Age, Growth, and Fall Diet of Channel Catfish in Cheat Lake, West Virginia

The channel catfish Ictalurus punctatus (Figure 1) is an economically important species in North America because of its popularity among recreational and commercial fishermen, as well as aquaculturalists. Channel catfish are native to the central drainages of North America (Etnier and Starnes 1993), and have been extensively introduced elsewhere to enhance sport fisheries (Marsh 1981; Tyus and Nikirk 1990; Bonar et al. 1997). Presently, the range of channel catfish extends throughout much of the United States (Hubert 1999a). On account of its importance to humans and extensive range, channel catfish have been studied extensively. However, few studies have been published on channel catfish populations in the Central Appalachians, presenting a gap in the knowledge base on the species. Given the extensive variability among populations, filling in gaps may help elucidate factors influencing channel catfish population dynamics.

Cheat Lake has one of the more prolific channel catfish populations of West Virginia's reservoirs. Currently, no management practices are in place to maximize channel catfish production in Cheat Lake, and limited data exist on the population. This study focuses on estimation of population characteristics of Cheat Lake channel catfish and evaluation of fall diets. Our objectives were to estimate size structure, age structure, mortality, condition, growth, and fall diets for Cheat Lake's channel catfish population, thus providing a baseline for future management decisions and filling a regional knowledge gap in the population ecology of channel catfish.

Cheat Lake (also referred to as Lake Lynn) is a 700.4-ha hydropower reservoir on the Cheat River near Morgantown, West Virginia (Figure 2). The reservoir flows 20.9 km from the head of the lake to the Lake Lynn Hydropower Dam. Acid precipitation (Welsh and Perry 1997) and acid mine drainage (Freund and Petty 2007; Merovich and Petty 2007; Merovich et al. 2007) within the Cheat River Watershed have affected downstream sections, including Cheat Lake. Fish survey reports from Cheat Lake during the 1950s documented 14 total species, predominately from the families Catostomidae and Ictaluridae (Core et al. 1959). However, mitigation efforts throughout the watershed have led to improved pH (D. M. Smith, West Virginia Division of Natural Resources, unpublished data) within Cheat Lake and a resurgence of fish species richness. Since 1997, 44 species (11 families) have been collected from Cheat Lake (D. M. Smith, unpublished data). Presently, channel catfish are abundant in Cheat Lake, making up a large proportion of the catch in gill-net surveys (D.M. Smith, unpublished data).

We collected channel catfish from Cheat Lake from 5 August 2014 to 4 December 2014 using gill nets, boat electrofishing, and baited hoop nets. We collected fish during this period to ensure spawning had been completed, which reduced sex-related bias in condition estimates. We strategically selected sampling sites in areas with historical success of channel catfish collection or favorable habitat. We also modified spatial sampling arrays to collect channel catfish as they moved between seasonal habitats and to avoid areas fish were no longer utilizing. We avoided areas with submerged woody debris in gill-net and hoop-net sampling to minimize the likelihood of net entanglement. We calculated mean catch per unit effort for all sampling gears and measured it as fish per net-night, fish per hour, and fish per net-night for gill nets, boat electrofishing, and baited hoop nets, respectively. We have provided catch per unit effort and water temperatures for sampling events in Table S1.

We primarily used gill nets because of prior success capturing channel catfish in a separate study in Cheat Lake (D. M. Smith, unpublished data). We fished with two different types of monofilament nets, including nets of dimensions 38.1 m × 1.8 m (consisting of five 7.6-m panels of bar mesh sizes 1.9-, 2.5-, 3.8-, 5.1- and 6.4-cm) and 45.7 m × 1.8 m (consisting of six 7.6-m panels of bar mesh sizes 3.8-, 4.4-, 5.1-, 3.8-, 4.4- and 5.1-cm). We initially used the shorter 38-m experimental gill nets to avoid exclusion of smaller channel catfish, but we discontinued their use when we collected few small individuals (five fish shorter than stock length [280 mm] in 47 net-nights). We subsequently used the 46-m gill nets in an effort to maximize catch rates. We tied gill nets to woody debris on shore and set them perpendicular to the shoreline. We anchored nets to the lake bottom using cinder blocks adjoined to the sinking line of the gill net. We fished nets overnight and pulled nets in the order they were set.

