations in higher trophic level fish and birds, as well as
humans (Mason et al 1995b).
Tidal wetlands found along the margins of the Estuary serve as critical habitat for
many species of fish, birds and mammals, many of which are threatened or endangered.
2
This has led to a large-scale restoration effort that seeks to convert 15,100 acres of
former salt production ponds back into native tidal wetlands; however, tidal wetlands are
extremely active in the production of methylmercury and its subsequent export to
adjacent marine environments due to increased bacterial activity associated with
increased sedimentation (Lacerda and Fitzgerald 2001). Furthermore, areas with a
higher proportion of wetlands have been found to export higher levels of methylmercury
(Saint Louis et al. 1994; Saint Louis et al. 1996). While restorative action may increase
natural habitat for wildlife, it may elevate net production and export of methylmercury
resulting in an increase in bioavailable mercury within the Estuary as well as the Pacific
Ocean (Brown 2003; Davis et al. 2003).
In response to potential changes in mercury bioavailability resulting from wetland
restoration, small forage fish have been chosen as bioindicators to monitor these trends
(Wiener et al. 2003). Forage fish act as suitable bioindicators because they represent a
direct measure of mercury that has entered the food web and accumulates in higher
trophic level organisms (Wiener et al. 2007). Due to small home ranges and short life
spans, forage fish have the ability to show trends in mercury availability on a fine scale
both spatially and temporally (Greenfield et al. 2006). Inland silversides (Menidia
beryllina) are an ideal bioindicator species and have been successfully used to evaluate
mercury trends in the Sacramento-San Joaquin Delta (Slotton et al. 2002) and the Cache
Creek watershed (Domagalski et al. 2004).
One of the most important aspects in using forage fish as bioindicators is being
able to understand and account for the effects of any co-variables that may confound the
3
interpretation of trends (Wiener et al. 2007). The co-variation of fish mercury with fish
size and been recognized and accounted for in past studies by either targeting fish within
a narrow size range (Greenfield et al. 2006) or by statistically adjusting for the mercuryfish size dependence by using fish size as a covariate in ANCOVA models (Watras et al.
1998; Soneston 2003). However, many studies have also recognized growth rate as a
potential confounding factor in the interpretation of fish mercury concentrations (Verta
1990; Harris and Bodalay 1998; Stafford and Haines 2001; Surrette et al. 2006).
Simoneau et al. (2005) found that growth rate variation among populations caused up to
a four-fold difference in fish mercury concentrations. To further illustrate the
importance of growth rate on fish mercury concentration, Wiener et al. (2007) stated
“variations in growth rates of fish could influence the size standardized mercury
concentrations that are used to assess temporal trends in MeHg (methylmercury)
accumulation.” The influence of growth rate on mercury concentration is caused by an
effect known as biodilution or growth dilution (Trudel and Rassmussen 2006).
The concept of biodilution suggests that faster growing fish will have lower
mercury concentrations than slower growing fish of the same size even if exposed to
similar base mercury levels (Doyon et al. 1998). This occurs when the mercury ingested
by fast growing fish is diluted into a greater body mass. A slower growing indivdiual
may have to expend more energy on certain activities such as competition for resources
or predator avoidance, and thus not be able to allocate the same amount of resources
toward growth. As a result, the ingested mercury will be incorporated into a smaller
mass of body tissue which will cause an increase in mercury concentration. The effect of
4
biodilution has even been suggested as a potential strategy for lowering fish mercury
concentrations in hydroelectric reservoirs (Mailman et al. 2006). In these cases intensive
fishing pressure would be exerted on predatory fish. The subsequent decrease in
predation pressure would increase growth rates of lower trophic level forage fish and
dilution of the mercury body burden, resulting in an overall decrease in the amount of
mercury in the food web.
Current adaptive management strategies within the Estuary include the use of
bioindicator forage fish to assess temporal and spatial trends in available mercury that
may result from restorative actions (Wiener et al. 2003). However, despite the potential
confounding effects on mercury concentrations in fish, the determination of growth rate
is not included. It is imperative that any potential confounding factors be identified and
accounted for when using any bio-indicator organism to assess trends in mercury
availability. The determination of growth rates in bioindicator fish will lead to a more
accurate interpretation of spatial and temporal trends in available mercury that result
from the restoration of tidal wetlands within the San Francisco Estuary.
The present study seeks to improve the use of forage fish as bioindicators when
assessing trends in methylmercury production that result from restorative actions in the
San Francisco Estuary. To accomplish this goal, two hypotheses were tested. The first
was to determine if growth rates significantly differ among populations of inland
silversides (Menidia beryllina) on the Don Edwards National Wildlife Refuge. Second,
the study determined if the growth rates of bioindicator fish have a significant effect on
the mercury bioaccumulation dynamics of these fish.
5
METHODS
Study Site and Target Species
Mercury concentrations and growth rates of inland silversides (Menidia
beryllina) from the South Bay region of the San Francisco Estuary, California (37.8° N,
122.3° W; Figure 1) were examined. Although many species of fish from this region can
serve as bioindicators, inland silversides have short life spans, small home range and
wide distribution. This makes them a suitable bioindicator species when assessing
relatively small scale temporal and spatial trends. Field work was conducted at two areas
in the southernmost region of San Francisco Estuary: the West Alviso salt pond complex
of the Don Edward’s San Francisco Bay National Wildlife Refuge (ponds A1, A2W and
AB2; Figure 2) and the East Alviso salt pond complex of the Don Edwards National
Wildlife Refuge (ponds A5, A7 and A16; Figure 2).
Sample Collection
Fish were collected during July 12 - 20, 2006 with various sized beach seines (3,
10 and 15 m) depending on shoreline topography. A sample size of 20 individuals
(standard length = 30 to 70 mm) was targeted, and collected fish were placed in resealable plastic bags and immediately flash frozen in the field with dry ice prior to
transport to the laboratory for storage at -20°C. In the laboratory samples were rinsed
with deionized water and identified to the species level. Standard lengths were taken to
the nearest mm and wet mass was taken to the nearest 0.01 g.
6
Figure 1.
The southern region of the San Francisco Estuary.
Figure 2.
West and East Alviso Salt Pond Complexes located on the Don Edwards San Francisco Bay National
Wildlife Refuge. Sampling occurred at labeled ponds between July 12 th and July 20th, 2006.
7
Once length and weight data were taken, each sample was given an alphanumeric code,
individually stored in a sterile 4 Oz. Whirl-Pak® (Nasco, Modesto, CA, USA) and kept
frozen at -20°C until otolith removal and mercury analysis.
Otolith Preparation
Otolith preparation techniques were derived from those described by Stevenson
and Campana (1992). Sagittal otoliths were dissected from each fish designated for
mercury analysis using a Nikon SMZ645 dissecting scope (Nikon Inc., Melville, NY,
USA) at 8× to 50× magnification, and stainless steel dissecting scissors and forceps.
