Evaluation of Three Aging Techniques and Back-calculated
Growth for Introduced Blue Catfish from Lake Oconee,
Georgia
Michael D. Homer Jr., James T. Peterson, and Cecil A. Jennings
Southeastern Naturalist, Volume 14, Issue 4 (2015): 740–756
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Southeastern Naturalist
M.D. Homer Jr., J.T. Peterson, and C.A. Jennings
2015 Vol. 14, No. 4
740
2015 SOUTHEASTERN NATURALIST 14(4):740–756
Evaluation of Three Aging Techniques and Back-calculated
Growth for Introduced Blue Catfish from Lake Oconee,
Georgia
Michael D. Homer Jr.1,2, James T. Peterson3, and Cecil A. Jennings4,*
Abstract - Back-calculation of length-at-age from otoliths and spines is a common technique
employed in fisheries biology, but few studies have compared the precision of data
collected with this method for catfish populations. We compared precision of back-calculated
lengths-at-age for an introduced Ictalurus furcatus (Blue Catfish) population among 3
commonly used cross-sectioning techniques. We used gillnets to collect Blue Catfish (n =
153) from Lake Oconee, GA. We estimated ages from a basal recess, articulating process,
and otolith cross-section from each fish. We employed the Frasier-Lee method to backcalculate
length-at-age for each fish, and compared the precision of back-calculated lengths
among techniques using hierarchical linear models. Precision in age assignments was highest
for otoliths (83.5%) and lowest for basal recesses (71.4%). Back-calculated lengths
were variable among fish ages 1–3 for the techniques compared; otoliths and basal recesses
yielded variable lengths at age 8. We concluded that otoliths and articulating processes are
adequate for age estimation of Blue Catfish.
Introduction
Use of precise and accurate age and growth data are crucial for proper management
of catfish populations. These data allow managers of catfish populations to
determine population size and age structure and elucidate trends in growth to assess
population responses to management and environmental changes (Sakaris et
al. 2006). Age and growth data have been used to infer catfish species interactions
within fish communities (Bonvechio et al. 2009, Homer and Jennings 2011, Kwak
et al. 2006). Age and growth data also allow fisheries biologists to predict habitat
suitability (Kwak et al. 2006), as well as to assess population status (Boxrucker and
Kuklinski 2006, Grist 2002, Homer and Jennings 201 1, Kwak et al. 2006).
Pectoral spines are the main structures that had traditionally been used to estimate
catfish age and growth, but their use has become less common among fisheries biologists
(Buckmeier et al. 2002, Jenkins 1956, Marzolf 1955, Michaletz et al. 2009,
Nash and Irwin 1999, Sneed 1951). Studies have demonstrated that earliest annuli
1Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and
Natural Resources, The University of Georgia, 180 East Green Street, Athens, GA 30602.
2Current address - Inland Fisheries Division - Management District 1B, Texax Parks and
Wildlife Department, 5325 Noirth 3rd Street, Abilene, TX 79603. 3US Geological Survey,
Oregon Cooperative Fish and Wildlife Research Unit, Oregon State University, 104 Nash
Hall, Corvallis, OR 97331. 4US Geological Survey, Georgia Cooperative Fish and Wildlife
Research Unit, Warnell School of Forestry and Natural Resources, The University of Georgia,
180 East Green Street, Athens, GA 30602. *Corresponding author - jennings@uga.edu.
Manuscript Editor: Lance Williams
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2015 Vol. 14, No. 4
in catfish pectoral spines are eliminated by the expansion of the central lumen as the
fish grow, resulting in decreased ability to interpret annuli of older fish and biased
age-estimates (Buckmeier et al. 2002, Mayhew 1969, Muncy 1959). The articulating
process of Pylodictis olivaris (Rafinesque) (Flathead Catfish) spines (Turner
1982) and Ictalurus punctatus (Rafinesque) (Channel Catfish) spines (Buckmeier
et al. 2002) yielded more precise assignments than basal recesses because they retained
more annuli throughout a fish’s life and were unaffected by expansion of the
central lumen.