We conducted high-frequency (60 pps) boat electrofishing using a Smith-Root boat-mounted electrofisher (5.0 GPP) with a typical output of 4–6 amps. Sampling took place from dusk to 2400 hours in shallow areas (<2.5 m) with suitable water clarity. On some occasions, we deployed bait to lure channel catfish into shallow areas to improve their susceptibility to electrofishing. We attached to a carabineer cheese trimmings (Memphis Net & Twine) or Charlie Blood B Dough Bait (Catfish Charlie Bait Co. Inc.) contained in a mesh bag, and we clipped the carabineer to a rope connecting an anchor to a buoy. We used buoys to mark the exact site of bait deployment. We electrofished in areas adjacent to buoys approximately 30–60 min after buoy deployment. We used electrofishing to target small individuals not recruited to other gears.

Several studies have suggested baited tandem hoop nets as the most effective gear for sampling channel catfish in lentic systems (Sullivan and Gale 1999; Michaletz and Sullivan 2002; Flammang and Schultz 2007; Richters and Pope 2011). Suitable sites for tandem hoop nets in Cheat Lake were limited on account of steep banks and submerged woody debris. As a result, we set single hoop nets in coves and mouths of tributaries where depths were shallow (<3 m) and bathymetry was relatively low gradient. Hoop nets consisted of seven 1.2-m fiberglass hoops with 3.8-cm mesh with restrictive throats on the second and fourth hoops. We placed cheese trimmings or Blood Dough Bait contained in mesh bags in the cod end of the hoop net. Neely and Dumont (2011) suggested baited, tandem hoop net sets were most effective if set for 2–3 d. In Cheat Lake, intense recreational boating prevented long-term sets and we set hoop nets over-night. After low success rates during pilot sampling, we used hoop nets sparingly.

We immediately placed collected channel catfish on ice to preserve specimen integrity and slow digestion of stomach contents. We measured channel catfish total length (TL) to the nearest mm and weighed them to the nearest g using a battery-operated digital scale (Ohaus^® Valor 2000 Series - Model V21PW6). We extracted lapillus otoliths (lapilli, hereafter) for age analysis. We cleaned lapilli of all soft tissue and allowed them to dry in coin envelopes.

Annulus interpretation on whole channel catfish lapilli is difficult because of opaqueness. To gain a view of the transverse plane, we used the protocol described by Buckmeier et al. (2002). In accordance with this technique, we burnt lapilli on a Carolina™ Hot Plate/Stirrer to produce a dark amber color. We mounted burnt lapilli to a glass microscope slide using Crystalbond™ 509 (Electron Microscopy Sciences). We sanded lapilli using 400- or 600-grit wet/dry sandpaper to remove the top third of the otolith. We polished the transverse plane using 1,200- and 2,500-grit automotive-grade sandpaper to improve clarity of annuli. We immersed mounted lapilli in water to reduce glare and viewed them under a dissecting microscope (20–40× magnification). Two independent readers counted annuli and discrepancies were resolved by mutual examination.

We included in diet analysis fish collected from 13 October 2014 to 4 December 2014. We used gill nets exclusively to sample fish for diet analyses. We excised a sample of 55 stomachs and preserved them in a 95% ethanol solution (Dagel et al. 2010). We removed stomach contents in the laboratory and identified them to family or the lowest practical taxonomic level. We counted prey items with the exception of detritus, which we noted as present or absent. We labeled counted prey items as either aquatic or terrestrial in origin. We noted empty stomachs as such and excluded them from further analysis.