Excess tissue was removed and the extracted otoliths were rinsed in deionized water and
placed in ABgene® 0.2 mL thermo-tubes (Thermo Fisher Scientific, Waltham, MA,
USA) with 90% ethanol for 24 hours to remove internal moisture and assist clearing. To
conform to a uniform standard the left otolith was used in microstructure analysis. If the
left otolith was vateritic (abnormal crystalline growth), broken or lost, the right otolith
was used assuming both were symmetrical (Morales-Nin 1992; Wang and Tzeng 1999).
The otolith was mounted on a microscope slide using Crystal Bond® 509
thermoplastic resin (Aremco Products, Valley Cottage, NY, USA) with the sulcal surface
facing upward. The mounted otolith was wet ground along the sagittal plane using 1200
grit Carbimet® sanding discs (Buehler Ltd., Lake Bluff, IL, USA) until the primordial
plane was reached. To avoid over polishing, progress was frequently checked using a
compound microscope at 100× and 400× magnification. Once the primordial plane was
reached, the otolith was polished with a MicroCloth® 2⅞ inch polishing pad and 0.3 µm
8
MicroPolish® II Alumina Suspension (Buehler Ltd., Lake Bluff, IL, USA) to enhance
microstructure visibility.
To examine daily growth increments, otoliths were viewed at 200× magnification
using a Nikon compound microscope (Nikon Inc., Melville, NY, USA) and microscopemounted digital camera. Bony Parts image analysis software (Brittnacher and Botsford,
1991 described in Calliet et al. 1996) was used to capture images, count daily growth
increments and measure inter-increment distances. Increment counts and measurements
were made in the dorso-posterior quadrant of the otolith along a radius that intersected
the growth increments at a 90° angle (Figure 3), and otolith radius measurements were
determined by summing inter-increment measurements.
Figure 3.
Digital image of a mounted and polished sagittal otolith (40× magnification). Increments were counted
and measured along a transect in the dorso-posterior quadrant (as indicated by dashed arrow).
9
Size-at-Age Back-Calculation and Growth Rate Determination
Size-at-age was back-calculated using the Biological Intercept Model (Campana
1990):
SLi = SLc + [(Oi – Oc) (SLc – SLh) / (Oc – Oh)] ,
where SLi is standard length (SL) at age i (days), SLc is SL at capture, SLh is SL at
hatching , Oi is otolith radius at age i, Oc is otolith radius at capture, and Oh is otolith
radius at hatching. A standard length at hatching of 4 mm was used for silversides
(Moyle 2002). Laboratory reared silversides had 6 growth increments at time of
hatching (Gleason and Bengston 1996); therefore, to determine the otolith radius at
hatching the distance from the primordium to the sixth increment was measured.
Upon completion of fish size-at-age back-calculations, multiple growth rates
were determined for each individual fish. Absolute growth rate was determined to
represent the average growth rate throughout the lifetime of the fish. Additionally, the
growth rate in the first 30 days after hatching, and the growth rate 30 pays pre-capture
were calculated. By calculating different growth rates, fish growth during different time
periods in its life span can be examined.
Mercury Analysis
Whole fish samples were analyzed for mercury at the U.S. Geological Survey,
Davis Field Station Mercury Analysis Laboratory. Previous research indicates that
nearly all (>95%) the mercury present in fish is in the form of methylmercury (Bloom
10
1992; Kim 1995). Therefore, whole fish samples were analyzed for total mercury as a
proxy for methylmercury concentrations. This method served to significantly reduce
analytical costs and analysis time. Prior to analysis, each sample was dried at 60°C for
48 hours to remove all moisture. Dried samples were then homogenized using a Wiley
Mill laboratory grinder (Thomas Scientific, Swedesboro, NJ) and placed in sterile 25 mL
vials. Homogenized samples were then placed in an air tight silica desiccator for 30
minutes to remove any moisture that may have contaminated the sample while grinding.
A 50 mg aliquot was removed from each processed sample to be analyzed for total
mercury. Analysis was done in accordance with U.S. Environmental Protection Agency
method 7473 (US EPA 2000) using a Milestone DMA-80 Direct Mercury Analyzer
(Milestone, Monroe, CT) using an integrated sequence of drying (160º C for 140
seconds), thermal decomposition (850º for 240 seconds), catalytic conversion and
amalgamation, followed by atomic absorption spectroscopy.
Data Analysis
To determine differences in total mercury concentration among sites, statistical
analyses were conducted on log-transformed data. Additionally, dry weight total
mercury concentrations were used to control for variation in percent moisture among
individual fish that may confound results. Total mercury was compared among sites
using analysis of covariance (ANCOVA) with site as the categorical variable and
standard length as the covariate. Pairwise comparisons were conducted when
appropriate using the Tukey HSD post hoc test to determine which groups are
significantly different from one another. In addition to examining spatial trends in fish
11
mercury concentrations, tissue levels were also compared to a San Francisco Bay Total
Maximum Daily Load (TMDL) quality criteria developed for mercury. The TMDL
criteria is a fish tissue goal that was developed by Federal, State and Local agencies in an
effort to protect piscivorous wildlife in the Estuary, including many species of birds,
aquatic mammals and predatory fish (SFBRWQCB 2006). The proposed objective is
0.03 ppm (wet weight) in small forage fish between three and five centimeters in length.
The young-of-year inland silversides analyzed in this study are ideal candidates. If tissue
concentrations fall below the objective, than fish-eating wildlife in the area are not
considered to be at risk of mercury exposure via the local food-web. In converting the
TMDL tissue objective to a dry weight concentration of 0.15 ppm, the following formula
was used with 80 percent moisture assumed to be an average for fish tissue:
Wet Weight Concentration = (Dry Weight Concetration) × [(100 - % moisture) / (100)]
To determine if average fish tissue mercury concentrations exceeded the TMDL
objective, a single sample t-test was used with log-transformed, dry weight total mercury
data compared against the log-transformed dry weight tissue objective. Separate
comparisons were conducted for each sampling location. Although statistical analyses
were performed on log-transformed, dry weight total mercury data, back-transformed
least-squares means are described in the text to increase clarity.
Otolith microstructure data was used to create growth trajectories at each site by
using linear regression of standard length on age (number of daily increments minus 6).
12
These growth trajectories were compared among sites using ANCOVA with standard
length as the dependant variable, site as the categorical variable and age as the covariate.
Growth rates were determined using the back-calculated size-at-age data (i.e., absolute
growth rate, first 30 day, 30 day pre-capture). These calculated growth rates were
compared among populations using ANCOVA with site as the categorical variable and
standard length as the covariate.
To determine the relationship between growth rate and total mercury
concentration, back-calculated growth rates were added into the ANCOVA model as
continuous variables. Log-transformed, dry weight mercury concentration will be the
dependant variable with site as the categorical variable and the different growth rates as
the continuous variables. Statistical tests were carried out using Statistica® 7.1 data
analysis software (StatSoft, Inc., Tulsa, OK) and results were considered to be significant
if P < 0.05.