Although collection of lapilli (previously described as sagittal otoliths; Long
and Stewart 2010) is lethal to fish, fisheries biologists more frequently use them to
age fish because they yield more precise estimates than pectoral spines (Maceina et
al. 2007, Olive et al. 2011). Multiple studies have documented greater precision of
otolith-based age assignments compared to pectoral spine-based age assignments for
various catfishes such as Flathead Catfish (Nash and Irwin 1999, Turner 1982) and
Channel Catfish (Buckmeier et al. 2002, Prentice and Whiteside 1974). However, Colombo
et al. (2010) did not find differences between the precision of otolith-based age
estimates and articulating process-based age estimates for Channel Catfish. Further,
otoliths and pectoral spines have only been validated for estimating ages of Channel
Catfish ranging from ages 1 to 4 (Buckmeier et al. 2002).
Back-calculation of length-at-age from otoliths and spines is a common technique
in fisheries that can be useful for predicting growth histories of individual
fish (Michaletz et al. 2009). Few studies have validated and compared the precision
of back calculations of growth for catfish populations. Michaletz et al. (2009)
compared and validated incremental growth for Channel Catfish and found that
spine-based estimates of incremental growth were more accurate than otolith-based
estimates when increments were measured along a lateral radius. Studies comparing
the precision of age and growth estimates for Ictalurus furcatus (LeSueur)
(Blue Catfish) based on pectoral spines and otoliths are limited. Olive et al. (2011)
compared precision of age estimates from pectoral spines and otoliths collected
from 4 native and 2 introduced populations of Blue Catfish and found that age and
growth rate estimated from both structures were the same with ≥80% probability
for fish up to age 4 with average growth, age 5 with slow growth, and age 2 with
rapid growth. However, none of these studies quantified the variability of estimated
growth among individual fish or compared that variation among methods. This
analysis is essential because among-fish variability, if ignored, can result in biased
estimates of precision and misinterpretation of the importance of differences among
aging techniques.
In this study, we used otoliths, pectoral-spine basal recesses, and articulating
processes to estimate fish ages and back-calculate lengths-at-age for a recently established
population of Blue Catfish in Lake Oconee, GA. We evaluated precision
of age estimates and back-calculated lengths among the structures and crosssectioning
techniques by use of hierarchical linear models to account for inherent
bias that may be associated with each technique as well as to determine whether
estimated length-at-age for a fish would differ depending on the technique used.
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2015 Vol. 14, No. 4
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We also estimated variation in incremental growth among fish and structures. The
primary objectives of this study were to (1) assess age and growth for a young,
introduced population of Blue Catfish by using techniques commonly employed
for estimating ages of catfishes, and (2) compare the precision of data among the 3
techniques. Our data and the conclusions from this study can be used by fisheries
biologists who need to choose a suitable technique for obtaining age and growth
information for young, introduced ictalurid catfish populations.
Methods
We conducted our study on a recently established population of B lue Catfish in
Lake Oconee, which is a 7677-ha reservoir on the Oconee and Appalachee rivers
in East Central Georgia (Fig. 1; GPC 2009). Lake Oconee is impounded by Wallace
Dam, a hydroelectric facility owned and operated by GPC, and there are many
private and commercial residential developments along its shoreline. The lake
supports popular recreational and commercial fisheries. The Georgia Department
of Natural Resources (GADNR) established 12 standardized sampling stations
throughout the reservoir (Fig. 1) and has used them since 1989 to conduct an annual
fisheries survey during winter. Blue Catfish were first discovered in the lake during
the 1998 GADNR gill-netting survey (Homer and Jennings 201 1).
