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Validation of aging techniques and growth of the river redhorse, Moxostoma carinatum, in the James River, Missouri.

Determination of age plays a vital role in conservation and management of populations of fish. Information on age is necessary for determining rates of growth and age at maturation, predicting size at various ages, estimating longevity and rates of mortality, and understanding effects of environmental changes (Isley and Grabowski, 2007). The most widely validated method for determining age of fish is osseochronometry, the estimation of age using hardparts (Casselman, 1987). Methods typically involve enumeration of zones or rings (annuli) formed by annual disruptions or changes in growth of the hardpart. For data to be reliable, it is critical that methods used to estimate age are validated; i.e., the temporal meaning of zones being counted must be documented (Beamish and McFarlane, 1983; Kalish, 1995). The most desirable method of validating age is to compare counts of annuli from hardparts to known ages of individual fishes; in wild populations, this often is unfeasible. Alternative methods of validation include marginal-increment analysis or edge analysis, which involve examining the growing margin of calcified structures in a population throughout the year (Casselman, 1987; Beckman et al., 1991). Edge analysis monitors presence of an opaque or translucent zone at the edge of structures monthly (Campana, 2001).

Various structures have been used in osseochronometry of fishes, including scales, otoliths, fin rays and spines, and opercular bones. Each can provide advantages for a given species based on ease of collection, simplicity of processing, accuracy and precision of estimates of age, and difficulty of discerning annuli, especially at youngest and oldest ages (Chilton and Beamish, 1982; Beamish and McFarlane, 1983; Casselman, 1987). To determine which structures provide accurate and precise estimates of age, techniques must be applied and comparisons made among structures for fish sampled from a single population (Beamish and McFarlane, 1983).

Although there have been numerous studies of aging in catostomids, few methods have been validated. Most studies of aging have been on Catostomus, including estimates of age from scales (Spoor, 1938; Geen et al., 1966), pectoral fin rays (Beamish and Harvey, 1969; Beamish, 1973), opercular bones (McConnell, 1952; Ovchynnyk, 1965; Scoppettone, 1988), clithera, and otoliths (Quist et al., 2007). For the white sucker Catostomus commersoni, otoliths were validated as a reliable estimator of age (Thompson and Beckman, 1995), whereas sections of fin rays and scales tended to underestimate ages of older fish (Sylvester and Berry, 2006) . Estimations of age have been reported for Moxostoma using scales (Meyer, 1962; Gabrowski et al., 2007) , pectoral fin rays (Sweet et al., 2009), and opercles (Reid, 2007). Ages have been estimated for Moxostoma carinatum, the river redhorse, using scales (Carlander, 1969; Tatum and Hackney, 1970), fin rays, and opercles (Reid, 2007). No validation has been reported for studies of aging in Moxostoma.

Moxostoma is comprised of >20 species, most of which are limited to eastern and central North America (Jenkins, 1970; Nelson et al., 2004). Moxostoma carinatum is one of the largest, reaching >5 kg (Jenkins and Burkhead, 1993). It is native to medium-to-large rivers with rapidly moving water, and some populations have adapted to life in impounded rivers. It feeds on mollusks and crayfish that are crushed with large molariform pharyngeal teeth (Hackney et al., 1967; Etnier and Starnes, 1993). Populations of river redhorses have been depleted or extirpated in much of their range over the past century, possibly due to loss of habitat (Etnier and Starnes, 1993; Jenkins and Burkhead, 1993). Our objective was to compare and assess validity of estimates of age using otoliths, scales, and opercular bones collected from a population of the river redhorse in southwestern Missouri, validated estimates of age were used to develop models of growth.

Materials and Methods--We collected samples of 12-41 river redhorses at ca. 30-day intervals during December 1997-November 1998 from the lower James River and extending into the James River Arm of Table Rock Reservoir (an impoundment of the White River) in southwestern Missouri. Samples were taken within a 4-km stretch of the river, from near Cape Fair northward to Galena, Missouri, and when necessary, upstream following migrations to spawn in spring. Sampling was conducted from sunrise to sunset or until a reasonable sample was procured. Fishes were collected using a boat electrofishing unit. Steep rocky shorelines with swift currents were targeted. All river redhorses shocked were collected, placed on ice, and transported to the lab for processing. Several scales were removed from each fish from a region 2-3 rows ventral to the dorsal fin and pressed between glass slides. Opercular bones were cut from the fish and placed in boiling water for 30-45 s to facilitate removal of tissue and then air-dried, otoliths (lapillae) were removed and stored dry until processing.