13
RESULTS
In total, 127 inland silversides were collected for mercury and otolith
microstructure analysis. Although a sample size of 20 individuals was the target for
analysis, some otoliths were broken or lost during dissection and in one case mercury
data was not available, resulting in three sites with less than 20 individuals (pond A1: n =
17, pond A16: n = 19, pond A7: n = 19). However, all analyses were still conducted
despite unequal sample sizes.
Fish Mercury Concentrations and Spatial Trends
The mean total mercury concentration for all fish collected was 0.64 ppm dry
weight. This is significantly higher that the San Francisco Bay TMDL small fish tissue
objective of 0.15 ppm dry weight (t-test, P < 0.001). Mean total mercury concentration
at each site was also compared to the TMDL value, and each site was significantly higher
(T-test: P < 0.001 for all sites; Figure 4).
Total mercury concentrations in inland silversides varied significantly among
sites (ANCOVA: F (5, 108) = 14.1435, P < 0.001; Figure 4). Mean concentrations (leastsquares mean ± standard error) were lowest in pond A1 (0.44 ± 0.04 ppm dry weight). In
contrast, mercury concentrations were highest in ponds A2W, AB2 and A7 (0.98 ± 0.09
ppm dry weight, 0.79 ± 0.07 ppm dry weight, and 0.74 ± 0.07 ppm dry weight,
respectively), and moderate in pond A16 (0.56 ± 0.05 ppm dry weight) and pond A5
(0.47 ± 0.04 ppm dry weight).
14
10.0
Mean
Mean±SE
Non-Outlier Range
Total Mercury Concentration
(ppm dry weight)
c
b, c
a
b, c
a, b
1.0
a
0.1
A1
A2W
AB2
A5
A7
A16
Site
Figure 4.
Inland silverside (Menidia beryllina) total mercury concentrations (dry weight) among sites. Sites with
similar letters are not significantly different (Tukey HSD post hoc test, P < 0.05). The red dashed line
indicates the San Francisco Bay TMDL fish tissue objective for protection of piscivorous fish and wildlife
(0.15 ppm dry weight).
15
Length-at-Age Analysis
Following otolith microstructure analysis, daily increments were counted for each
fish to determine age in days. The youngest fish captured was 49 days old and the oldest
fish captured was 133 days old. Once length-at-age data were determined, a linear
regression of standard length on age was conducted by pond. A strong significant
relationship was found between standard length and age at each site (pond A1: R² = 0.90,
P < 0.001; pond A2W: R² = 0.88, P < 0.001; pond AB2: R² = 0.93, P < 0.001; pond A5:
R² = 0.90, P < 0.001; pond A7: R² = 0.89, P < 0.001; pond A16: R² = 0.89, P < 0.001;
Figure 5). Comparison of the growth trajectories (i.e., slopes of each regression) among
sites indicated that there was a significant difference among sites (ANCOVA: F (5, 108) =
5.41, P < 0.001). Pairwise post hoc tests were conducted and revealed that all sites were
similar except for pond A7.
Growth Rate Analysis
Fish age data (in days) were used with length at capture and length at hatching
data to determine an absolute growth rate for each individual. Absolute growth rates
ranged from 0.38 mm/day to 0.65 mm/day. Mean absolute growth rate at each site
increased in the following order: pond A2W (0.47 ± 0.01 mm/day), pond A1 (0.48 ± 0.01
mm/day), pond AB2 (0.48 ± 0.01 mm/day), pond A7 (0.50 ± 0.01 mm/day), pond A16
(0.51 ± 0.01 mm/day), pond A5 (0.55 ± 0.01 mm/day). Statistical analysis indicated that
absolute growth rate was significantly different among sites (ANCOVA: F (5, 108) =
11.096, P < 0.001). The post hoc test indicated that absolute growth rates were similar at
16
all locations except for pond A5, which had a significantly faster absolute growth rate
that all other sites (Figure 6).
Using the Biological Intercept model, back-calculated standard lengths were
determined for each individual fish at 30 days old. Using these data and length at
hatching data, the first 30 day growth rate (mm/day) was calculated for each fish to
compare growth right after hatching. First 30 day growth rates ranged from 0.38
mm/day to 0.67 mm/day. Mean first 30 day growth rate increased among sites in the
following order: pond A7 (0.48 ± 0.01 mm/day), pond A2W (0.49 ± 0.01 mm/day), pond
A1 (0.50 ± 0.01 mm/day), pond AB2 (0.51 ± 0.01 mm/day), pond A16 (0.52 ± 0.01
mm/day), pond A5 (0.54 ± 0.01 mm/day). Statistical analysis indicated that mean first
30 day growth rate significantly differed among sites (ANCOVA: F (5, 108) = 3.0822, P =
0.01), with the fastest growth rate in pond A5, intermediate growth rates in ponds A16,
AB2 and A1, and the slowest growth rates in ponds A7 and A2W (Figure 7).
The Biological Intercept Model was also used to back-calculate individual fish
standard lengths at 30 days pre-capture. These data were used with standard length-atcapture data to calculate the 30 day pre-capture growth rate (mm/day) for each individual
fish. The 30 day pre-capture growth rates ranged from 0.36 mm/day to 0.66 mm/day.
Mean 30 day pre-capture growth rates increased among sites in the following order: pond
A1 (0.45 ± 0.01 mm/day), pond A2W (0.46 ± 0.01 mm/day), pond AB2 (0.47 ± 0.01
mm/day), pond A16 (0.47 ± 0.01 mm/day), pond A7 (0.53 ± 0.01 mm/day), pond A5
(0.57 ± 0.01 mm/day). Mean 30 day pre-capture growth rates were significantly
17
different among sites (ANCOVA: F (5, 108) = 18.835, P < 0.001), with fish from ponds A5
and A7 having the faster growth rates than fish from the remaining locations (Figure 8).
Growth Rate Effects on Mercury Concentrations
To determine the effect of growth rate on total mercury concentration, each
determined growth rate was individually included in an ANCOVA model as a continuous
variable. Individual models for each growth rate were run because a correlation was
found between the three growth rates (Absolute growth rate : First 30 day growth rate, R
= 0.79; Absolute growth rate : 30 day pre-capture, R = 0.80; First 30 day growth rate :
30 day pre-capture growth rate, R = 0.39). In addition to calculated growth rates,
standard length remained in each model as a continuous variable and site remained as a
categorical variable. Results of the models indicate that none of the three calculated
growth rates had a significant effect on total mercury concentration (Absolute growth
rate, P = 0.86; First 30 day growth rate, P = 0.51; 30 day pre-capture growth rate, P =
0.74). In all three models, standard length was the only continuous variable to have a
significant effect on total mercury concentration (ANCOVA: Absolute growth rate, F (1,
107)
= 133.67, P < 0.001; First 30 day growth rate, F (1, 107) = 134.17, P < 0.001; 30 day
pre-capture growth rate, F (1, 107) = 132.0797, P < 0.001).