During December 2008, we collected Blue Catfish by deploying experimental
gill nets (20.4-m panels; 1.9-, 3.8-, and 4.5-cm-bar mesh) at the 12 GADNR
standardized stations at Lake Oconee. In January 2009, we deployed 4 additional
experimental gill nets at 4 of the standardized stations that previously yielded
higher catches. We fished each net overnight (~18 h) and retrieved them the next
morning. We sacrificed, marked with an individually numbered aluminum tag;
measured for total length (mm TL), weighed to the nearest gram (g), placed on ice
for transport back to the University of Georgia, and then stored in a freezer all collected
Blue Catfish until they could be processed for age estima tion.
We removed pectoral spines following the method developed by Sneed (1951).
We cross-sectioned 1 spine from each fish at the basal recess (Sneed 1951) and
articulating process (Buckmeier et al. 2002) with a Buehler® low-speed Isomet
saw (Buehler-Lake Bluff, IL). We mounted cross-sections on glass slides and
processed them as described by Homer (2011). We employed the method described
by Buckmeier et al. (2002) to collect lapillar otoliths from Blue Catfish
specimens and prepared them for age estimation using the methods described by
Homer (2011).
Two experienced readers counted the annuli by placing the cross-sections under
a dissecting microscope (50X magnification for basal recesses, 40X for articulating
processes, and 90X for otoliths) equipped with a camera that projected the image
onto a computer screen and captured a digital image of each sample (see Homer
2011). The technicians used a fiber-optic light source for side illumination to facilitate
readability. They began their counts at the focus (i.e., centermost point) of
the cross-sections, moved outward to the edge of the structure, and reported the
last annulus formed as the outer margin of each cross-section. The readers made
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2015 Vol. 14, No. 4
age assignments based on the age each fish would have been during the spring of
2009 and they resolved any disagreements on age assignments by repeating them
in concert. If the readers could not reach agreement, they excluded that particular
Figure 1. A map of the study site and the 12 standardized gill-netting stations at Lake
Oconee, GA, enlarged (reprinted with permission from Homer and Jennings 201 1).
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structure (i.e., spine or otolith) from the analysis; percent reader agreement was
recorded by technique after all structures were assigned ages.
We used ImagePro Plus® 7.0 (Media Cybernetics, Inc., Bethesda, MD) image-
analysis software to measure incremental growth for all cross-sections. We
measured incremental growth from the focus along the anterior radii of basal-process
cross-sections, anterior radii of otolith sections, and lateral radii of articulating
processes to the outer margin as described by Michaletz et al. (2009). We employed
the Frasier-Lee method (Busacker et al. 1990, Carlander 1982, DeVries and Frie
1996, Frie 1982) to back-calculate length-at-age for each fish with each technique.
We eliminated some fish from our analyses because their structures were damaged
during collection or cross-sectioning, or if there were discrepancies in their age assignments;
numbers of fish included in each technique’s dataset were unequal.
Comparison of techniques
We evaluated differences in mean back-calculated lengths-at-age among ageestimation
techniques using hierarchical linear-regression models (Raudenbush
and Bryk 2002). Hierarchical models used in the study differed from traditional
regression techniques because we accounted for dependence among repeated
measurements from individual fish by including random effects for intercepts
and slopes. That is, we treated the intercept and the relation (i.e., slope) between
age increments and length-at-age as varying normally among individual fish and
interpreted fixed effects as the average relation between age increment and lengthat-
age among individual Blue Catfish. We interpreted fixed effects associated with
the cross-sectioning location as the effect of location on estimated length-at-age
and random effects as variation in the relationship between an age increment and
length-at-age among individual fish. All hierarchical models were fitted using the
lme4 package (Bates et al. 2012) in R statistical software (R C ore Team 2012).
We created 2 binary predictor variables to represent the age-estimation techniques.
We coded the basal-recess predictor and the articulating-process predictor
as 1 or 0. Thus, otoliths served as the baseline technique for the compari sons.
We used an information-theoretic approach to evaluate the relationship between
the age increment and age-estimation technique on back-calculated length-at-age
(Burnham and Anderson 2002). The primary hypotheses of interest were whether
Table 1. Biological interpretations of predictors used in the candidate models relating to the backcalculated
length-at-age of Blue Catfish from Lake Oconee, GA.