Scales were viewed with the aid of a dissecting microscope at 10-15X magnification using transmitted light. Annuli were defined by patterns of crowding and crossing over of circuli (Carlander, 1969). Otoliths were embedded in Embed-812 polymer (Electron Microscopy Sciences, Hatfield, Pennsylvania) and baked at 60[degrees]C for 24 h. Embedded structures were sectioned in the transverse plane through the core to ca. 0.5-mm thick using a Struers Minitom (Struers, Inc., Cleveland, Ohio) diamond-tipped saw. Sections were sanded with 600-grit, wet-or-dry sandpaper and polished on a felt pad with alumina suspension polishing compound to improve clarity (Secor et al., 1991; Thompson and Beckman, 1995). For aging, sections were viewed with a compound microscope at 40X magnification using transmitted light. Opercles were viewed whole without magnification in an unlit room using transmitted light.

Age was estimated for each structure by counting the number of opaque zones (annuli). The relatively translucent nonannulus-forming regions are referred to as translucent zones. Preliminary observations indicated no discernable difference in annuli of structures taken from left and right sides of fishes; therefore, data are reported for one of each structure from each fish. For scales, data were recorded from the scale with the clearest and most distinct annuli.

Timing of formation of annulus was documented by edge analysis. Edge analysis involves recording presence of an opaque or translucent zone at the edge of the structure and plotting relative frequency of the edge zone monthly (Campana, 2001). Structures taken from individuals sampled from the population throughout the year were observed to determine if a single annulus (opaque zone) was formed concurrently at the edge of hardparts. Index values for edge were assigned for each structure for each fish: 0-1/3 complete; 1/3-2/3 complete; and 2/3-100% complete (Casselman, 1987; Beckman et al., 1991). Age of each otolith, scale, and opercle was determined twice by the same observer who completed the analysis of edge. Disagreements in age for a fish were resolved by reexamination. All assessments of annuli were done randomly and blind of origin of the sample or other estimates of age to avoid bias.

Ages were assigned from counts of annuli and assuming a birth date of 1 April, based on reported spawning period of the river redhorse in Missouri (Pflieger, 1997). Assignment of a birth date based on life history provides more accuracy in designating year classes than would selection of an arbitrary birthdate of 1 January, and adds precision in modeling growth (i.e., ages can be assigned to the nearest month; Beckman, 2002). Frequency distributions of age classes for the population were generated based on ages estimated by otoliths, opercles, and scales. Rates of growth were modeled using power (log-log) models in the form: total length = b(age)m, where b is the intercept and m is the slope. Separate models were generated for each structure. Percentage agreement among ages was calculated for all possible pair wise comparisons of estimators of age. The coefficient of variation and index of precision were calculated to test for reproducibility between estimators of age (Chang, 1982).

Results--Analysis of edge clearly indicated that both opercles and otoliths formed one opaque region (annulus) per year almost exclusively in May and June (Fig. 1). In addition, a translucent region formed continuously during non-annulus-forming months, as indicated by progression of peaks for each marginal index (Fig. 2). Marginal-increment analysis did not support formation of one annulus per year in scales. Although 60-75% of individuals formed an annulus in May and June, >60% exhibited an opaque region in December as well (Fig. 1). Data suggested that the translucent region in scales did not form continuously during non-annulus-forming months (Fig. 2); however, further studies are needed to assess factors controlling formation of annuli in scales. Comparison of results of analysis of marginal increments indicated differences in formation and ageability of annuli among structures. False annuli in opercles and otoliths were distinguished due to incomplete formation around much of the structure, their irregular nature, and dissimilarity to true annuli.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

In pairwise comparisons of estimates of age between structures, the best agreement was between otoliths and opercles, with exact agreement in ages 94% of the time (Table 1). Estimates of age from scales generally were lower than those of opercles and otoliths. Otoliths and opercles also had the lowest variance and greatest precision. Variance and index of precision (n = 250), respectively, for comparisons of estimators of age were: otoliths-opercles, 0.003 and 0.002; otoliths-scales, 0.464 and 0.328; opercles-scales, 0.460 and 0.325; otoliths-scalesopercles, 0.313 and 0.181.