In addition to separate ANCOVA models, a global model was also run including
all three growth rates as continuous variables. Like the separate models, standard length
remained as a continuous variable and site remained as a categorical variable. Results of
the global model were similar to the individual models, with none of the calculated
growth rates having a significant effect (Absolute growth rate, P = 0.86; First 30 day
18
growth rate, P = 0.42; 30 day pre-capture growth rate, P = 0.79) and standard length
having a strong significant effect (ANCOVA: F (1, 105) = 132.0797, P < 0.001).
These results show that none of the three calculated growth rates had a significant
impact on total mercury concentration. This indicates that the inland silverside total
mercury concentrations were not significantly confounded by differences in growth rates.
Therefore, the spatial trends identified in these fish can be considered to be due to
differences in mercury availability among the different ponds.
19
70
65
60
55
50
45
40
35
30
SL (mm)
25
40
60
80
100 120 140
40
60
Site: A1
80
100 120 140
40
60
Site: A16
80
100 120 140
Site: A2W
70
65
60
55
50
45
40
35
30
25
40
60
80
100 120 140
Site: A5
40
60
80
100 120 140
Site: A7
Age (days)
Figure 5.
Growth trajectories of inland silversides at each site sampled.
40
60
80
100 120 140
Site: AB2
20
0.66
0.64
Mean
Mean±SE
Non-Outlier Range
0.62
0.60
b
Absolute Growth Rate (mm/day)
0.58
0.56
a
a
0.54
a
a
0.52
a
0.50
0.48
0.46
0.44
0.42
0.40
A1
A2W
AB2
A5
A7
A16
Site
Figure 6.
Inland silverside absolute growth rates (mm/day) among sites. Sites with similar letters are not
significantly different (Tukey HSD post hoc test, P < 0.05).
21
0.66
0.64
Mean
Mean±SE
Non-Outlier Range
0.62
0.60
Growth Rate: First 30 days (mm/day)
0.58
a, b
b
a, b
0.56
a, b
a
a
0.54
0.52
0.50
0.48
0.46
0.44
0.42
0.40
A1
A2W
AB2
A5
A7
A16
Site
Figure 7.
Inland silverside first 30 day growth rates (mm/day) among sites. Sites with similar letters are not
significantly different (Tukey HSD post hoc, P < 0.05).
22
0.66
0.64
Mean
Mean±SE
Non-Outlier Range
b
0.62
0.60
b
Growth Rate: 30 days pre-capture (mm/day)
0.58
0.56
0.54
a
0.52
0.50
a
a
a
0.48
0.46
0.44
0.42
0.40
A1
A2W
AB2
A5
A7
A16
Site
Figure 8.
Inland silverside 30 day pre-capture growth rates (mm/day) among sites. Sites with similar letters are not
significantly different (Tukey HSD post hoc, P < 0.05).
23
DISCUSSION
Fish mercury concentrations showed a high degree of variation on a relatively
small spatial scale, reinforcing the utility of small forage fish to be used when monitoring
differences in mercury bioavailability. This study attempted to improve upon the use of
bioindicator fish by integrating daily growth rate analysis into currently utilized
monitoring techniques. Differences in absolute growth rate, first 30-day growth rate and
30-day pre-capture growth rate were seen among ponds. This result could be attributed
to differences in various water quality parameters among the ponds. As part of a water
quality monitoring program, data was collected by the U.S. Fish and Wildlife Service
(2007) from various ponds on the Refuge, three of which coincided with sites sampled
during this study (ponds A2W, A7 and A16). Temperature, salinity and dissolved
oxygen all varied among the three ponds (Appendix C). More importantly, differences
in these parameters have been shown to affect fish growth (Buentello et al. 2000;
Likongwe et al. 1996). Despite significant variation in fish growth rates and mercury
concentrations among ponds, results suggest that growth rate is not influencing the
dynamics of methylmercury bioaccumulation in inland silversides. More importantly,
growth rate analysis did not improve upon the use of these fish to elucidate trends in
available mercury within the West and East Alviso salt pond complexes.
Inorganic mercury loading in this region is generally considered to be elevated
when compared to other regions of the San Francisco Estuary and the Sacramento-San
Joaquin Delta (Wiener et al. 2003; Conaway et al. 2004). This is largely a result of
Alviso and Guadalupe sloughs entering the Estuary near the small community of Alviso,
24
adjacent to the sampling sites. The Alviso and Guadalupe slough watersheds contain the
New Almaden mining district, the most productive mercury mining district in North
America. These mines supplied over 220 million pounds of mercury to the Sierra
Nevada Mountains for use in hydraulic gold mining operations. Historic activities
mobilized large quantities of inorganic mercury and ultimately led to the deposition of
mercury contaminated sediments in much of the southern Estuary (Beutel and Abu-Saba
2004; Conaway et al. 2004). Despite mine closures and remedial actions, it is estimated
that as much as 30 kilograms of mercury are annually being transported into the Estuary
via the Guadalupe River (Thomas et al. 2002). As a result, previous studies have
observed elevated mercury concentrations in fish tissue sampled in the southern reaches
of the Estuary (Greenfield et al. 2006; Eagles-Smith and Ackerman 2009).
Fish mercury levels measured in this study were at concentrations considered to
be extremely high for small forage fish. In addition to exceeding the San Francisco Bay
TMDL small fish tissue objective, many individual fish had total mercury concentration
in excess of the human health consumption threshold proposed in the same document
(1.0 ppm dry weight). This threshold concentration was also derived by Federal, State
and local agencies in an effort to create fish tissue goals in the Estuary that would reduce
the risk of mercury exposure to humans that consume fish caught in the Estuary
(SFBRWQCB 2006). Because this objective is targeted toward human consumption of
popular sport fish, it was meant to be applied to 600 mm striped bass and 750 mm
California halibut. These fish are an order of magnitude larger than the small inland
silversides analyzed in this study. Moreover, they are predatory fish that occupy high
25
trophic positions within the community. Despite the small size and low trophic status,
inland silversides had mercury concentrations that would be considered elevated in much
larger, predatory fish. This indicates that these fish are exposed to an environment with
extremely high levels of methylmercury in the food-web, which in turn facilitated rapid
bioaccumulation and subsequent biomagnification.
In addition to large loads of inorganic mercury, the former salt production ponds
sampled during this study may be providing ideal abiotic conditions for the bacterial
conversion of inorganic mercury to methylmercury. Production of methylmercury is
generally accelerated by increased temperature, salinity and import of organic material
(Marvin-DiPasquale et al. 2003; Lambertsson and Nilsson 2006; Hall et al. 2008). These
ponds are generally very shallow (0.5 – 3 meters), and some may experience very little
water movement either in or out. During the summer months, when field sampling was
conducted, the shallow depths and long water residence commonly lead to an increase in
temperature and an increase in salinity via evapo-concentration (USFWS 2002).
