Dennis et al.: Using otolith chronologies to identify extrinsic drivers of growth 
143 
which included no environmental variables (Table 3; Suppl. 
Table 3) (online only). When the difference in AIC, between 
the current and top models (AAIC,) indicated a high degree 
of support for numerous models (AAIC, <2), AIC. weight was 
examined to determine which model had the highest like- 
lihood (Table 3). Common jack mackerel from KI were the 
only fish for which growth was associated with environmen- 
tal conditions. The model that included SST had the highest 
AIC, weight (0.96; Table 3, Suppl. Table 3 [online only]), with 
predicted growth increasing 11.47% for every 1°C increase 
in SST in the range of 16.2—17.7°C (Fig. 6, A and B). 
Discussion 
In this study, the von Bertalanffy growth function and 
mixed-effects modeling were used to explore differences in 
the growth of small pelagic fishes between 2 temperate 
regions in southern Australia. Growth chronologies were 
produced from otolith increments of common jack mack- 
erel and redbait caught off KI in South Australia and off 
southern NSW to explore regional differences in growth 
rates and to investigate the influence of local environmen- 
tal conditions on the growth rates of both species in each 
region. This study is one of the few to apply growth chronol- 
ogy analyses to small pelagic fishes, and results indicate 
that common jack mackerel and redbait had lower growth 
synchrony than site-attached benthic or benthopelagic 
species. Consistent regional differences were also identi- 
fied for both species, with fish from KI growing to greater 
lengths than fish from southern NSW. Age was the key 
intrinsic driver of growth detected across models, and 
each of the environmental factors selected for this study 
had limited influence on growth. Sea-surface temperature 
correlated with growth of common jack mackerel from KI, 
with no other correlations evident for the other species 
and regions. 
Temporal growth synchrony (estimated with the inter- 
class correlation coefficient) in both species and regions 
(0.17-3.50%) was low compared with that from other 
studies. For example, growth synchrony in species from 
similar regions has been reported as 2.0—21.6% in tiger 
flathead (Platycephalus richardsoni) (Morrongiello and 
Thresher, 2015), 0.4-13.7% in snapper (Martino et al., 
2019), 0.1-15.0% in ocean perches (Helicolenus spp.) 
(Grammer et al., 2017), and 3.0-13.8% in black bream 
(Doubleday et al., 2015). The movement patterns of com- 
mon jack mackerel and redbait may explain the low 
growth synchrony observed in our study. Results of stud- 
ies on the reproductive biology of both species indicate 
frequent movements (Marshall et al., 1993; Welsford and 
Lyle, 2003; Ewing and Lyle, 2009), which expose them to 
a broad range of physical and biological conditions. In 
contrast, species that have previously been determined 
to have growth synchrony are mostly benthic species that 
tend to move over relatively small distances. 
Table 3 
Results from the full-fixed and intrinsic model fitted with local data for environmental variables, sea-surface temperature (SST) 
and chlorophyll-a (Chl-a) concentration, and used to examine growth of common jack mackerel (Trachurus declivis) and redbait 
(Emmelichthys nitidus) collected off Kangaroo Island (KI) and New South Wales (NSW) in Australia between 2014 and 2016. 
Models included the base model (full-fixed and intrinsic model), with values provided in the rows labeled Growth, and the base 
model fitted with SST or Chl-a concentration. Degrees of freedom (df), Akaike information criterion corrected for small sample size 
(AIC,), the difference in AIC, between the current and top models (AAIC,), the proportion of the total predictive power of the model 
set (AIC, weight [AIC.Wt]), log likelihood (LL), conditional coefficient of multiple determination (R*,), and marginal R? (R?,,) are 
presented for each model. Values of R”, and R”,, were calculated with the restricted maximum likelihood estimates of error. The 
top-ranked model for each species in each region is indicated with an asterisk (*). 
Trachurus declivis 
KI 
Model df AIC, 
AAIC, AIC,Wt LL 
Growth 9.00 124.65 9.12 0.01 
+SST* 10.00 115.53 0.00 0.96 
+Chl-a 10.00 122.31 6.78 0.03 
-53.19 0.50 0.72 
-47.59 0.54 0.73 
-50.99 0.52 0.72 
Model df AIC 
Growth* 8.00 
+SST 9.00 
+Chl-a 9.00 
NSW 
. AAIC, AIC.Wt LL 
43.34 0.00 0.48 
44.00 0.66 0.35 
45.43 2.09 0.17 
-13.48 0.61 0.73 
-12.76 0.62 0.74 
-13.47 0.61 0.73 
Emmelichthys nitidus 
KI 
Model df AIC, AAIC, AIC,Wt LL 
Growth* 8.00 
+SST 9.00 
+Chl-a 9.00 
45.84 0.00 0.52 
47.80 1.96 0.19 
47.01 1.17 0.29 
-14.49 
-14.36 
-13.97 
Model df AIC 
Growth* 9.00 250.18 0.00 0.42 
+SST 
+Chl-a 
NSW 
AAIC, AIC.Wt LL 
Cc 
—115.87 
—114.95 
-115.49 
10.00 250.44 0.26 0.37 
10.00 251.53 1.35 0.21 