To estimate the size structure of the Cheat Lake channel catfish population, we constructed a length-frequency histogram and calculated proportional size distributions (PSDs). We constructed the length-frequency histogram using length–group interval guidelines presented in Neumann et al. (2012). We estimated proportional size distributions (Gabelhouse 1984; Neumann et al. 2012) using the following length categories presented by Gabelhouse (1984); stock (280–409 mm), quality (410–609 mm), preferred (610–709 mm), memorable (710–909 mm), and trophy (≥910 mm). We estimated confidence intervals (95%) on PSDs using the equation presented by Gustafson (1988).

We used a relative weight index to estimate condition of Cheat Lake's channel catfish population. Relative weight (W_r) is calculated as W_r = (W/W_s) × 100, where W is the weight of an individual fish in g and W_s is the standard weight (Wege and Anderson 1978). Standard weight of channel catfish is calculated as log₁₀ (W_s) = −5.800 + 3.294 log₁₀ (L), where L is the TL of an individual fish in mm (Brown et al. 1995). We summarized relative weights using the length categories presented by Gabelhouse (1984) with the addition of a sixth category (substock) to encompass small fish not represented in the other categories (Shuman et al. 2011).

We used age-class data and catch-curve linear regression (Ricker 1975) in R version 3.1.3 (R Core Team 2015) to estimate an instantaneous mortality rate (Z) of channel catfish. We used weighted linear regression to lessen the influence of older, less common age classes (Maceina 1997). We subsequently calculated the annual mortality (A) rate as A = 1 − e ^−Z (Miranda and Bettoli 2007). Hubert (1999b) stated in a meta-analysis of 102 channel catfish age studies that most studies had few fish <3 y old, indicating channel catfish may not fully recruit to sampling gears until age 3. As a result, we excluded fish younger than age 3 from catch-curve analysis. Catch-curve analysis assumes no age estimation error, as well as constant recruitment, mortality, and catchability over all age classes considered (Miranda and Bettoli 2007; Smith et al. 2012).

We used mean length-at-age data to compare Cheat Lake channel catfish growth to standards presented by Hubert (1999b) and Jackson et al. (2008). Hubert (1999b) provided growth standards for channel catfish from age 3–10, allowing assignment of percentiles to mean length-at-age data. We also compared Cheat Lake channel catfish growth using the relative growth index (RGI) to the standard length (L_s) equation presented by Jackson et al. (2008), where ⁠. We calculated relative growth index values as RGI = (L_t/L_s) × 100, where L_t was the observed length-at-age and L_s was the predicted age-specific total length from the standard length equation (Quist et al. 2003). We calculated relative growth index values for all aged fish and averaged them by age class. We compared mean RGI values to an average value of 100, where RGI values >100 indicate fast growth, while RGI values <100 indicate slow growth (Quist et al. 2003). We presented mean RGI values from ages 3 to 10 because Hubert (1999b) indicated most studies lack adequate sample sizes outside this range.

We fit four growth models to channel catfish length-at-age data using a Gauss–Newton algorithm in Program R. The four candidate models were the von Bertalanffy (VBGM), logistic, Gompertz, and power models:

All candidate models, except the power model, include a maximum length term (L_∞), indicative of asymptotic growth. However, the power model assumes indeterminate growth. The VBGM assumes the growth rate decreases linearly with size (Katsanevakis 2006; Katsanevakis and Maravelias 2008). In the VBGM, k describes how quickly L_∞ is reached and t₀ is the theoretical age when length equals zero (Quist et al. 2012). The Gompertz and logistic models approximate sigmoidal curves. The Gompertz model assumes an exponential decrease in growth rate with age (Katsanevakis 2006), whereas the logistic model is symmetrical about an inflection point (Quist et al. 2012). In the Gompertz model, t₀ is the inflection point of the curve and G is the instantaneous growth rate at age t₀ (Quist et al. 2012). The parameters G and t₀ in the logistic model are the instantaneous growth rate at the origin of the curve and the theoretical age when length is zero, respectively (Quist et al. 2012).