Ultimately, this may be enhancing methylmercury production and availability. Ideal
methylation conditions, coupled with the long history of inorganic mercury deposition,
are likely creating a situation where aquatic organisms are able to bioaccumulate
mercury at very high concentrations within a relatively short time frame. In this
instance, it may be possible that the impacts of differing growth rates and the effects of
biodillution would not been noticed. The absence of a significant effect of growth rate
on mercury bioaccumulation could have been due to high levels of mercury deposition
and ideal methylation conditions found in the extreme southern region of the San
26
Francisco Estuary. These small fish are experiencing such rapid uptake of mercury that
it could be obscuring any effects that differences in inter-population growth rates may be
having on bioaccumulation dynamics.
Previous studies examining the impact of growth rate on mercury
bioaccumulation in fish via biodillution have all investigated growth rate and mercury
interactions in large multi-year fish such as smallmouth bass (Micropterus dolomieu),
walleye (Sander vitreus) and yellow perch (Perca flavescens) (Stafford and Haines 2001;
Essington and Houser 2003; Simoneau et al. 2005). Growth rates were determined on an
annual basis and the fish analyzed were long-lived, predatory sport fish that occupy high
trophic positions in the food-web. This study represents the first time a growth rate and
mercury interaction was examined in a small, lower tophic level fish species. Moreover,
this study also determined growth rates on a short time scale. Instead of examining
otoliths for annual growth rings, more detailed daily growth increments were used to
determine growth rates on a daily basis with young-of-year fish that were as young as
two months old. Perhaps the interaction between growth rate and mercury
bioaccumulation occurs on a more coarse temporal scale in higher trophic level species,
leading to an interaction that becomes apparent in larger, older individuals. In these fish,
variations in growth rate over a longer time period result in more extreme differences in
body mass and body condition between populations or study groups.
In general, fish experience indeterminate growth (Van Den Avyle and Hayward
1999), but a marked decrease in growth rate is commonly observed as age increases
(Busacker et al. 1990). However, inland silversides are characterized by a relatively
27
short lifespan, usually one year, and rapid growth (Moyle 2002). This was verified by
the length-at-age plots created from otolith microstructure analysis which indicated that
the inland silversides were experiencing rapid, linear growth. The observed differences
in growth rates among ponds may not have impacted mercury bioaccumulation due to
the rapid growth of all populations when compared to large, multi-year fish that have
been examined in past studies.
This study found that growth rate had no significant impact on assessing smallscale spatial trends in mercury bioaccumulation. However, small forage fish can also be
used to track short term temporal variations in mercury availability within the same
location. Recent data collected in the same region of the Estuary indicate high variability
in mercury concentrations in both threespine sticklebacks (Gasterosteus aculeatus) and
long jaw mudsuckers (Gillichthys mirabilis) within different wetland types during a fourmonth period (Eagles-Smith and Ackerman 2009). While these changes were largely
attributed to variations in production of methylmercury, an elevation in growth rate
during late summer was recognized as a possible driver for the short-term temporal
trends observed. This indicates that growth rate analysis on forage fish being used as
bioindicators of temporal rather than spatial changes in mercury availability is a possible
direction for future research.
28
CONCLUSIONS
Despite no effect of fish growth rate on mercury bioaccumulation, the results of
this study can still be considered applicable to current and future management and
monitoring programs. Mercury contamination remains to be one of the most pervasive
and persistent issues in California, as well as the entire United States. Over 1.4 million
km of rivers and 13.5 million acres of lakes across the United States had fish
consumption advisories due to problems with mercury contamination (U.S. EPA 2006).
As these problems persist, small forage fish monitoring programs will continue to be
recommended due to their ability to indicate inter-annual trends and fine scale spatial
patterns (Mason et al. 1995a; Wiener et al. 2007). Locally, efforts to restore 15,100 acres
of tidal habitat along the margins of the southern Estuary represent the largest wetland
restoration effort on the West Coast. Using forage fish as bioindicators of changing
mercury dynamics resulting from restoration remains to be a key component of reducing
risk of mercury exposure to local wildlife that the restoration is seeking to protect
(Wiener et al. 2003). One of the most important factors in using bioindicator fish is to
accurately assess any factors, other than changing mercury availability, which may be
impairing the ability to establish trends in mercury risk to wildlife. Prior to this study,
growth rate had been identified as a potential confounding factor that could be
influencing mercury concentrations in bioindicator fish. The results presented examined
this possible interaction and found that although growth rate has been shown to influence
mercury bioaccumulation in fish, there is no impact on young-of-year inland silversides
used in monitoring efforts in the southern Estuary. While this study shows that growth
29
rate analysis may not be necessary in monitoring spatial trends with inland silversides,
there may still be a need to examine the role that changing fish growth may play in the
monitoring of temporal patterns, such as inter-annual comparisons within a given
location. Many species of fish inhabit the proposed restoration area and have the
potential to be used as bioindicators. These species may have different life history
characteristics, including feeding strategy, home range and habitat use. All of these
variables have the potential to affect growth and mercury bioaccumulation. Therefore,
growth rate analysis should be taken into account for any new species of forage fish that
may be used as a bioindicator of mercury dynamics.
30
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38
APPENDIX A
Inland silverside data collection data and total mercury results.
