Jung and Houde: Recruitment and spawning-stock biomass distribution of Anchoa mitchilli 



67 



Because N mvT of bay anchovy was highly variable, even 

 at stations on the same sampling transect, and a mixed 

 model (SAS version 6.12, SAS Inst. Inc., Cary, NC) includ- 

 ing spatial covariance ( variogram ) did not significantly im- 

 prove precision in annual, seasonal, and regional means or 

 differences of N MWT , a stratified sampling design ( Steel and 

 Torrie, 1980), i.e. stratum = region, was adopted. Based on 

 the mean effective water volume (=sxV MWJ , ), we estimated 

 regional "absolute" abundance and biomass (number and 

 wet weight) and related standard errors of the linear com- 

 bination by regional subvolumes (Samuels, 1989) of bay 

 anchovy >21 mm TL for all MWT surveys from 1995 to 

 2000 by multiplying regional mean MWT catch by V r /989, 

 where V r represents the water volume (m 3 ) in each bay 

 region (Cronin, 1971): 



N !olal =(N^V l+ N n 



V,„ + N. 



Vj/(sxV MWT )xV lotal 



SE N =Sc N jVr/n,+V*/n n 



+v:?/n„ 



where N lotal 



v„ v m , v u 



SE X 



Sc N = 



baywide absolute abundance; 



mean values of N mvT for the lower (1), 



middle (m), and upper (u) bay; 



bay subvolumes for the lower (1), middle 



(m), and upper (u) bay (from Cronin, 1971), 



V, = 26.7 x 10 9 m 3 , V m = 16.8 x 10 9 m 3 , V„ = 



8.7 x 10 9 m 3 , V„„„, = V, + V m + V„ =52.1 x 



10 9 m 3 ; 



standard error of N lolal ; 



number of midwater trawl stations for the 



lower (1), middle (m), and upper (u) bay; 



pooled standard deviation of N MWT = 



square root of mean squares within 



groups in analysis of variance table = 



t/< SS, + SS m + SS„ ) / ( n, lMl -3i, where SS,, SS m , 



SS tl = sum of squares of N MWT for the 



lower (1), middle (m), and upper (u) bay, 



and "total = n l + n m + n u- 



Environmental factors 



Depth profiles of temperature, salinity, and dissolved 

 oxygen ( DO ) concentration were determined from conduc- 

 tivity-temperature-depth ( CTD ) casts at sampling stations. 

 DO data were adjusted by calibrating against Winkler 

 titration data from water samples collected in Niskin bot- 

 tles deployed with the CTD cast. However, DO data from 

 the CTD could not be adjusted for the 1999 summer and all 

 calendar year 2000 cruises because Winkler titrations were 

 not conducted. To estimate regional means for the water 

 column, we averaged temperature, salinity, and DO values 

 by integrating the observed values with respect to depth, 

 after dividing the water column into "above pycnocline" and 

 "subpycnocline" layers. 



Ontogenetic migration 



We analyzed length-frequency distributions along the 

 south-north axis of the bay (i.e. by latitude) to delineate 



possible ontogenetic migrations of YOY and adult bay 

 anchovy. To parameterize the distribution of YOY and 

 adult abundance and biomass, we estimated the biomass- 

 weighted mean latitudes of occurrence for each length class 

 (3-mm interval). 



l b.i = 2_, B kjL k /2jB tl , 



where L B , = biomass-weighted mean latitude of a length 

 class, /; 

 L k = latitude of the station, k; and 

 B = biomass (g, wet weight) per 20-min tow. 



We devised a metric to parameterize the location of bay 

 anchovy SSB. We assumed that the baseline boundary for 

 SSB distribution during the spring was at the mouth of 

 the bay (37°00'N). Then, the upbay difference between 

 biomass-weighted mean latitude of SSB (in decimal units) 

 in Jun-August and the baseline for SSB during the spring 

 lAL i was calculated: 



SL 



biomass-weighted mean latitude of 

 SSB in June - August 



-37.00. 



Recruitment model 



As an exploratory step, a correlation analysis was under- 

 taken to examine the relationships between bay anchovy 

 SSB, migration patterns, and recruitment levels with 

 respect to regional and depth-layer-specific mean tempera- 

 ture, mean salinity, mean DO, their gradients, and monthly 

 mean freshwater flow from the Susquehanna River. Cross- 

 correlations revealed that SSB migration pattern {AD, 

 regional mean DO concentrations, and October YOY 

 recruitment level were closely correlated. Regional mean 

 DO concentration provided the best fit to YOY recruitment 

 level in October when baywide SSB also was included as 

 an explanatory variable in multiple regressions. However, 

 because there is uncertainty in the uncalibrated DO 

 measurements in 1999 and 2000. we did not use regional 

 mean DO in our recruitment model. Instead, we developed 

 a modified Ricker-type stock-recruitment model (Ricker, 

 1975) that included AL as an explanatory variable: 



R x = a S exp (-/3j S - /i, AL) + e (modified Ricker model ) 



where 



R, 



recruitment level = October YOY abun- 

 dance in each year ( 1995-2000); 

 y; a, l\ and p.-, = regression coefficients; 



S = estimated baywide SSB (male-i- female) in 



metric tons for April-May; and 

 £ = the error term. 



In this model, if AL is held constant, R s . is maximum at S = 

 l//3j. Although no abiotic factor was included explicitly in 

 the model, AL is strongly correlated with regional mean DO 

 and serves as a proxy for it. For the modified Ricker model, 

 collinearity, and jackknife influence diagnostic tools were 



