Peters and Chigbu: Abundance of juvenile Centropristis striata in Maryland coastal bays 
507 
and was calculated as the number of black sea bass 
caught divided by the number of tows. CPUE was cal¬ 
culated for each year, month, and site, and was used to 
evaluate patterns in temporal and spatial abundance. 
To determine whether any of the abiotic factors re¬ 
corded in the survey influence spatial distribution of 
black sea bass in the MCBs, generalized linear models 
(GLMs) with a quasi-Poisson distribution due to over 
dispersion, were run for each month. The abiotic factors 
used in the GLMs were water temperature, dissolved 
oxygen, salinity, and Secchi disk depth because analy¬ 
ses of variance determined that these factors varied 
between sites and months. Water depth was not used 
in each monthly GLM because it did not vary at sites 
between months. For the monthly models, number of 
fish at each of the 20 sampling sites was used as the 
dependent variable, and abiotic variables were used as 
the predictor variables. A separate regression analysis 
was run with total CPUE at each site (from 1990 to 
2012) as the dependent variable and average depth (in 
meters) at each site (from 1990 to 2012) as the pre¬ 
dictor variable. All statistical analyses were run in R 
statistical software (vers. 3.2.0; R Core Team, 2015). 
Because previous studies found that YOY black sea 
bass enter estuaries from July to September (Able et 
al., 1995), 1 January was determined to be the birth 
date and fish caught during April and May the follow¬ 
ing year were assumed to be age 1 (juveniles) (Able 
et al., 1995). Fish length frequencies were examined 
each month and the 2 standard deviations greater and 
less than the mode were used to distinguish between 
year classes (Gulland and Rosenberg, 1992). Black sea 
bass less than 2 standard deviations of the mode were 
designated as age 0, and those greater than 2 standard 
deviations were considered age 1+. The CPUE of YOY 
fish, used as a recruitment index, was examined in re¬ 
lation to abiotic factors (temperature and salinity), cli¬ 
matic events (annual NAO index, NAO winter, spring, 
and summer indices, ENSO index, ENSO winter and 
spring indices, which were calculated and downloaded 
from the NOAA Climate Prediction Center; website, 
accessed April 2015), and spawning stock biomass of 
black sea bass, the latter of which was provided by the 
Northeast Fisheries Science Center. For this analysis, 
only data from 1990 to 2012 were used because those 
were the years with full records of environmental fac¬ 
tors measured at each site for each month. A GLM with 
a Poisson distribution (R Core Team, 2015) was used 
in a stepwise approach to determine which model and 
variables best predict recruitment of YOY black sea 
bass. The model with the lowest Akaike information 
criterion (AIC) value was chosen as the best indicator 
for predicting recruitment. 
Growth rate of juvenile (age 1) black sea bass was 
also assessed for years when, at least, 5 black sea bass 
individuals were captured in May, and in September. 
Length of black sea bass (TL in millimeters) in May 
was averaged and subtracted from the average length 
in September, and the value was then divided by the 
total number of days over that time period (May-Sep- 
tember) (n=152) to estimate growth rates (mm per day) 
for each year (Tucker, 2000). These values were then 
averaged together to estimate absolute growth rate of 
juvenile black sea bass in the MCBs. Regression analy¬ 
sis was then performed to determine whether growth 
rates were related to abundance or temperature. 
Results 
Size composition and growth of black sea bass in 
Maryland coastal bays 
The length-frequency distributions of fish collected 
each month (April-October) from 1989 to 2013 are pre¬ 
sented in Figure 2. Trawl catches consisted mostly of 
age-1 fish; however a few age-0 black sea bass were 
caught. Black sea bass began to enter MCBs in April 
in low numbers at sizes ranging from 45 to 95 mm TL. 
In June they began to enter in higher numbers at sizes 
of 27 to 205 mm. By October, they had attained sizes 
of about 58-240 mm TL. Samples collected in April 
showed the presence of fish that were approximately 1 
year old that had entered the MCBs from the coastal 
ocean. The size range of these fish was similar to the 
size range of YOY fish captured in October of the previ¬ 
ous year, suggesting there was minimal growth during 
the winter. 
Growth rate of age-1 black sea bass from May to 
September was fastest in 1992 (0.72 mm TL/day) and 
slowest in 1990 and 2002 (0.46 mm TL/day) and av¬ 
eraged 0.58 mm TL/day. Growth rate had no correla¬ 
tion (P>0.05) with average temperature and abundance 
data for each year. 
Interannual variation in abundance of black sea bass 
CPUE of age-1 black sea bass showed no significant 
increasing or decreasing trend over time (Fig. 3A) but 
was characterized by low values in 1989, 1993, 1996, 
1998, 2004 and 2005 and by the highest CPUE in 2008. 
In contrast, CPUE of age-0 fish showed a significant 
annual increasing trend (P<0.05) during 1989-2013, 
although it also exhibited fluctuations in relative abun¬ 
dance between years. The largest CPUE occurred in 
2002, 2008, and 2013, whereas the lowest CPUE was 
observed in 1989, 1990, 1993 and 1996 (Fig. 3B). The 
patterns in CPUE of age-1 and age-0 black sea bass 
over time were somewhat similar, with peak abundanc¬ 
es occurring in similar years. 
Spatial distribution of black sea bass in Maryland coastal 
bays 
On average from 1989 to 2013, mean CPUE was rela¬ 
tively low at a site (site 5) in the St. Martin River, and 
at 4 sites (sites 13, 14, 15, 18) in the central part of 
Chincoteague Bay (Fig. 4). In May, black sea bass were 
most abundant in the southernmost site located by the 
Maryland/Virginia border in Chincoteague Bay (Fig. 