Predictor variable Biological interpretation (hypothesis)
Age increment The year in which the annulus was formed will influence
back-calculated lengths.
Method The chronometric structure being used will influence
back-calculated lengths.
Age increment × age increment The quadratic effect on the rate of growth will influence
back-calculated lengths.
Method × age increment The quadratic effect on the rate of growth varies among the
chronometric structures and will influence back-calculated
lengths.
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length-at-age estimates differed among techniques and whether those differences
changed with the age of a Blue Catfish (Table 1). Thus, we created 3 candidate
models to represent these hypotheses (Table 2). All candidate models contained
age increment and an age-increment quadratic term because they are related to
length-at-age (Isley and Grabowski 2007). We used Akaike’s information criterion
(AIC; Akaike 1973) with the small-sample-bias adjustment (AICc; Hurvich and Tsai
1989) to evaluate the plausibility of each candidate model. Parameters used to estimate
AICc included the fixed effects, random effects, and random effect covariance
(Burnham and Anderson 2002). We calculated model weights (wi) and used them
to determine the plausibility of one model over the other (Burnham and Anderson
2002). We estimated the precision of fixed effects in the best-approximating model
by calculating 95% confidence intervals based on a t-statistic with n−1 degrees
of freedom (Littell et al. 1996). We created empirical Bayes plots of the relation
between estimated length-at-age and age increment to aid in the interpretation of
random effects. We assessed goodness-of-fit for each candidate model by examining
residual and normal probability plots.
We evaluated differences among estimated mean lengths-at-age for each technique
by estimating length-at-age with the best-approximating model. To determine
precision in back-calculated lengths-at-age among the techniques, we calculated
95% confidence intervals for each estimated age increment. Hierarchical models
accounted for variation in length-at-age relationships among individual fish (i.e.,
the random effects) and within individual fish (i.e., the residual). Thus, we created
2 sets of confidence limits, the first of which incorporated the predictable variation
from fish-to-fish (random effects) and random error (residual) and represented the
expected error in technique-specific length-at-age estimates from fishes selected at
random from the population. The second set only incorporated the random error
(residual) and represented the expected error in the estimated length-at-age for an
individual Blue Catfish for each age-estimation technique.
Results
During sampling at Lake Oconee, we captured and processed for age estimation
153 Blue Catfish. Total length (TL) range = 138 mm–740 mm (mean = 376.4
mm, SD = 155.3 mm). Agreement of age estimates was greatest for otolith-based
age assignments (83.5%), followed by articulating-process–based age assignments
(77%) and basal-recess–based age assignments (71.4%). Ages ranged from 1 to 8 y
Table 2. Predictor variables, log-likelihood (LogL), Akaike’s information criterion with the smallsample
bias adjustment (AICc), ΔAICc, and Akaike weights (wi) for the set of candidate models for
predicting back-calculated length-at-age of Blue Catfish caught during the December 2008 and January
2009 sampling sessions in Lake Oconee, GA.
Candidate models LogL AICc ΔAICc wi
Method, age increment, age increment × age increment, -7952 15,932 0 1
method × age increment
Age increment, age increment × age increment -8235 16,490 558 0
Method, age increment, age increment × age increment -8045 16,113 182 0
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Table 3. Otolith-derived back-calculated mean total lengths-at-age (mm TL) and associated standard deviations for each year class (2001–2008) of Blue
Catfish caught during the December 2008 and January 2009 sampling sessions in Lake Oconee, GA. A standard deviation could not be calculated for the
2008 year-class because we only sampled 1 fish.
