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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 Southeastern Naturalist 741 M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 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. Southeastern Naturalist M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 742 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 Southeastern Naturalist 743 M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 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). Southeastern Naturalist M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 744 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. Southeastern Naturalist 745 M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 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 Southeastern Naturalist M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 746 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 Southeastern Naturalist 747 M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 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 Southeastern Naturalist M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 748 (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 Southeastern Naturalist 749 M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 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. Southeastern Naturalist M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 750 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. Southeastern Naturalist 751 M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 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. Southeastern Naturalist M.D. Homer Jr., J.T. Peterson, and C.A. Jennings 2015 Vol. 14, No. 4 752 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 Southeastern Naturalist 753 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. 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