Sample
ID
6FF5814
6FF5802
6FF5808
6FF5803
6FF5793
6FF5798
6FF5797
6FF5816
6FF5792
6FF5789
6FF5815
6FF5735
6FF5787
6FF5785
6FF5733
6FF5786
6FF5732
6FF5731
6FF5728
6FF5727
6FF6462
6FF6460
6FF6463
6FF6466
6FF6468
6FF6471
6FF6474
6FF6476
6FF6482
6FF5166
6FF6481
6FF6479
6FF5167
6FF5173
6FF5171
6FF5175
6FF5159
6FF5163
6FF5157
6FF5169
Spp
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
Site
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
Collection
Date
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
7/18/2006
Standard Length
(mm)
29
30
31
32
33
34
37
38
39
40
41
43
45
46
47
48
55
56
65
66
29
31
32
35
36
38
39
41
42
43
46
48
50
51
52
54
56
59
62
65
Wet mass
(g)
0.25
0.25
0.29
0.33
0.31
0.41
0.49
0.52
0.55
0.57
0.62
0.8
0.8
0.89
0.97
0.96
1.78
1.62
2.63
2.89
0.22
0.24
0.28
0.34
0.46
0.54
0.51
0.64
0.71
0.83
0.79
0.86
0.99
1.11
1.37
1.44
1.55
1.7
2.28
2.38
Total Mercury
(ppm dry weight)
0.3645
0.4799
0.3405
0.2572
0.3371
0.2972
0.3118
0.4492
0.2187
0.2585
0.3338
0.3096
0.3401
0.3078
0.2884
0.3027
0.7535
0.8291
1.0438
1.0098
0.3893
0.3083
0.3540
0.3404
0.3236
0.3392
0.6609
0.6193
0.3384
0.2586
0.3464
0.7579
0.5451
1.0538
0.8125
0.8640
0.9726
1.0298
1.1939
1.2572
39
Sample
ID
6FF5568
6FF5569
6FF5526
6FF5524
6FF5560
6FF5558
6FF5554
6FF5550
6FF5551
6FF5549
6FF5542
6FF5540
6FF5495
6FF5517
6FF5510
6FF5507
6FF5508
6FF5528
6FF5527
6FF5552
6FF5506
6FF5487
6FF5504
6FF6389
6FF6397
6FF6379
6FF6375
6FF6386
6FF6385
6FF5017
6FF6390
6FF6377
6FF5015
6FF6394
6FF6383
6FF6382
6FF5012
6FF5016
6FF5013
6FF5014
6FF5011
6FF5010
6FF5008
6FF5007
Spp
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
Site
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
Collection
Date
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/12/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
7/20/2006
Standard Length
(mm)
27
29
30
31
33
34
35
35
37
41
43
45
48
50
51
52
53
54
56
58
61
63
65
28
30
31
33
35
36
38
41
42
43
44
44
45
46
46
47
47
50
53
57
58
Wet mass
(g)
0.21
0.23
0.26
0.27
0.31
0.36
0.41
0.37
0.46
0.52
0.66
0.79
0.95
1.3
1.41
1.36
1.53
1.82
1.84
1.7
1.89
2
2.45
0.25
0.27
0.26
0.34
0.37
0.4
0.72
0.63
0.64
0.71
0.82
0.81
0.8
0.82
0.78
0.89
0.87
1.16
1.3
1.59
1.75
Total Mercury
(ppm dry weight)
0.3896
0.4006
0.5068
0.5405
0.6344
0.5802
0.4839
0.6972
0.7034
0.9384
1.1342
1.0299
1.1326
1.1137
1.1465
1.4080
1.3211
1.5476
1.3885
1.5361
1.5906
1.5803
1.7421
0.3323
0.2397
0.7157
0.3453
0.4977
0.4169
0.2332
0.2175
0.2559
0.3120
0.3311
0.3995
0.2334
0.2165
0.2602
0.1494
0.3043
0.9769
1.3391
0.8731
1.0315
40
Sample
ID
6FF5009
6FF5005
6FF5004
6FF6488
6FF6490
6FF6492
6FF6494
6FF6498
6FF6506
6FF6499
6FF6500
6FF5089
6FF6502
6FF5103
6FF5152
6FF5110
6FF5123
6FF5125
6FF5120
6FF5095
6FF5127
6FF5144
6FF5079
6FF5298
6FF5237
6FF5235
6FF5227
6FF5253
6FF5271
6FF5263
6FF5221
6FF5279
6FF5261
6FF5260
6FF5255
6FF5251
6FF5267
6FF5254
6FF5207
6FF5210
6FF5206
6FF5202
6FF5205
Spp
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
ISS
Site
A5
A5
A5
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
Collection
Date
7/20/2006
7/20/2006
7/20/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/19/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
7/13/2006
Standard Length
(mm)
60
65
67
31
33
36
37
39
42
43
44
45
46
51
54
56
57
58
60
61
62
64
67
28
30
32
34
35
37
38
39
40
41
42
43
44
45
57
58
60
64
66
67
Wet mass
(g)
1.73
2.28
2.88
0.27
0.31
0.45
0.46
0.56
0.67
0.77
0.75
0.88
0.78
1.34
1.53
1.71
1.73
1.87
2.11
2.28
2.26
2.35
2.96
0.21
0.27
0.32
0.36
0.41
0.5
0.6
0.53
0.56
0.64
0.69
0.74
0.85
0.9
1.59
2.03
2.21
2.34
3.42
2.9
Total Mercury
(ppm dry weight)
1.3950
2.8535
ND
0.1897
0.3556
0.2587
0.6544
0.5381
0.2613
0.6655
0.3669
0.4766
0.7214
2.4755
2.0996
2.2041
1.7569
1.2968
0.9550
2.3692
1.5136
1.7194
2.1352
0.3452
0.5527
0.7005
0.5731
0.6468
0.7892
0.6650
0.6792
0.7132
0.7485
0.7649
0.7403
0.8075
0.7571
1.3715
1.1087
0.4780
1.2861
1.5355
1.1327
APPENDIX B
Data obtained from otolith microstructure analysis and size-at-age back calculation using the Biological Intercept Model.
Sample ID
6FF5814
6FF5802
6FF5808
6FF5803
6FF5793
6FF5798
6FF5797
6FF5816
6FF5792
6FF5789
6FF5815
6FF5735
6FF5787
6FF5785
6FF5733
6FF5786
6FF5732
6FF5731
6FF5728
6FF5727
6FF6462
6FF6460
6FF6463
6FF6466
Site
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A16
A16
A16
A16
Age
(days)
N/A
69
57
62
70
70
69
71
80
81
N/A
75
79
N/A
76
86
102
111
118
138
58
59
67
66
Total
Otolith
radius
(µm)
N/A
297.99
286.38
341.85
307.02
323.79
332.18
312.83
316.05
358.62
N/A
363.78
357.33
N/A
389.58
368.94
466.34
455.37
463.76
538.58
269.61
328.31
311.54
334.76
Otolith
hatch
radius
(µm)
N/A
27.09
29.03
34.19
30.32
27.09
26.45
27.74
23.22
19.35
N/A
18.06
23.22
N/A
25.80
25.16
29.03
22.58
20.64
23.87
27.74
29.67
24.51
29.67
Absolute
growth
rate
(mm/day)
N/A
0.38
0.47
0.45
0.41
0.43
0.48
0.48
0.44
0.44
N/A
0.52
0.52
N/A
0.57
0.51