Percentage of each age class in the population indicated that the proportion of older individuals was underestimated when estimates of age from scales were used (Fig. 3). Estimates of age from otoliths and opercles indicated that fishes were 1-15 years old, while scales yielded estimates of 1-12 years. Estimates of age from scales would suggest a higher proportion of individuals at intermediate ages (5-10 years) than otoliths or opercles. No age-0, and relatively few age-1 and age-2 fishes, was collected. All structures indicated a dominance of age classes 3 and 4 in the population, with each comprising ca. 20-27% of the population. Estimates of age from otoliths and opercles indicated a steep decline in abundance at 4-5 years old, followed by a low and fairly constant mortality through age 14.

Power growth models described >85% of variability in length-at-age data based on [R.sup.2]-values (Fig. 4). Fit of data to the model satisfy the regression assumption of normality as residual data appeared to be distributed evenly about the model at each age class (Sokal and Rohlf, 1981). Models based on ages estimated by otoliths and opercles produced nearly identical estimates of growth. Models indicated that, on average, growth was relatively rapid in early years, followed by declining rates, with continuous growth into older ages. For scale-based models, greater slope would suggest a faster rate of growth, and the growth curve would underestimate ages for larger fish (Fig. 4). Further evidence of errors in estimation of age from scales can be seen by comparing raw length-at-age data for each structure used in estimating age. For otoliths and opercles, length-at-age data were highly variable. Although, on average, size increased with age throughout life, beyond about age 9 there were few differences in ranges of size among age classes; e.g., most 9-11-year-old fish were 550-650 mm. Lengths within age classes varied as much as 200 mm or more; e.g., age-5 fish were 320-560 mm. Ages at a given length varied as much as 6 years; e.g., 55-cm fish were 5-11 years old (Fig. 4).

[FIGURE 3 OMITTED]

Discussion--Although Campana (2001) cautioned against abuse of marginal-increment analysis or analysis of edge for validation of age, we have addressed these concerns by including a large proportion of older individuals in samples ( > 40% were 5-14 years old), randomizing samples before examination, and considering a structure valid for aging only when a single prominent peak was evident in plots of frequency (no fish exhibited opaque zones at the growing edge outside of annulus-forming months for otoliths and opercles). In no month did all fish exhibit an annulus at the edge of otoliths (Fig. 1). However, this does not negate formation of a single annulus per year; it likely reflects variability in timing of formation of an annulus among individuals (e.g., some individuals complete formation of an annulus in May, others in June). The most valid explanation of observations for the edge analysis is that the distinct annuli observed in otoliths and opercles form once per year in May and June at all ages, validating these structures for estimation of age.

For scales, formation of a single yearly annulus is not indicated clearly. Thus, our data do not support use of scales to estimate age. Although edge data might be interpreted to indicate formation of two annuli on scales each year (Fig. 1), such a conclusion would result in substantial underestimation of age compared to validated ages. Crowding of false incomplete annuli (checks) near the edge of scales hindered marginal-increment analysis and estimation of age, especially for older fish. Similar difficulties with use of scales to assess age have been reported for white suckers (Beamish, 1973; Sylvester and Berry, 2006) and other populations of river redhorses (Jenkins and Burkhead, 1993; Reid, 2007). Beamish and McFarlane (1983) reported underestimation of age by >10 years in some white suckers. We discovered that counts of annuli on scales of the river redhorse underestimates age by [less than or equal to]6 years relative to otoliths and opercles. Despite these problems, our data do not preclude the possibility that scales could be useful for aging young fish. Length-at-age data, models of growth, and age-frequency data suggest that scales of river redhorses produce estimates of age comparable to those of validated structures up to ca. 4-5 years old; beyond these ages, scale-based models of growth overestimate rate of growth and underestimate age using length of fish. This agrees with observations by Beamish (1973) that scales from white suckers might be valid for aging fish [less than or equal to] 5 years old. A more thorough study of younger age classes might validate use of scales for aging of some age classes. Studies of known-age fish would be necessary to validate use of scales to determine age.