We compared the four candidate growth models for goodness-of-fit using an information theoretic approach. We used Akaike's Information Criterion with a small-sample bias correction (AIC_c) to rank models in order of decreasing fit (Anderson 2008; Katsanevakis and Maravelias 2008; Burnham et al. 2011). We considered the model with the lowest AIC_c value to be the “best” approximating model (Burnham et al. 2011). We calculated AIC_c weights (w_i) for each model and used them as relative measures of evidence for each model (Akaike 1983; Burnham and Anderson 2002). We completed all AIC_c calculations in R using the AICcmodavg package (Mazerolle 2015). We used multimodel inference to reduce uncertainty associated with using a single model (Buckland et al. 1997; Burnham et al. 2011). We used estimates of L_∞ from the VBGM, Gompertz, and logistic models to find the model average asymptotic length (L̄_∞; Katsanevakis and Maravelias 2008; Yin and Tzeng 2009). We calculated standard error on the L̄_∞ estimate using an equation presented by Katsanevakis (2006).

We summarized diets (n = 55) using percent frequency of occurrence (O_i), mean percent frequency by number (MN_i), and prey-specific abundance (P_i). We calculated frequency of occurrence as O_i = J_i P⁻¹ × 100, where J_i was the number of stomachs containing a particular food item and P was the total number of stomachs containing food (Chipps and Garvey 2007). We calculated mean percent composition by number as below, where P was the total number of stomachs containing food, Q was the number of prey item categories, and N_ij, was the number of food category i in fish j (Chipps and Garvey 2007).

We calculated prey-specific abundance (P_i) for each food item i as P_i = (ΣS_i/ΣS_t), where ΣS_i was the total number of food item i observed and ΣS_t was the total number of prey items in stomachs with food item i in them (Amundsen et al. 1996).

We collected 155 channel catfish during the sampling period, most of which we collected using gill nets (n = 136). We collected a smaller proportion of fish using boat electrofishing (n = 17) and baited hoop nets (n = 2). Sampling efforts of gill nets, boat electrofishing, and hoop nets were 85 net-nights, 3.9 h, and 4 net-nights, respectively. We calculated mean catch per unit effort estimates of gill nets, boat electrofishing, and baited hoop nets as 1.6 fish/net-night, 4.4 fish/h, and 0.5 fish/net-night, respectively. We have provided catch per unit effort and water temperatures for sampling events in Table S1. The mean TL of the sample was 466 mm (SD = 117 mm), and the length-frequency distribution (Figure 3) indicates a majority of fish collected were >400 mm in length.

We summarized length and relative weight data using the Gabelhouse (1984) five-category system. The number of individuals in the Gabelhouse (1984) categories of stock (S), quality (Q), preferred (P), memorable (M), and trophy (T) were 34, 94, 16, 1, and 0, respectively. Ten fish were shorter than the minimum stock length (280 mm). We estimated proportional size distributions with 95% confidence intervals as follows: PSD = 77 ± 8, PSD-P = 12 ± 6, PSD-M = 1 ± 2, and PSD-T = 0. We estimated incremental PSDs with 95% confidence intervals as follows: PSD S-Q = 23 ± 8, PSD Q-P = 65 ± 9, PSD P-M = 11 ± 6, and PSD M-T = 1 ± 2. We calculated relative weight (W_r) for all channel catfish collected (n = 155; Table 1). Mean W_r of the substock, stock, quality, and preferred categories were 98, 93, 93, and 91, respectively.