Mean length-at-age (mm TL)
Age Year-class 1 2 3 4 5 6 7 8
1 2008 138
2 2007 84 ± 21.8 195 ± 15.8
3 2006 80 ± 20.2 191 ± 25.5 269 ± 37.8
4 2005 76 ± 20.6 205 ± 27.7 308 ± 37.4 381 ± 52.2
5 2004 91 ± 35.4 211 ± 44.5 318 ± 53.8 406 ± 63.8 470 ± 69.2
6 2003 86 ± 40.9 210 ± 44.3 319 ± 37.3 419 ± 45.7 499 ± 48.5 560 ± 51.5
7 2002 92 ± 30.3 230 ± 54.7 341 ± 66.0 425 ± 77.8 498 ± 81.5 563 ± 81.7 619 ± 85.8
8 2001 102 ± 19.1 232 ± 18.8 331 ± 2.1 439 ± 15.7 515 ± 22.6 582 ± 18.6 631 ± 0.8 674 ± 5.0
Mean 84 ± 29.9 204 ± 36.3 308 ± 48.7 406 ± 58.0 486 ± 63.2 562 ± 57.0 622 ± 74.5 674 ± 5.0
Table 4. Articulating-process–derived back-calculated mean total lengths-at-age (mm TL) and associated standard deviations for each year class (2001–
2008) of Blue Catfish caught during the December 2008 and January 2009 sampling sessions in Lake Oconee, GA. A standard deviation could not be
calculated for the 2008 year-class because we sampled only 1 fish.
Mean length-at-age (mm TL)
Age Year-class 1 2 3 4 5 6 7 8
1 2008 138
2 2007 132 ± 18.0 198 ± 20.7
3 2006 137 ± 30.7 209 ± 33.2 261 ± 42.5
4 2005 144 ± 28.7 243 ± 27.6 313 ± 1.1 367 ± 55.6
5 2004 143 ± 22.0 238 ± 30.5 329 ± 61.1 396 ± 80.6 439 ± 88.7
6 2003 154 ± 38.9 250 ± 48.8 346 ± 6.8 439 ± 64.3 500 ± 67.2 534 ± 63.7
7 2002 146 ± 40.2 223 ± 42.2 327 ± 56.0 432 ± 75.1 514 ± 78.4 559 ± 84.7 584 ± 89.9
8 2001 170 ± 22.0 240 ± 29.9 309 ± 51.6 389 ± 101.0 455 ± 118.0 523 ± 107.0 591 ± 103.0 619 ± 107
Mean 144 ± 30.4 228 ± 40.2 319 ± 59.5 411 ± 73.9 479 ± 82.7 539 ± 70.3 586 ± 88.5 619 ± 107
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Table 5. Basal-recess–derived back-calculated mean total lengths-at-age (mm TL) and associated standard deviations for each year class (2001–2008) of
Blue Catfish caught during the December 2008 and January 2009 sampling sessions in Lake Oconee, GA. We could not calculate a standard deviation for
the 2001 and 2008 year-classes because we sampled only 1 fish for each of those year c lasses.
Mean length-at-age (mm TL)
Age Year-class 1 2 3 4 5 6 7 8
1 2008 138
2 2007 143 ± 20.4 198 ± 19.6
3 2006 161 ± 20.0 225 ± 22.5 275 ± 38.1
4 2005 160 ± 17.7 238 ± 21.6 301 ± 41.0 344 ± 51.6
5 2004 192 ± 35.6 280 ± 47.8 352 ± 63.1 419 ± 81.7 459 ± 89.4
6 2003 182 ± 39.1 277 ± 62.7 353 ± 68.1 433 ± 73.2 491 ± 81.3 521 ± 80.7
7 2002 196 ± 40.5 287 ± 46.9 388 ± 63.6 466 ± 85.2 529 ± 86.0 566 ± 88.6 587 ± 86.7
8 2001 171 228 313 410 506 548 575 590
Mean 172 ± 36.1 251 ± 54.6 339 ± 67.1 417 ± 67.1 486 ± 86.8 534 ± 83.4 586 ± 83.1 590
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(i.e., year-classes 2000–2007) for each technique. Back-calculated lengths-at-ages
derived from the 3 structures were: lapillar otoliths = 84−674 mm TL (Table 3),
articulating processes =144–619 mm TL (Table 4), and basal recess =172−590 mm
TL (Table 5).
