Aquatic Nuisance Species in the Great Lakes and Mississippi River Basin-A Risk Assessment in Support of GLMRIS

Competition between Invasive Ruffe (Gymnocephalus cernua) and Native Yellow Perch (Perca flavescens) in Experimental Mesocosms

Ruffe (Gymnocephalus cernua) were introduced to North America from Europe in the mid-1980s and based on similar diets and habit use may compete with yellow perch (Perca flavescens). To examine competitive interactions between invasive ruffe and native yellow perch, individually marked perch and ruffe were placed in mesocosms in a small lake. Mesocosms allowed fish to interact and feed on the natural prey populations enclosed. In the first experiment, four treatments were assessed: 28 perch, 14 perch + 14 ruffe, 14 perch, and 7 perch + 7 ruffe. Yellow perch growth was significantly lower in the presence of ruffe (ANOVA, p = 0.005) than in treatments containing only perch. In a second experiment, an increasing density of one species was superimposed upon a constant density of the other in parallel treatment series. Growth rates of both ruffe and perch declined when ruffe density was increased (t test, p = 0.006). However, neither ruffe nor perch growth was affected by increasing perch density. Total stomach content mass of perch was significantly decreased by ruffe in both years (p < 0.02), but no effects of ruffe on the composition of perch diets were observed. Ruffe growth and food consumption was greater than that of perch for both experiments. Ruffe can outcompete yellow perch when both species depend on a limited benthic food resource. Thus there is reason for concern for the ecological effects of ruffe if they expand their range into Lake Erie or North American inland lakes that contain yellow perch.

Genomic analyses of Northern snakehead (Channa argus) populations in North America

Background

The introduction of northern snakehead (Channa argus; Anabantiformes: Channidae) and their subsequent expansion is one of many problematic biological invasions in the United States. This harmful aquatic invasive species has become established in various parts of the eastern United States, including the Potomac River basin, and has recently become established in the Mississippi River basin in Arkansas. Effective management of C. argus and prevention of its further spread depends upon knowledge of current population structure in the United States.

Methods

Novel methods for invasive species using whole genomic scans provide unprecedented levels of data, which are able to investigate fine scale differences between and within populations of organisms. In this study, we utilize 2b-RAD genomic sequencing to recover 1,007 single-nucleotide polymorphism (SNP) loci from genomic DNA extracted from 165 C. argus individuals: 147 individuals sampled along the East Coast of the United States and 18 individuals sampled throughout Arkansas.

Results

Analysis of those SNP loci help to resolve existing population structure and recover five genetically distinct populations of C. argus in the United States. Additionally, information from the SNP loci enable us to begin to calculate the long-term effective population size ranges of this harmful aquatic invasive species. We estimate long-term N_e to be 1,840,000–18,400,000 for the Upper Hudson River basin, 4,537,500–45,375,000 for the Lower Hudson River basin, 3,422,500–34,225,000 for the Potomac River basin, 2,715,000–7,150,000 for Philadelphia, and 2,580,000–25,800,000 for Arkansas populations.

Discussion and Conclusions

This work provides evidence for the presence of more genetic populations than previously estimated and estimates population size, showing the invasive potential of C. argus in the United States. The valuable information gained from this study will allow effective management of the existing populations to avoid expansion and possibly enable future eradication efforts.

Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs

Following decades of ecologic and economic impacts from a growing list of nonindigenous and invasive species, government and management entities are committing to systematic early- detection monitoring (EDM). This has reinvigorated investment in the science underpinning such monitoring, as well as the need to convey that science in practical terms to those tasked with EDM implementation. Using the context of nonindigenous species in the North American Great Lakes, this article summarizes the current scientific tools and knowledge - including limitations, research needs, and likely future developments - relevant to various aspects of planning and conducting comprehensive EDM. We begin with the scope of the effort, contrasting target-species with broad-spectrum monitoring, reviewing information to support prioritization based on species and locations, and exploring the challenge of moving beyond individual surveys towards a coordinated monitoring network. Next, we discuss survey design, including effort to expend and its allocation over space and time. A section on sample collection and analysis overviews the merits of collecting actual organisms versus shed DNA, reviews the capabilities and limitations of identification by morphology, DNA target markers, or DNA barcoding, and examines best practices for sample handling and data verification. We end with a section addressing the analysis of monitoring data, including methods to evaluate survey performance and characterize and communicate uncertainty. Although the body of science supporting EDM implementation is already substantial, research and information needs (many already actively being addressed) include: better data to support risk assessments that guide choice of taxa and locations to monitor; improved understanding of spatiotemporal scales for sample collection; further development of DNA target markers, reference barcodes, genomic workflows, and synergies between DNA-based and morphology-based taxonomy; and tools and information management systems for better evaluating and communicating survey outcomes and uncertainty.