0.50
0.47
0.52
0.45
0.43
0.46
0.42
0.47
Otolith
radius: 30
days old
N/A
150.93
170.93
188.99
158.03
161.25
161.90
163.19
121.26
149.00
N/A
155.45
156.09
N/A
176.73
150.93
164.48
147.06
144.48
139.97
161.25
184.47
158.03
168.35
Backcalculated
SL: 30 days
old
N/A
15.89
18.89
18.09
17.39
17.57
18.62
20.15
15.72
17.76
N/A
19.50
20.31
N/A
21.84
20.10
19.80
18.96
21.05
17.99
17.80
18.00
17.03
18.09
Growth
Rate:
first 30
days
N/A
0.40
0.50
0.47
0.45
0.45
0.49
0.54
0.39
0.46
N/A
0.52
0.54
N/A
0.59
0.54
0.53
0.50
0.57
0.47
0.46
0.47
0.43
0.47
Otolith
radius: 30
days precapture
N/A
186.41
156.74
198.66
199.31
200.60
202.53
205.11
192.21
239.94
N/A
227.04
241.23
N/A
254.78
262.52
340.56
340.56
363.78
430.86
154.80
178.67
194.15
199.31
Backcalculated
SL: 30 d
pre-capture
N/A
19.29
17.40
18.97
21.71
21.54
23.01
25.15
24.20
27.41
N/A
27.57
30.75
N/A
31.07
34.38
40.33
42.21
51.24
53.03
17.13
17.47
20.55
21.24
Growth
Rate
last 30
days
N/A
0.36
0.45
0.43
0.38
0.42
0.47
0.43
0.49
0.42
N/A
0.51
0.47
N/A
0.53
0.45
0.49
0.46
0.46
0.43
0.40
0.45
0.38
0.46
Sample ID
6FF6468
6FF6471
6FF6474
6FF6476
6FF6482
6FF5166
6FF6481
6FF6479
6FF5167
6FF5173
6FF5171
6FF5175
6FF5159
6FF5163
6FF5157
6FF5169
6FF5568
6FF5569
6FF5526
6FF5524
6FF5560
6FF5558
6FF5554
6FF5550
6FF5551
6FF5549
6FF5542
6FF5540
Site
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A16
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
Age
(days)
66
81
67
73
66
77
77
87
88
88
96
89
106
N/A
108
105
59
N/A
60
N/A
63
64
69
62
69
95
89
94
Total
Otolith
radius
(µm)
338.63
370.23
310.89
353.46
353.46
398.61
388.94
356.69
426.99
426.35
403.77
393.45
479.88
N/A
498.59
502.46
255.42
N/A
294.12
N/A
325.73
323.15
303.15
310.25
337.98
406.35
408.29
419.25
Otolith
hatch
radius
(µm)
27.09
22.58
24.51
25.80
24.51
26.45
24.51
27.09
25.16
24.51
24.51
30.96
24.51
N/A
36.12
27.74
28.38
N/A
25.80
N/A
27.09
26.45
25.16
21.93
27.74
25.16
26.45
27.09
Absolute
growth
rate
(mm/day)
0.48
0.42
0.52
0.51
0.58
0.51
0.55
0.51
0.52
0.53
0.50
0.56
0.49
N/A
0.54
0.58
0.39
N/A
0.43
N/A
0.46
0.47
0.45
0.50
0.48
0.39
0.44
0.44
Otolith
radius: 30
days old
172.86
156.09
155.45
154.80
183.83
187.05
174.80
137.39
157.38
143.19
147.06
163.19
174.15
N/A
176.73
167.06
141.90
N/A
153.51
N/A
180.60
176.73
146.42
159.32
175.44
146.42
181.25
176.09
Backcalculated
SL: 30 days
old
18.97
17.06
20.00
18.57
22.40
20.83
21.32
18.72
19.14
17.88
19.51
22.24
21.09
N/A
21.63
21.90
15.50
N/A
16.38
N/A
18.91
19.20
17.52
18.77
19.71
15.77
19.81
19.58
Growth
Rate:
first 30
days
0.50
0.44
0.53
0.49
0.61
0.56
0.58
0.49
0.50
0.46
0.52
0.61
0.57
N/A
0.59
0.60
0.38
N/A
0.41
N/A
0.50
0.51
0.45
0.49
0.52
0.39
0.53
0.52
Otolith
radius: 30
days precapture
208.34
253.49
185.12
222.53
212.21
276.71
264.45
228.33
292.83
276.06
295.41
277.35
367.64
N/A
399.90
379.26
137.39
N/A
153.51
N/A
192.86
194.79
177.38
170.28
214.14
284.45
299.93
290.25
Backcalculated
SL: 30 d
pre-capture
22.62
26.58
23.63
26.22
25.68
30.23
31.65
30.86
34.64
33.42
38.29
37.99
43.18
N/A
49.62
49.17
15.04
N/A
16.38
N/A
20.10
21.02
20.98
19.95
23.83
29.17
31.93
31.51
Growth
Rate
last 30
days
0.45
0.38
0.51
0.49
0.54
0.43
0.48
0.57
0.51
0.59
0.46
0.53
0.43
N/A
0.41
0.53
0.40
N/A
0.45
N/A
0.43
0.43
0.47
0.50
0.44
0.39
0.37
0.45
Sample ID
6FF5495
6FF5517
6FF5510
6FF5507
6FF5508
6FF5528
6FF5527
6FF5552
6FF5506
6FF5487
6FF5504
6FF6389
6FF6397
6FF6379
6FF6375
6FF6386
6FF6385
6FF5017
6FF6390
6FF6377
6FF5015
6FF6394
6FF6383
6FF6382
6FF5012
6FF5016
6FF5013
6FF5014
Site
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A2W
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
A5
Age
(days)
86
98
97
83
105
116
103
N/A
111
122
117
N/A
56
49
N/A
58
59
64
N/A
68
71
72
71
71
76
83
77
78
Total
Otolith
radius
(µm)
407.00
441.83
405.71
411.51
461.82
462.47
436.02
N/A
486.33
521.81
515.36
N/A
285.09
260.58
N/A
278.64
354.75
342.50
N/A
332.18
345.72
381.20
357.33
397.97
397.97
397.97
385.71
441.83
Otolith
hatch
radius
(µm)
27.74
27.74
31.61
29.67
28.38
27.74
23.22
N/A
23.87
24.51
26.45
N/A
32.90
27.09
N/A
27.74
32.25
39.99
N/A
31.61
34.83
26.45
28.38
35.48
26.45
26.45
35.48
40.64
Absolute
growth
rate
(mm/day)
0.51
0.47
0.48
0.58
0.47
0.43
0.50
N/A
0.51
0.48
0.52
N/A
0.46
0.55
N/A
0.53
0.54
0.53
N/A
0.56
0.55
0.56
0.56
0.58
0.55
0.51
0.56
0.55
Otolith
radius: 30
days old
167.06
162.54
157.38
175.44
168.35
146.64
120.62
N/A
148.35
147.71
159.32
N/A
154.80
172.86
N/A
145.77
178.02
181.89
N/A
152.22
155.45
168.35
151.58
183.18
174.80
176.73
171.80
195.44
Backcalculated
SL: 30 days
old
20.16
18.98
19.80
22.32
19.82
17.68
16.27
N/A
19.34
18.62
20.58
N/A
16.57
20.86
N/A
18.58
18.46
19.95
N/A
19.25
19.13
20.00
18.98
20.71
20.77
20.99
20.74
20.59
Growth
Rate:
first 30
days
0.54
0.50
0.53
0.61
0.53
0.46
0.41
N/A
0.51
0.49
0.55
N/A
0.42
0.56
N/A
0.49
0.48
0.53
N/A
0.51
0.50
0.53
0.50
0.56
0.56
0.57
0.56
0.55
Otolith
radius: 30