The global model—which included age increment and associated quadratic
term, the age-estimation technique, and the interaction of age increment and
growth—was the most plausible model of mean back-calculated lengths-atage.
The model explained 96% of the variation in length-at-age, and the wi for
this model was 1, which indicated zero support for the other 2 models. Mean
back-calculated lengths-at-age were positively, non-linearly related to the age
increment, but differed among the techniques, and those differences varied with
age increment (Table 6). Relation between age increment and length-at-age also
varied among individual Blue Catfish. The model suggested that the relation
between age increment (i.e., the parameter estimate) and back-calculated length
varied substantially, by 20% among fish on average (Table 6; Fig. 2). Parameter
estimates indicated that lengths-at-age determined from articulating processes
and basal recesses were greater than those from otoliths for individual fish at ages
1-5 and were similar at age-6. (Fig. 3). From age 6, predicted back-calculated
lengths were greater when derived from otolith-annuli measurements than when
determined by the other techniques. Length-at-age estimates were greater for the
basal-recess technique than the articulating-process technique until age 6, after
which the difference between basal-recess–derived mean lengths-at-age versus
articulating process-derived estimates decreased (Fig. 4).
Assuming no fish-to-fish variability, mean back-calculated lengths differed
among all 3 age-estimation techniques at ages 1–4, as well as between basal recesses
and otoliths at age 8 (Fig. 4). Accounting for additional variation among fish
(i.e., predictable variation among fish) by incorporating random effects indicated
Table 6. Estimates of fixed and random effects, standard errors (SE), and the lower and upper confidence
limits (CLs) for the best-approximating model for evaluating back-calculated lengths-at-age of
Blue Catfish (age 1 to age 8) in Lake Oconee, GA.
Parameter Estimate SE Lower CL Upper CL
Fixed Effects
Intercept -32.44 3.99 -40.25 -24.63
Age Increment 130.06 3.22 123.75 136.37
Age increment × age increment -7.71 0.40 -8.49 -6.94
Articulating-process method 60.03 4.01 52.17 67.89
Basal-recess method 91.63 3.99 83.82 99.44
Age increment × articulating process -12.10 1.19 -14.43 -9.77
Age increment × basal recess -16.39 1.18 -18.71 -14.07
Age increment × otolith (baseline) 0.00 0.00 0.00 0.00
Random effects
Individual fish 98.35 9.92 78.91 117.79
Age increment 664.14 25.77 613.63 714.65
Age increment × age increment 4.88 2.21 0.55 9.21
Residual 932.43 30.54
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2015 Vol. 14, No. 4
greater overlap in estimated back-calculated lengths at ages among the 3 techniques
for all ages (Fig. 5). After including this variation, differences among the 3 techniques
were only predictably different for fish aged 1–3 y.
Figure 2. Empirical
Bayes estimates of the
relationship between
age and total length
based on (a) articulating
process, (b) basal
recess, and (c) lapillar
otoliths for each Blue
Catfish included in the
analysis (n = 135). Fishspecific
relationships
are only plotted for the
observed range of ages
using the best-approximating
hierarchical linear
model.
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2015 Vol. 14, No. 4
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Figure 4. Mean
b a c k - c a l c u l a t e d
lengths at each age
increment 1−8 and
the associated 95%
confidence intervals
by the articulating
process (grid
pattern), basal recess
(solid white),
and otolith (black
diagonal lines)
techniques used to
estimate growth of
Blue Catfish from
Lake Oconee, GA.
Confidence intervals
were derived
from the best-approximating
hierarchical
and the
residual error and
do not include the predictable variation among individual Blue Catfish.