[FIGURE 4 OMITTED]

To ensure reliability, the same person who evaluated edges completed estimations of age, based on the assumption that they would reliably recognize the structure of annuli. Results indicate that determining age by an experienced observer using otoliths and opercles are equally accurate and valid for aging river redhorses. Ages agreed for >90% of individuals using these structures and there was no indication of systematic differences when structures disagreed (mostly by a single year); growth curves and age distributions were nearly identical when generated from either structure. Therefore, preference of one structure over the other would be largely a practical consideration. Comparable dissection time is needed to remove each structure; however, opercles have the practical advantage of ease of preparation and observation. They can be viewed whole without microscopic observations after removal of the covering tissues and no further preparation. However, for specimens that were age 4 and older, the first, and sometimes second, annuli were difficult to discern due to thickening of bone as the opercle developed. Opercles of age-1 and age-2 fish should be viewed for reference to ensure inclusion of the first two annuli when aging older fish. Determining age using otoliths requires additional time, equipment, and expertise; because of their size, shape, and opacity, otoliths must be embedded, sectioned, and examined microscopically. Care must be taken to ensure that all annuli are included and that false incomplete annuli (checks) are discounted. The observed progression of translucent-zone formation in otoliths outside of months of formation of annuli indicates a consistent annual pattern of growth (Fig. 2). Development of a distinct pattern in formation of annuli can be used to assist in recognizing false checks.

Although there is no report of validations for estimates of age from scales for the river redhorse, desirability of non-lethal sampling and ease of preparation will continue to tempt managers to use these structures as a population-monitoring tool, it must be recognized that unvalidated ages, especially for scales, repeatedly have been shown to be inaccurate and imprecise, resulting in poor conservation and management decisions (Beamish and McFarlane, 1983; Campana, 2001). Further studies are needed if scales are to be used for aging even younger classes of the river redhorse and other catostomids (Thompson and Beckman, 1995; Sylvester and Berry, 2006).

Both otoliths and opercles of river redhorses formed an annulus almost exclusively during May and June. Timing of formation of an annulus (opaque zone) in otoliths agrees with other marginal-increment analyses; formation of an annulus in north-latitude, temperate fishes are most prevalent during April-June (Beckman and Wilson, 1995). Although these months coincided with the end of the peak spawning period, annuli formed in both mature and immature individuals suggests that formation of an annulus is not a direct result of spawning. This agrees with observations for other species of fish (Beckman and Wilson, 1995). Months when formation of an annulus occurred also coincided with onset of warmer temperatures. In warmwater fishes, the majority of growth occurs during warmer months and formation of opaque zones in otoliths often coincides with increasing growth (Casselman, 1987). The hypothesis of environmental control of the timing of formation of an annulus in otoliths is supported further by observations that five species of cyprinids and catostomids in the James River system of southwestern Missouri formed opaque zones primarily during May or June (Howlett, 1999; Simmons and Beckman, 2012; our study).

Validated estimates of age were used to generate data that could be compared with that of other populations and species of catostomids. Average length at age indicated by our study was at the upper end of the range presented by Carlander (1969) for ages 1-6 in several populations of river redhorses and by Purket (1958) for ages 1-4 in the Saint Francis River of Missouri. This suggests that either river redhorses from the James River exhibit more rapid growth than other populations that have been studied or that there were errors in previously estimated ages, which were based mostly on scales and not validated.

Reports of maximum life span for the river redhorse are 12-17 years (Parker, 1988; Etnier and Starnes, 1993; Pflieger, 1997; Reid, 2009). This is comparable to the maximum age of 14 years observed in our study. However, 770-mm-long individuals have been reported in other populations (Page and Burr, 1991), which is >80 mm longer than the largest fish in our study. This suggests the maximum longevity for the river redhorse might be greater than has been reported.