We estimated age for 148 channel catfish using lapilli and included 143 total fish from age 3 to age 19 in catch-curve analysis. Independent readers agreed on 87% of assigned ages and agreement within 1 y was 96%. An age was agreed upon for all individuals after mutual examination. Estimated ages ranged from age 2 (n = 5) to age 19 (n = 1). Age-0 (young-of-the-year) and age-1 fish were absent from the sample. The age-frequency plot (Figure 4) showed the largest age classes in the sample were age-3, −4, and −7 fish, while other age classes were less common. We excluded age-2 fish from catch-curve analysis because we suspect they were not sampled effectively. We estimated instantaneous mortality as Z = 0.163 ± 0.079 (95% CI) using weighted linear regression. We calculated annual total mortality (A = 17.8% ± 9.4 [95% CI]) from instantaneous mortality.

We fit four candidate growth models to length-at-age data from 148 channel catfish (ages 2–19, length 154–721 mm; Figure 5). The VBGM was selected by AIC_c as the best approximating model of Cheat Lake channel catfish growth (w_i = 0.39), whereas the power (w_i = 0.38, ΔAIC_c = 0.02) and Gompertz (w_i = 0.17, ΔAIC_c = 1.65) models were also supported (Table 2). The logistic model was the least supported model (w_i = 0.06, ΔAIC_c = 3.74). Using model-averaging of the parameter estimates from the three asymptotic models (Table 3), we estimated L̄_∞ as 589.767 mm (SE = 16.772).

We compared Cheat Lake channel catfish growth to growth standards produced from populations throughout the species' range (Table 4). Using the Hubert (1999b) growth standards, Cheat Lake channel catfish mean lengths at age were at the 75th percentile or above for ages 3–8. Age-9 fish were between the 50th and 75th percentile, whereas age-10 fish were between the 25th and 50th percentile. We found similar results using the Relative Growth Index. Fish of ages 3–9 exhibited a mean RGI value >100, whereas the mean RGI value of age-10 fish was 94.85 (SD = 18.17).

We quantified stomach contents for 55 channel catfish ranging from 274 to 606 mm TL (mean = 458.2, SD = 73.0) and the fish contained food items 74.5% of the time. Of the stomachs containing food items, 31.7% contained detritus and 2.4% contained detritus only. Fall diets were numerically dominated by aquatic invertebrates (Table 5). Chironomidae and Chaoboridae larvae were the most common prey items, whereas Ephemeroptera and Sialidae larvae were also frequently present (Table 5). Piscivory was observed—fish remains were present in 9.8% of stomachs with food items.

Terrestrial food items were present in 39.0% of stomachs. A wide diversity of terrestrial invertebrate taxa were consumed, with Lepidoptera being the most common (Table 5). Adult terrestrial insects were commonly identified in stomach contents. Mammalian remains were also present in channel catfish diets—Rodentia were found in 4.9% of stomachs and were the only prey items present in those stomachs. Channel catfish stomachs also contained seasonally available fruits and acorns 14.6 % of the time. Acorns Quercus spp., grapes Vitis spp., and paw-paw Asimina triloba seeds were identified in stomach contents.

Given the lack of regional published data on channel catfish growth, the robust population in Cheat Lake provided an opportunity to gather data on age, growth, and diet within a West Virginia hydropower reservoir. Channel catfish in Cheat Lake exhibited faster than average growth (Hubert 1999b; Jackson et al. 2008). Low exploitation may contribute to fish reaching relatively old ages compared with other populations. Fall diet was also comparable to other studies. However, the high incidence of terrestrial items possibly suggests increased availability related to constant, small water-level fluctuations typical of hydropower operations. These data provided valuable information for potential future management of the population and for comparison with other populations in the region.

