Abstract — Stable isotope (SI) values 
of carbon (5 13 C) and nitrogen (5 15 N) 
are useful for determining the tro- 
phic connectivity between species 
within an ecosystem, but interpreta- 
tion of these data involves important 
assumptions about sources of intra- 
population variability. We compared 
intrapopulation variability in 5 13 C 
and 8 15 N for an estuarine omnivore, 
Spotted Seatrout ( Cy noscion nebulo- 
sus), to test assumptions and assess 
the utility of SI analysis for delinea- 
tion of the connectivity of this spe- 
cies with other species in estuarine 
food webs. Both 8 13 C and § 15 N val- 
ues showed patterns of enrichment 
in fish caught from coastal to off- 
shore sites and as a function of fish 
size. Results for § 13 C were consistent 
in liver and muscle tissue, but liver 
8 15 N showed a negative bias when 
compared with muscle that increased 
with absolute 5 15 N value. Natural 
variability in both isotopes was 5-10 
times higher than that observed in 
laboratory populations, indicating 
that environmentally driven intra- 
population variability is detectable 
particularly after individual bias is 
removed through sample pooling. 
These results corroborate the utility 
of SI analysis for examination of the 
position of Spotted Seatrout in an 
estuarine food web. On the basis of 
these results, we conclude that inter- 
pretation of SI data in fishes should 
account for measurable and ecologi- 
cally relevant intrapopulation vari- 
ability for each species and system 
on a case by case basis. 
Manuscript submitted 17 January 2012. 
Manuscript accepted 4 January 2013. 
Fish. Bull. 111:111-121 (2013). 
doi:10.7755/FB.l 11.2.1 
The views and opinions expressed 
or implied in this article are those of the 
author (or authors) and do not necessar- 
ily reflect the position of the National 
Marine Fisheries Service, NOAA. 
Quantifying intrapopulation variability in 
stable isotope data for Spotted Seatrout 
( Cy noscion nebulosus ) 
Richard S. Fulford (contact author) 
Kevin Dillon 
Email address for contact author: fulford.richard@epa.gov 
Department of Coastal Sciences 
University of Southern Mississippi 
Gulf Coast Research Laboratory 
703 East Beach Road 
Ocean Springs, Mississippi 39564 
The mandate for management of 
fisheries has shifted toward a focus 
on ecosystem-based fisheries man- 
agement (EFM) (Brodziak and Link, 
2002). The EFM approach differs 
from historical single-species man- 
agement in that EFM acknowledges 
the linkages between ecosystem com- 
ponents as well as the spatial and 
temporal variability in those linkag- 
es (Arkema et ah, 2006; Christensen 
et al., 1996; NRC 1999; Thomas and 
Huke, 1996). The EFM paradigm has 
great potential to improve the abil- 
ity of managers to accurately pre- 
dict and appropriately respond to 
the impacts of multiple stressors on 
exploited fish populations. However, 
the data needs of EFM are high, and 
the acquisition of these data has 
generally lagged behind development 
of EFM theory (Dame and Christian, 
2006). 
Quantification of trophic connec- 
tions among species is an important 
precursor for the shift to EFM be- 
cause such data support the devel- 
opment of food-web network models 
that allow fishing pressure to be an- 
alyzed in an ecological context with 
other sources of mortality (Chris- 
tensen and Pauly, 2004). Quantita- 
tive techniques, like stable isotope 
(SI) analysis, have been widely ap- 
plied to fish populations as a method 
for quantification of the trophic links 
between predator and prey (Power et 
ah, 2002; Rooker et ah, 2006). Sta- 
ble isotope data are well suited to 
the development of network models 
of food webs because they are time- 
integrated and provide data on the 
relative importance of entire trophic 
pathways down to sources of primary 
production (Peterson and Fry, 1987). 
Yet, the interpretation of stable iso- 
tope data requires several important 
assumptions, and these assumptions 
should be carefully analyzed if re- 
sults may affect management deci- 
sion making. 
Key assumptions made in inter- 
preting stable isotope data involve 
the sources of variability in isoto- 
pic values for a given population 
(Peterson and Fry, 1987). Popula- 
tion variability integrates several 
sources of variability, including en- 
vironmental influences (Barnes et 
al., 2008), differences in individual 
behavior (Sweeting et al., 2005), 
ontogeny, growth, and tissue turn- 
over rates (Herzka, 2005; Perga and 
Gerdeaux, 2005), and prey availabil- 
ity and feeding success (Sweeting 
et al., 2005). Partitioning out these 
sources of variability, particularly 
for an omnivorous consumer in an 
estuarine ecosystem, can be com- 
plex. In particular, quantification of 
intrapopulation variability in isotope 
data (e.g., variability due to differ- 
ences in individual behavior or tis- 
sue turnover rates) is important 
for separating this variability from 
changes that are ecologically impor- 
tant (e.g., changes due to ontogeny 
or the environment). 