Models of growth based on otoliths and opercles indicated a trajectory similar to that observed for other species of suckers that were comparable in size and longevity (e.g., Reid, 2009). Power growth models provided a good fit to the data without violating assumptions of normality required for fitting regressions (Sokal and Rohlf, 1981). Von Bertalanffy models of growth, which traditionally fit data for growth of fish, were not considered due to inherent problems with these models (Roff, 1980). Especially, the unrealistic values sometimes produced for supposed biologically meaningful parameters when data do not exhibit an asymptotic length (Haddon, 2001), which also was indicated by data in our study.

Our data on ages indicate successful recruitment into the population for [greater than or equal to]14 consecutive years. The low numbers of aged 0-2 fish in samples, a pattern common in sampling populations of fish (Sigler and Sigler, 1990), might be the result of differences in habitat, size-selection biases of electrofishing, or both. Dominant year classes (ages 3 and 4) predominantly were immature fish. However, presence of a broad age structure of mature fish, making up about one-half of fish we sampled, suggests a healthy spawning population. The large difference in relative abundance of year classes 4 and 5 could be explained by a high rate of mortality after age-at-maturity due to natural mortality, fishing mortality, or possibly, from harvesting during spawning runs into shallow upstream habitats (Pflieger, 1997). However, these observations also could be explained by annual variability in recruitment. Multiple years of data on age of a population would be needed to further define sources of variability in year-classes.

Studies of age and growth provide the foundation for analyses needed for conservation of populations. After an awareness of the negative impacts of non-validated studies of aging was brought to the attention of biologists and managers (Beamish and McFarlane, 1983), validation became more routine. However, thorough methods of validation have been applied mostly to marine species and those considered valuable for harvesting (Beckman and Wilson, 1995; Campana, 2001). There has been relatively little effort to perform validations of aging techniques used for freshwater fishes that are considered non-game species; possibly due to the time and expense of conducting the research. Although analysis of edge and marginal-increment analyses are not the preferred methods of validation, if done with adequate rigor, they provide an acceptable means of validation (Campana, 2001) that can be applied without the need for known-age fish or mark-recapture studies. Our study has shown that analysis of edge provides an adequate validation for two structures used to determine age of fish, otoliths and opercles, in the river redhorse, and that these structures are superior to scales in analysis of age. Because these methods of validation have produced similar results for other populations of warmwater-stream fish, it is likely that analysis of edge could be applied broadly to other species. Further studies that include validation of aging techniques for warmwater, stream and riverine species, other than those that are endangered or valued as fisheries, are needed to enable precise monitoring of health and environmental impacts on populations.

The Biology Department of Missouri State university provided financial support. Thanks to M. Barnhart and R. Rhodes for providing criticisms and technical help, and B. Simmons, D. Howlett, M. Baird, F. Riusech, D. Hanson, and C. Klotz who provided field and technical assistance.

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Submitted 1 December 2010. Accepted 3 April 2012. Associate Editor was Christopher M. Taylor.

Daniel W. Beckman* and Christian A. Hutson

Department of Biology, Missouri State University, Springfield, MO 65897 *Correspondent: danielbeckman@missouristate.edu
Table 1--Frequency of differences of estimated ages in
pairwise comparisons of structures used to determine age of 250
river redhorses ( Moxostoma carinatum) from the James River,
Missouri.

                               Opercle-    Opercle-     Otolith
Difference in age               otolith       scale       scale

+6 years                              0           2           2
+5 years                              0           3           3
+4 years                              0           8           6
+3 years                              0           8          12
+2 years                              1          13          14
+1 year                               5          35          33
0 years                             236         158         160
-1 year                               7          20          18
-2 years                              1           2           1
-3 years                              0           0           0
-4 years                              0           1           1
-5 years                              0           0           0
-6 years                              0           0           0
Agreement (%)                        94          63          64
Agreement within 1 year (%)          99          85          84
Agreement within 2 years (%)        100          91          90
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Date:Sep 1, 2012
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