days precapture
283.16
320.57
291.54
288.32
345.72
357.98
311.54
N/A
356.69
410.87
392.81
N/A
137.39
116.75
N/A
138.68
172.86
198.66
N/A
183.18
199.31
227.69
197.37
241.23
249.62
270.90
263.16
287.03
Backcalculated
SL: 30 d
pre-capture
33.63
36.53
36.66
36.51
39.88
41.98
40.32
N/A
45.02
49.84
49.71
N/A
14.77
14.37
N/A
17.71
17.95
21.83
N/A
23.16
24.63
26.69
24.55
27.27
29.23
31.64
31.95
30.41
Growth
Rate
last 30
days
0.48
0.45
0.48
0.52
0.44
0.40
0.52
N/A
0.53
0.44
0.51
N/A
0.51
0.55
N/A
0.58
0.60
0.54
N/A
0.63
0.61
0.58
0.65
0.59
0.56
0.48
0.50
0.55
Sample ID
6FF5011
6FF5010
6FF5008
6FF5007
6FF5009
6FF5005
6FF5004
6FF6488
6FF6490
6FF6492
6FF6494
6FF6498
6FF6506
6FF6499
6FF6500
6FF5089
6FF6502
6FF5103
6FF5152
6FF5110
6FF5123
6FF5125
6FF5120
6FF5095
6FF5127
6FF5144
6FF5079
6FF5298
Site
A5
A5
A5
A5
A5
A5
A5
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
A7
AB2
Age
(days)
90
85
103
93
86
101
107
59
56
67
67
70
70
79
83
81
N/A
112
99
105
96
106
121
95
109
117
128
57
Total
Otolith
radius
(µm)
478.59
471.50
499.23
470.85
481.17
542.45
621.78
281.87
284.45
339.27
329.60
367.01
325.73
359.27
373.46
349.59
N/A
503.75
465.69
497.30
425.70
486.33
494.72
470.85
457.95
493.43
534.06
236.72
Otolith
hatch
radius
(µm)
25.80
27.74
24.51
34.83
27.74
29.03
29.03
36.12
30.32
22.58
26.45
21.29
23.22
22.58
27.74
29.67
N/A
19.35
23.22
21.93
27.74
31.61
27.09
25.16
30.96
29.03
25.16
35.48
Absolute
growth
rate
(mm/day)
0.51
0.58
0.51
0.58
0.65
0.60
0.59
0.46
0.52
0.48
0.49
0.50
0.54
0.49
0.48
0.51
N/A
0.42
0.51
0.50
0.55
0.51
0.46
0.60
0.53
0.51
0.49
0.42
Otolith
radius: 30
days old
158.67
194.15
168.35
183.18
181.89
197.37
201.24
149.00
164.48
167.06
143.19
158.03
150.93
152.87
155.45
139.97
N/A
139.32
156.74
138.68
141.26
160.61
156.09
163.19
150.29
152.22
150.93
146.42
Backcalculated
SL: 30 days
old
17.50
22.38
20.06
22.37
23.04
24.00
22.30
16.40
19.31
18.60
16.71
17.84
20.04
19.09
18.78
18.14
N/A
15.64
19.09
16.77
19.12
19.32
19.45
21.65
20.21
19.92
19.57
17.23
Growth
Rate:
first 30
days
0.45
0.61
0.54
0.61
0.63
0.67
0.61
0.41
0.51
0.49
0.42
0.46
0.53
0.50
0.49
0.47
N/A
0.39
0.50
0.43
0.50
0.51
0.51
0.59
0.54
0.53
0.52
0.44
Otolith
radius: 30
days precapture
321.86
328.31
357.33
318.63
321.21
392.81
460.53
144.48
142.55
198.02
173.51
206.40
188.99
227.04
255.42
221.24
N/A
357.98
324.44
355.40
280.58
345.08
353.46
341.85
331.53
368.94
423.12
135.45
Backcalculated
SL: 30 d
pre-capture
34.08
37.19
41.16
39.15
40.24
47.22
49.86
15.91
16.81
21.73
20.01
22.74
24.82
27.68
30.34
28.55
N/A
36.86
38.04
40.48
37.67
41.23
43.08
44.50
44.83
47.92
53.27
15.92
Growth
Rate
last 30
days
0.53
0.53
0.53
0.63
0.66
0.59
0.57
0.50
0.54
0.48
0.57
0.54
0.57
0.51
0.46
0.55
N/A
0.47
0.53
0.52
0.64
0.56
0.56
0.55
0.57
0.54
0.46
0.40
Sample ID
6FF5237
6FF5235
6FF5227
6FF5253
6FF5271
6FF5263
6FF5221
6FF5279
6FF5261
6FF5260
6FF5255
6FF5251
6FF5267
6FF5254
6FF5207
6FF5210
6FF5206
6FF5202
6FF5205
Site
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
AB2
Age
(days)
64
63
73
64
60
66
67
75
75
73
72
74
76
125
121
125
128
133
129
Total
Otolith
radius
(µm)
285.09
294.12
308.31
283.16
305.73
337.34
361.85
322.50
330.24
335.40
339.27
366.36
378.62
497.30
459.24
469.56
516.00
614.04
468.92
Otolith
hatch
radius
(µm)
26.45
29.67
32.90
31.61
30.96
36.12
32.25
35.48
29.67
27.74
27.74
35.48
25.16
25.16
26.45
23.87
29.67
34.19
24.51
Absolute
growth
rate
(mm/day)
0.41
0.44
0.41
0.48
0.55
0.52
0.52
0.48
0.49
0.52
0.54
0.54
0.54
0.42
0.45
0.45
0.47
0.47
0.49
Otolith
radius: 30
days old
149.00
154.16
154.16
144.48
168.35
176.73
192.86
154.80
158.03
163.19
168.35
184.47
172.22
140.61
130.94
139.97
170.93
175.44
147.06
Backcalculated
SL: 30 days
old
16.32
17.18
17.21
17.91
20.50
19.87
21.06
18.97
19.80
20.73
21.60
22.01
21.06
16.96
17.04
18.59
21.43
19.10
21.37
Growth
Rate:
first 30
days
0.41
0.44
0.44
0.46
0.55
0.53
0.57
0.50
0.53
0.56
0.59
0.60
0.57
0.43
0.43
0.49
0.58
0.50
0.58
Otolith
radius: 30
days precapture
162.54
168.35
193.50
158.67
168.35
207.05
223.82
205.11
215.43
212.21
217.37
237.36
241.23
376.04
354.11
353.46
406.35
507.62
376.68
Backcalculated
SL: 30 d
pre-capture
17.68
18.68
21.49
19.66
20.50
23.29
24.34
25.28
26.87
26.78
27.74
28.41
29.06
43.39
44.88
45.41
50.47
54.62
53.92
Growth
Rate
last 30
days
0.41
0.44
0.42
0.51
0.55
0.49
0.49
0.49
0.47
0.51
0.51
0.52
0.53
0.45
0.44
0.49
0.45
0.38
0.44
APPENDIX C
Water quality data obtained from the U.S. Fish and Wildlife Service (2007) for three ponds sampled during this study.
2006
2006
2006
2006
2006
Average Dissolved
Temperature Range
Average Temperature
Salinity Range
Average Salinity
Oxygen*
Pond
(ºC)
(ºC)
(ppt)
(ppt)
(mg/L)
A2W
12.7 - 33.6
21
1.93 - 26.72
17.2
No Data
A7
13.8 - 29.3
21.3
1.81 - 31.47
16.4
4.4
A16
16.2 - 32.6
22.9
0.87 - 21.90
14.2
* Dissolved oxygen values are presented as the 10th percentile of all surface water samples
4.1