Discussion
We successfully used the 3 structures evaluated in this study to estimate Blue
Catfish ages, and used the estimated ages to predict reasonably precise back-
Figure 3. The
predicted relations
between age
increment and
lengths-at-ages
1−8 derived from
the articulating
process (dotted
line), basal recess
( b r o k e n l i n e ) ,
and otolith (solid
line) techniques
used to estimate
growth of Blue
Catfish from Lake
Oconee, GA.
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2015 Vol. 14, No. 4
calculated lengths-at-age for Blue Catfish from a young, introduced population.
Our findings suggest that the use of lapillar otoliths and articulating processes yield
more precise age estimates than basal recesses when aging Blue Catfish. Although
the sample lacked older fish, we were able to evaluate precision of age estimates
and back-calculated length from data obtained from 3 cross-sectioning techniques
that could be used on young Blue Catfish populations. We could not use other
commonly employed growth models, such as the Von Bertalanffy (von Bertalanffy
1957), because the population lacked older, larger fish that should be included to
properly fit the models. For instance, a preliminary analysis indicated that estimates
of the asymptotic growth parameter L∞ were too large (i.e., 13 m; the slope of the
line never reached the asymptote) for Blue Catfish when using otolith-based age
data. However, we successfully used hierarchical models to estimate variation in
growth rates among individuals to fit length at-age-models.
Similar to the findings of previous studies evaluating the same 3 age-estimation
techniques for other catfishes (Buckmeier et al. 2002, Nash and Irwin 1999, Olive
et al. 2011), our otolith-based age assignments were more precise than the age
Figure 5. Mean back-calculated lengths-at-ages 1−8 and associated confidence intervals by
the articulating process (AP; grid pattern), basal recess (BR; solid white bar), and otolith
(OTO; black diagonal lines) techniques used to estimate growth of Blue Catfish from Lake
Oconee, GA. Confidence intervals were derived from the best-approximating hierarchical
model and included predictable variation among individual fish and residual error. Note,
a confidence interval was not calculated for the mean length at age-increment 8 for the
articulating process technique because there was only 1 fish estimated to be age 8 by this
technique.
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assignments derived from cross-sections of pectoral spines. Buckmeier et al. (2002)
reported high (i.e., 79%) reader agreement and high accuracy (i.e., 97%) for age assignments
derived from cross-sections of lapillar otoliths from known-age, 1–4-yold
Channel Catfish. Biased age-estimations can lead to flawed interpretations of
data (Campana et al. 1995, Olive et al. 2011), and may have led to the imprecision
observed in our study. However, our results were similar to those of other studies
that have compared these techniques for aging other catfishes. Specifically, higher
precision (Nash and Irwin 1999) and accuracy (Buckmeier et al. 2002) of assigned
ages were achieved for catfishes when determined from otoliths compared with
pectoral spines.
The appearance of annuli and the number of false annuli present in the crosssections
were variable among the techniques we compared and may have affected
our interpretation of age and growth estimates. Kelley and Carver (1966) experienced
similar difficulties when using pectoral spines to assign ages to Blue
Catfish from the Mississippi River. Taking multiple cross-sections from small
Blue Catfish otoliths often damaged the structure and made them more difficult
to read compared to pectoral spines. When the cross-sections were not damaged,
annuli from cross-sections of Blue Catfish otoliths often appeared clearer and less
variable in appearance compared to cross-sections obtained from pectoral spine
cross-sections. Cross-sections taken from the basal recess and articulating process
of pectoral spines were variable in appearance. False marks, crowding of annuli,
and loss of annuli from central-lumen expansion were evident in the spine crosssections;
similar phenomena have been reported for Channel Catfish (Buckmeier
et al. 2002) and Flathead Catfish (Nash and Irwin 1999). Such features were more
prevalent in the basal-recess sections than the other structures. Erosion of annuli by
expansion of the central lumen and the crowding of annuli toward the outer margins
of the pectoral spine cross-sections may have contributed to errors in our age
assignments and measurements of growth. Basal recess cross-sections from fish
older than age 3 had obvious partial loss of the first annulus. We did not quantify
the number of fish with missing or false annuli for any of the t echniques.
