68 
Fishery Bulletin 11 7(1-2) 
project. Logistical support was provided by J. van der 
Ham and B. Bachman. This study was funded by Fair¬ 
fax County through its Department of Public Works 
and Environmental Services and by Alexandria Renew 
Enterprises. Mention of trade names or commercial 
companies does not imply endorsement by the Potomac 
Environmental Research and Education Center, George 
Mason University. 
Literature cited 
Akaike, H. 
1974. A new look at the statistical model identifica¬ 
tion. IEEE Trans. Auto. Control 19:716-723. 
Allen, M. S., and D. Gwinn. 
2013. Population models for assessment and management 
of inland striped bass fisheries. Am. Fish. Soc. Symp. 
80:351-364. 
ASMFC (Atlantic States Marine Fisheries Commission). 
2012. River herring benchmark stock assessment. Stock 
Assess. Rep. 12-02. 2 vols. Atl. States Mar. Fish. 
Comm., Washington, D.C. [Available from website.) 
2017. River herring stock assessment update. 2 vols. Atl. 
States Mar. Fish. Comm., Washington, D.C. [Available 
from website.] 
Beamish, R. J., and G. A. McFarlane. 
1987. Current trends in age determination methodolo¬ 
gy. In Age and growth of fish (R. C. Summerfelt and G. 
E. Hall, eds.), p. 15-42. Iowa State Univ. Press, Ames, 
IA. 
Bertignac, M., and H. de Pontual. 
2007. Consequences of bias in age estimation on assess¬ 
ment of the northern stock of European hake ( Merluccius 
merluccius) and on management advice. ICES J. Mar. 
Sci. 64:981-988. 
Besler, D. A. 
1999. Utility of scales and whole otoliths for aging large- 
mouth bass in North Carolina. Proc. Annu. Conf. SEA- 
WFA 53:119-129. 
Burnham, K. P, and D. R. Anderson. 
2002. Model selection and multimodel inference: a prac¬ 
tical information-theoretic approach, 488 p. Springer- 
Verlag, New York. 
Campana, S. E. 
2001. Accuracy, precision and quality control in age deter¬ 
mination, including a review of the use and abuse of age 
validation methods. J. Fish Biol. 59:197-242. 
Campana, S. E., and J. D. Neilson. 
1985. Microstructure of fish otoliths. Can. J. Fish. 
Aquat. Sci. 42:1014-1032. 
Campana, S. E., M. C. Annand, and J. I. McMillan. 
1995. Graphical and statistical methods for determining 
the consistency of age determinations. Trans. Am. Fish. 
Soc. 124:131-138. 
Cating, J. P. 
1953. Determining age of Atlantic shad from their 
scales. Fish. Bull. 85:187-199. 
Cerrato, R. M. 
1990. Interpretable statistical tests for growth compari¬ 
sons using parameters in the von Bertalanffy equa¬ 
tion. Can. J. Fish. Aquat. Sci. 47:1416-1426. 
Darimont, C. T., S. M. Carlson, M. T. Kinnison, P. C. Paquet, 
T. E. Reimchen, and C. C. Wilmers. 
2009. Human predators outpace other agents of trait 
change in the wild. Proc. Natl. Acad. Sci. U.S.A. 
106:952-954. 
Duffy, W. J., R. S. McBride, S. X. Cadrin, and K. Oliveira. 
2011. Is Cating’s method of transverse groove counts to an¬ 
nuli applicable for all stocks of American shad? Trans. 
Am. Fish. Soc. 140:1023-1034. 
Dunton, K. J., A. Jordaan, D. O. Conover, K. A. McKown, L. 
A. Bonacci, and M. G. Frisk. 
2015. Marine distribution and habitat use of Atlantic 
sturgeon in New York lead to fisheries interactions and 
bycatch. Mar. Coast. Fish. 7:18-32. 
Evans, G. T., and J. M. Hoenig. 
1998. Testing and viewing symmetry in contingency ta¬ 
bles, with application to readers of fish ages. Biomet¬ 
rics 54:620-629. 
Francis, R. I. C. C. 
1988. Are growth parameters estimated from tagging and 
age-length data comparable? Can. J. Fish. Aquat. Sci. 
45:936-942. 
Gompertz, B. 
1825. XXIV. On the nature of the function expressive of 
the law of human mortality, and on a new mode of de¬ 
termining the value of life contingencies. Philos. Trans. 
R. Soc. Lond. 115:513-583. 
Haddon, M. 
2011. Modelling and quantitative methods in fisheries, 2 nd 
ed., 465 p. CRC Press, Boca Raton, FL. 
Heino, M. 
1998. Management of evolving fish stocks. Can. J. Fish. 
Aquat. Sci. 55:1971-1982. 
Heino, M., and O. R. Godo. 
2002. Fisheries-induced selection pressures in the context 
of sustainable fisheries. Bull. Mar. Sci. 70:639-656. 
Hightower, J. E., A. M. Wicker, and K. M. Endres. 
1996. Historical trends in abundance of American shad 
and river herring in Albemarle Sound, North Caroli¬ 
na. North Am. J. Fish. Manage. 16:257-271. 
Hilborn, R., and C. J. Walters (eds.). 
1992. Quantitative fisheries stock assessment: choice, 
dynamics and uncertainty, 570 p. Chapman and Hall, 
London. 
Hilborn, R., and M. Mangel. 
1997. The ecological detective: confronting models with 
data, 336 p. Princeton Univ. Press, Princeton, NJ. 
Jessop, B. M. 
1994. Homing of alewives (Alosa pseudoharengus ) and 
blueback herring (A. aestivalis) to and within the Saint 
John River, New Brunswick, as indicated by tagging 
data. Can. Tech. Rep. Fish. Aquat. Sci. 2015, 22 p. 
Jones, P. W., F. D. Martin, and J. D. Hardy Jr. 
1978. Development of fishes of the mid-Atlantic Bight: 
an atlas of egg, larval, and juvenile stages. Vol. 1: 
Acipenseridae through Ictaluridae, 366 p. Biol. Serv. 
Prog., U.S. Fish Wildl. Serv., Washington, D.C. 
Jones, R. C., D. P. Kelso, and E. Schaeffer. 
2008. Spatial and seasonal patterns in water quality in 
an embayment-mainstem reach of the tidal freshwa¬ 
ter Potomac River, USA: a multiyear study. Environ. 
Monit. Assess. 147:351-375. 
Katsanevakis, S., and C. D. Maravelias. 
2008. Modelling fish growth: multi-model inference as a 
better alternative to a priori using von Bertalanffy equa¬ 
tion. Fish Fish. 9:178-187. 
Klauda, R. J., S. A. Fischer, L. W. Hall Jr., and J. A. Sullivan. 
1991. Alewife and blueback herring; Alosa pseudoharen- 
