Native to the central United States, Flathead Catfish Pylodictus olivaris have invaded Atlantic coast rivers from Florida to Pennsylvania. They are now invasive in several subestuaries of the Chesapeake Bay, yet contemporary accounts of their distribution do not exist. Due to their piscivorous nature, Flathead Catfish could have deleterious impacts on native ichthyofauna, yet their feeding ecology has not been well described in these systems. We used a large-scale, stratified random sampling effort to describe the current distribution and feeding ecology of Flathead Catfish in Virginia tidal rivers. Low-frequency electrofishing was conducted at more than 1,500 sites in the James, Pamunkey, Mattaponi, and Rappahannock rivers in eastern Virginia, resulting in 766 Flathead Catfish being captured in the James, Pamunkey, and Mattaponi rivers. Flathead Catfish are abundant in the tidal James River from Richmond, Virginia, to the confluence of the Chickahominy River. A relatively new but established population was also observed in the Pamunkey River, where the highest observed densities of Flathead Catfish occurred near Williams Landing (37°36 0 21.49″N, 77°5 0 33.42″W) in New Kent County, Virginia. Stomachs collected from 731 Flathead Catfish revealed that they are piscivores that feed heavily on Gizzard Shad Dorosoma cepedianum, White Perch Morone americana, and various Alosa species. Analysis of trophic level, diet breadth, and feeding strategy demonstrated that Flathead Catfish are piscine specialists that occupy trophic positions indicative of an apex predator. Our results show that Flathead Catfish could have substantial per capita impacts on at-risk native species including American Shad Alosa sapidissima, Blueback Herring A. aestivalis, and Alewife A. pseudoharengus as they make seasonal migrations in and out of these river systems. Moreover, future range expansion of Flathead Catfish into the Rappahannock River is plausible, as established populations now exist in adjacent tributaries.

Biological invasions occur as a multistage process, and life history traits can change during the invasion process. Blue Catfish Ictalurus furcatus were introduced in three Virginia tidal tributaries of the Chesapeake Bay during the 1970s and 1980s but have expanded their range to almost all large tributaries of the bay. An understanding of the species' growth is important for evaluating impacts on other resident species and population dynamics. Virginia Blue Catfish exhibited wide variability in individual growth, prompting the testing of six alternative hypotheses (similar growth across space and time as well as variable growth by river system, sampling year, cohort, and both river system and time) on its growth dynamics within four Virginia tidal rivers (James, Mattaponi, Pamunkey, and Rappahannock rivers) over the period 2002-2016. Blue Catfish growth in Virginia was best explained by a model considering cohort and river as random effects. The Rappahannock River was the first in Virginia to receive Blue Catfish; growth was slower in this river than in the other systems during the observation period. Growth rates declined for all ages examined in the James, Mattaponi, and Pamunkey rivers but only for ages 7, 10, and 13 in the Rappahannock River. We did not generally observe synchronous growth responses among rivers, supporting that finer-scale factors may be influencing growth rates. This work suggests that the growth rates of nonnative species may decline over time and that comparisons of nonnative growth may be most useful when variability over space and time is considered.

Acidification has historically impaired Cheat Lake's fish community, but recent mitigation efforts within the Cheat River watershed have improved water quality and species richness. Presently, channel catfish Ictalurus punctatus are abundant and attain desirable sizes for anglers. We evaluated the age, growth, and fall diet of the population. We collected a sample of 155 channel catfish from Cheat Lake from 5 August to 4 December 2014, a subset of which we aged (n = 148) using lapillus otoliths. We fit four growth models (von Bertalanffy, logistic, Gompertz, and power) to length-at-age data and compared models using an information theoretic approach. We collected fall diets from 55 fish sampled from 13 October to 4 December 2014. Total lengths of individuals in the sample ranged from 154 to 721 mm and ages ranged from 2 to 19 y. We AICc-selected the von Bertalanffy growth model as the best approximating model, and the power and Gompertz models also had considerable support. Diets were numerically dominated by Diptera larvae, specifically Chironomidae and Chaoboridae, while 39% of stomachs contained terrestrial food items. This study provides baseline data for management of Cheat Lake's channel catfish population. Further, this study fills a knowledge gap in the scientific literature on channel catfish, because few previously published studies have examined the population ecology of channel catfish in the Central Appalachian region.

In temperate waters, growth and mortality of bony fishes are frequently estimated from age information derived from the examination of annular rings on hard structures (e.g., otoliths). However, determining ages from hard structures can be time consuming, often requires sacrificing fish, and has associated costs for supplies and personnel time in processing or reading structures. Subsampling based on a target number of fish per length bin is commonly used to reduce time and costs but may introduce biases into the estimation of population characteristics. We wanted to understand how interactive effects of bin width, gear selectivity, and length‐at‐age variability influence the estimation of growth parameters, total instantaneous mortality (Z), and age frequency. We developed a simulation model to generate populations under the assumption that growth followed the von Bertalanffy growth model; we then sampled from those populations for age analysis based on no gear selectivity, dome‐shaped selectivity, and logistic selectivity. Furthermore, we wanted to determine whether observed biases could be corrected by using a weighting procedure during growth model fitting. Fifteen subsampling schemes were evaluated, with five different length bin widths and three target subsample sizes for each bin (subsampling levels). Gear selectivity, variability in length at age, and estimation procedures had a greater and more predictable influence on growth parameters than bin widths for size‐based subsampling. Dome‐shaped gear selectivity was associated with biases in growth parameter and Z estimation. Weighted regression based on weighting factors calculated from the original sample's length frequency generally improved the consistency of growth parameter estimates among subsampling schemes but did not always improve accuracy. No bin widths or subsample sizes were clearly superior across modeled scenarios. Consequently, alteration of bin widths seems less useful in reducing biases than using alternative estimation methods for population characteristics of interest and considering other external factors.