Cheat Lake's channel catfish population included an abundance of quality-length fish (>410 mm; Gabelhouse 1984). However, size structure estimated above could be biased because of selectivity of sampling gears (Colombo et al. 2008). Colombo et al. (2008) found channel catfish PSD in the Wabash River, Indiana, differed by >40 when comparing samples collected with hoop nets versus alternating-current electrofishing. In a Missouri impoundment, Sullivan and Gale (1999) found similar PSDs from hoop-net and gill-net samples. Using multiple gears in this study could have reduced biases associated with a single sampling gear, but a vast majority of fish were collected using gill nets because of low catch rates using other gears. Use of gill nets as the primary sampling gear likely excluded small channel catfish. Future studies could incorporate trawls (Phelps et al. 2011) and wooden slat traps, angling, or trotlines (Santucci et al. 1999) to improve catch rates of small and exceptionally large fish, respectively.

The catch numbers from baited electrofishing were similar to nonbaited electrofishing, but baited electrofishing required less time investment (∼120 s/site). The method appeared to concentrate local fish around the bait, but did not appear to draw additional fish from other areas. We noted presence of cheese trimmings in the stomachs of collected fish during dissection. This method may be more effective in lotic systems where flows carry scent of baits downstream or smaller impoundments. Further study is required to determine whether using bait during electrofishing can actually increase catch rates.

Cheat Lake channel catfish W_r was >90 for all length categories. Condition appeared to be similar to six Missouri River reservoirs, where W_r estimates ranged from 89 to 93, with a single reservoir estimated as 81 (Bouska et al. 2011). Barada and Pegg (2011) found channel catfish condition declined below average as fish grew longer, attributing this trend to differences in food availability for length categories. However, Cheat Lake has a large forage base, dominated by gizzard shad Dorosoma cepedianum, emerald shiner Notropis atherinoides, and three Lepomis species (D. M. Smith, unpublished data). Cheat Lake channel catfish W_r may decline below average on account of intraspecific competition, resulting from large numbers of quality-length (>410 mm) fish. Another possible explanation may be due to seasonal differences in W_r (Blackwell et al. 2000). We collected fish in late summer and fall after spawning had taken place.

We estimated Cheat Lake channel catfish annual total mortality as A = 17.8% using catch-curve analysis. Hubert (1999a) reported annual mortalities ranging from 13% to 88% from >50 channel catfish populations. The annual mortality estimate presented above is relatively low compared with the range of estimates in other populations. We speculate low mortality rates could be related to low exploitation. Haxton and Punt (2004) estimated annual mortality as 15.7% in the Ottawa River, while claiming there is little fishing pressure for the species. Low mortality rates may allow fish to reach relatively old ages. A maximum observed age in Cheat Lake, of 19 y, is relatively old compared with most populations. Bouska et al. (2011) also noted low mortality rates (12–25%) and long-lived fish with maximum ages ranging from 14 to 28 y, suggesting 28 y was the oldest wild channel catfish on record. Hubert (1999a) observed a most frequent maximum age of 8 y and noted few bodies of water produce fish older than 15 y.

Cheat Lake channel catfish experience rapid growth based on the standards presented by Hubert (1999b) and Jackson et al. (2008). Channel catfish growth has been correlated with a number of factors, including forage abundance, latitude, and limnological variables (Shoup et al. 2007; Rypel 2011). Abundant gizzard shad, emerald shiners, and Lepomis spp. in Cheat Lake may provide a forage base conducive to rapid channel catfish growth. Rypel (2011) described a latitudinal countergradient when normalizing for mean annual temperature. Cheat Lake is located just south of the West Virginia–Pennsylvania state line (Figure 2) and may experience fast growth rates due to moderate latitudinal and thermal influences. Meta-analysis of growth data from both lentic and lotic systems found growth positively correlated with lentic habitats (Rypel 2011). The shallow transitional section, coves, and upper riverine section of Cheat Lake may promote fast growth rates. Shoup et al. (2007) found a positive correlation with channel catfish growth and littoral area in 11 Illinois reservoirs.