The findings of the present study suggest that each technique produced different
estimates for back-calculated length-at-age for individual Blue Catfish. Growth
estimated from each cross-sectioning technique was variable from age 1 to age 8.
However, the hierarchical models indicated that differences were minor compared to
the variation in back calculated length-at-age among individual fish when using the
same technique. If ignored, this fish-to-fish variation (i.e., dependence) would have
led to underestimates of the variation in the length-at-age regression-parameter estimates
(Sokal and Rohlf 1995) and erroneously inflated estimates of precision.
Despite the significant differences in back-calculated lengths from age 1 to
age 6, back-calculated length was similar at the later age increments except between
the basal-recess– and otolith-derived estimates at age 8. Similarities in the
lengths were likely a result of the length/age distributions in the samples. Modeled
mean lengths-at-age were similar for the 3 techniques at the median length, which
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M.D. Homer Jr., J.T. Peterson, and C.A. Jennings
2015 Vol. 14, No. 4
suggests that differences in back-calculated lengths-at-age might be negligible at
or near the actual recorded lengths of fish at the time of capture. We collected fish
138–740 mm TL in the gill nets. Some size-selectivity was evident in our sample;
the younger fish had not yet recruited to the gear and the larger fish were not very
abundant, given the young age of the population. Repeating this study on populations
of Blue Catfish with diverse age classes might reduce variability and yield
more precise estimates of lengths-at-age.
Over the last decade, fisheries biologists have debated the reliability of pectoral
spine-based vs. otolith-based age estimates. Recent studies have reported that pectoral
spines produce data similar to otolith-based age data when ages are estimated for
catfishes (Colombo et al. 2010, Michaletz et al. 2009, Olive et al. 2011). Furthermore,
accuracy of otolith-based age estimates has been evaluated only for Channel Catfish
up to age 4 (Buckmeier et al. 2002), but the use of these structures may produce accurate
age estimates up to age 16 for Blue Catfish (Olive et al. 2011). Back-calculated
length-at-age estimates in this study varied by 31% among the Blue Catfish. Nonlethal
age- and growth-estimation techniques are suitable for populations with low
abundance and for populations intended to produce trophy fish (Boxrucker and
Kuklinski 2006, Maceina et al. 2007, Olive et al. 2011). However, otoliths may provide
greater precision and are likely more suitable than spines for obtaining age and
growth information for introduced populations. Our results can be used by biologists
to help them decide which techniques are best-suited for the age- and growth-data
collection for managing Blue Catfish and other catfish populations.
Acknowledgments
This research was conducted collaboratively with the Wildlife Resources Division of
the Georgia Department of Natural Resources. Michael S. Bednarski, Benjamin Carswell,
Colin P. Shea, and Daniel Farrae of the University of Georgia (UGA); Dave Buckmeier
(Texas Parks and Wildlife); and Kurt Kuklinski (Oklahoma Department of Wildlife and
Conservation) provided technical assistance. Scott Lamprecht and Chad Holbrook of South
Carolina Department of Natural Resources provided time and technical training for otolith
preparation and sectioning. Peter Sakaris, Southern Polytechnic State University, generously
provided additional technical training for otolith preparation and sectioning. Robert
Bringolf (UGA) provided in-kind support. We thank Chris Nelson, Jamie Dowd, Mark
Rigglesford, Ramon Martin, Michael Sheppard, Taylor Duke, Tony Beck, Daniel Malcom,
and John Ruiz for assistance with collections. We are grateful to the reviewers of this manuscript
for their helpful comments. The Georgia Cooperative Fish and Wildlife Research Unit
is sponsored jointly by the Georgia Department of Natural Resources, UGA, the US Fish
and Wildlife Service, the US Geological Survey, and the Wildlife Management Institute.
Any use of trade, product, or firm names is for descriptive purposes only and does not imply
endorsement by the US Government.
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