Schlick and de Mutsert: Growth of adult river herring that spawn in tributaries of the Potomac River 
67 
back herring in this study than for those in previous 
studies (Messieh, 1977; ASMFC, 2012), differences that 
could be due to a lack of smaller individuals in the 
previous studies. For this study, alewife ranging in age 
from 2 to 7 years and blueback herring ranging in age 
from 2 to 6 years were used; however, in both previ¬ 
ous studies, river herring ranged from 3 years to more 
than 9 years (Messieh, 1977; ASMFC, 2012). When 
age-2 individuals were removed from the analysis in 
this study, K increased from 0.179 to 0.243 mm/year for 
alewife (both sexes combined) and from 0.525 to 0.697 
mm/year for blueback herring (both sexes combined). 
One hypothesis for why age-2 individuals were avail¬ 
able in this study and not in previous studies is that 
the populations could have spawned at earlier ages 
in this study because of years of overfishing, which 
has been documented in Atlantic cod (Gadus morhua ) 
(Trippel, 1995), several Pacific salmon species (Ricker, 
1981), and numerous other fish species (Darimont et 
al., 2009). Fishermen target large individuals within 
a population. With years of fishing pressure, a popula¬ 
tion adapts to spawning as smaller, younger individu¬ 
als because individual fish that can spawn at smaller 
sizes are more likely to successfully spawn than slower 
maturing individuals (Ricker, 1981; Thorpe, 1993). A 
change in maturity schedules is important to document 
for estimating potential recruitment of a population 
and should be examined further. 
The lack of ages from 0 to 1 years and ages >7 years 
also could have contributed to the similarities between 
the different growth models tested within this study 
because the parameters in each model are correlated to 
each other (Hilborn and Walters, 1992; Campana, 2001; 
Allen and Gwinn, 2013). Missing younger and older 
fish of a population can make model estimation diffi¬ 
cult because these 2 ends of a population can influence 
growth more than the part of a population at median 
ages (Campana, 2001). The younger and older ends of 
a population can also be the most difficult to obtain 
because of increased mortality for older individuals, 
anadromous species being collected during spawning 
runs only (as in this study), or age estimation being 
hardest for these categories (Campana, 2001; ASMFC, 
2012; ASMFC 8 ). Even in this study, when scales were 
used for aging, younger individuals were overaged and 
older individuals were underaged. 
The ASMFC River Herring Ageing Workshop found 
that participating state agencies also overaged young¬ 
er fish and underaged older fish when using scales 
(ASMFC 8 ). Many agencies base the methods for using 
scales to age river herring on the methods developed 
by Cating (1953) for American shad. Marcy (1969) de¬ 
veloped transverse groove counts specific to river her¬ 
ring captured in Connecticut based on Cating’s (1953) 
method. However, this method does not take geographi¬ 
cal location into account as a factor on fish growth and 
scale formation (Duffy et al., 2011). The use of trans¬ 
verse grooves, outlined by Cating (1953) and Marcy 
(1969), to determine location of freshwater zones and 
the first 3 years of age resulted in inconsistencies be¬ 
tween ages in different geographical regions within the 
distribution of American shad (Duffy et al., 2011). Age 
validation with known-age river herring needs to be 
completed for each geographical region for the analysis 
of scales to be reliable as an aging technique for river 
herring (ASMFC 8 ). The ASMFC River Herring Ageing 
Workshop has developed protocols to standardize aging 
techniques and has started a reference collection for 
aging structures from different rivers throughout the 
East Coast of the United States (ASMFC 8 ). 
Using data sets with biased ages can result in poor 
population modeling and conflicting strategies for pop¬ 
ulation management (Beamish and McFarlane, 1987; 
Bertignac and de Pontual, 2007; Katsanevakis and 
Maravelias, 2008; Tyszko and Pritt, 2017; Porta et al., 
2018). Age biases can influence stock assessment by 
overestimating or underestimating growth or mortality, 
affecting policy decisions about a population (Beamish 
and McFarlane, 1987; Katsanevakis and Maravelias, 
2008). Alewife in this study were more likely to be und¬ 
eraged by the use of scales, increasing the estimates for 
growth and mortality rates (Beamish and McFarlane, 
1987). Management strategies for species that are not 
growing as fast as models indicate can lead to overfish¬ 
ing practices. Conversely, blueback herring were more 
often overaged when scales were used in this study. 
Therefore, growth and mortality predictions could be 
lower than real levels, possibly limiting the ability of 
management agencies to track how reactive a popu¬ 
lation is to fishing changes (Tyszko and Pritt, 2017). 
The ages presented here for this study are considered 
precise between readers on the basis of the ACV, but 
there was no way to determine accuracy without the 
use of known-age individuals. Currently, no known-age 
samples for either species of river herring are available 
for use (ASMFC 8 ). 
This study reveals the importance of validating ag¬ 
ing techniques for species of river herring, as well as of 
continuing to monitor the ages and individual growth 
rates of the populations of alewife and blueback her¬ 
ring. Many management agencies are calling for an 
increase in run counts and abundance estimates of 
these populations (ASMFC, 2012, 2017). Documenting 
abundances of river herring is only a small component 
in understanding a population that may have dramati¬ 
cally changed over decades because of overfishing and 
degraded habitats. The additional age and growth es¬ 
timates completed in this study provide information 
needed in the ongoing efforts to restore the once great 
fisheries that targeted these species. 
Acknowledgments 
Comments of 2 anonymous reviewers have improved 
the manuscript. We would like to acknowledge R. Jones 
for conception of the monitoring program and previous 
co-principal investigator R. Kraus for collection of the 
samples before 2011. We would like to thank M. Og- 
burn, C. Ahn, and K. Lewis for their insight into this 