Frequently in fisheries science, somatic growth is described using a single model selected a priori (generally the VBGM; Katsanevakis 2006; Katsanevakis and Maravelias 2008). Katsanevakis and Maravelias (2008) noted the VBGM often had the highest L_∞ estimate. This trend was also noted in the present study. Selecting the VBGM a priori could lead to overestimates of L_∞. Future growth modeling efforts should use an information theoretic approach comparing multiple models to minimize biases associated with use of a single model.

Diets of Cheat Lake channel catfish are consistent with results of other diet studies. Numerous studies observed extensive use of macroinvertebrates and a large presence of dipterans in channel catfish diets (Griswold and Tubb 1977; Weisberg and Janicki 1990; Michaletz 2006). Piscivory appeared to be infrequent, as was observed by Crumpton (1999). Infrequent encounters of fish in channel catfish stomachs could be the result of selection of prey items requiring less energy expenditure or handling time. Also, fish could have been lost via regurgitation due to sampling stress. Sutton et al. (2004) reported large piscivorous fish have higher regurgitation rates than small nonpiscivorous fish. Stomach content retention by gill-net collected channel catfish may be high because they are primarily caught by entanglement of spines, not wedging (Sutton et al. 2004).

Cheat Lake channel catfish were found to use terrestrial food items between 13 October and 4 December 2014. Use of terrestrial food items has been documented in other systems (Bailey and Harrison 1948; Hill et al. 1995; Edds et al. 2002). Inclusion of terrestrial food items in fall diets may supplement growth or may simply be the result of opportunistic foraging. Sweka and Hartman (2008) used bioenergetics modeling to determine that inclusion of terrestrial invertebrates in diets improved brook trout Salvelinus fontinalis growth in two West Virginia streams. Although trophic dynamics in small streams and reservoirs are quite different, consumption of vulnerable terrestrial organisms could provide calories with small energetic investment. Channel catfish have been described as opportunistic foragers (Bailey and Harrison 1948; Lewis 1976). Use of terrestrial prey items may be due to increased availability of terrestrial insects and tree fruits. Water-level fluctuations as a result of hydropower operations could have led to displacement of terrestrial food items through inundation of previously exposed substrate.

Our data on size structure, age structure, mortality, condition, growth, and fall diets of the channel catfish population in Cheat Lake will inform future management decisions. Additionally, this information may be applicable to the management of other Central Appalachian channel catfish populations because existing scientific literature on the population dynamics of channel catfish is limited for this geographic region. Based on the fast growth and abundance of quality-length (>410 mm; Gabelhouse 1984) channel catfish in Cheat Lake, further management may not be needed at this time. Presently, angling effort for channel catfish in Cheat Lake is unknown. Conducting an angler survey would allow fisheries managers to quantify angling effort and harvest rates for channel catfish on Cheat Lake and also determine angler attitudes about size structure and abundance.

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Table S1. Details on individual sampling events for channel catfish Ictalurus punctatus in Cheat Lake, West Virginia. Dates provided were from the year 2014. Water temperatures presented were measured by the U.S. Geological Survey water gauge on the Cheat River at Albright, West Virginia, at 2400 hours. Gear was denoted as EGN (experimental gill nets), HN (hoop nets), GN (46-m gill nets), and BEF (boat electrofishing). See text for thorough explanations of net dimensions.

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Data A1. Hilling CD, Welsh SA, Smith DM. Data from: Age, growth and fall diet of channel catfish in Cheat Lake, West Virginia. Journal of Fish and Wildlife Management, 7(2):xx–xx. Archived in Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.8583t

G. Merovich and D. Wellman provided constructive comments that improved previous versions of this manuscript. We thank E. Irwin and an anonymous reviewer for comments that further improved the manuscript. We also thank J. Aldinger, E. Miller, A. Rizzo, and P. Thompson for their assistance in collection and processing of samples. Funding for this project was provided by Harbor Hydro Holdings, LLC., and West Virginia Division of Natural Resources. This study was performed under the auspices of West Virginia University IACUC protocol 11-0403.

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