Background: Detecting species at low abundance, including aquatic invasive species (AIS), is critical for making informed management decisions. Environmental DNA (eDNA) methods have become a powerful tool for rare or cryptic species detection; however, many eDNA assays offer limited utility for community-level analyses due to their use of species-specific (presence/absence) 'barcodes'. Metabarcoding methods provide information on entire communities based on sequencing of all taxon-specific barcodes within an eDNA sample.Aims: Evaluate measures of fish species detections, community diversity, and estimates of relative abundance based on eDNA metabarcoding and traditional fisheries sampling approaches in the context of fish community characterization and AIS survellience. Materials and Methods:In 2016, eight limnologically diverse lakes (surface area range: 13 -1,728 ha) in Michigan, USA were sampled using a variety of traditional fisheries gears to characterize fish community composition. Environmental DNAs from surface (33 ± 6, mean ± 1 SD) and benthic (14 ± 2) water samples from each lake were isolated and amplified for two metabarcoding markers (mitochondrial 12S and 16S rDNA loci) using fish-specific primers. Fish species detected within each lake were determined by comparing the sequencing data to a database of sequences from native Michigan fish species and 19 AIS on the Michigan's Watch List.Results: Analysis of species accumulation curves indicated multi-locus eDNA metabarcoding assays can enhance species detection capacities and characterize 95% of a fish community in fewer sampling efforts than traditional gear (range: 2 -62, median: 14). In addition, all AIS detected in traditional gear samples were also detected by eDNA, while some AIS detected by eDNA assays were absent from traditional gear samples. | 369SARD et Al.

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Fish often exhibit complex movement patterns, and quantification of these patterns is critical for understanding many facets of fisheries ecology and management. In this study, we estimated movement and fishing mortality rates for exploited walleye (Sander vitreus) populations in a lake-chain system in northern Michigan. We developed a state-space model to estimate lake-specific movement and fishery parameters and fit models to observed angler tag return data using Bayesian estimation and inference procedures. Informative prior distributions for lake-specific spawning-site fidelity, fishing mortality, and system-wide tag reporting rates were developed using auxiliary data to aid model-fitting. Our results indicated that postspawn movement among lakes was asymmetrical and ranged from approximately 1% to 42% per year, with the largest outmigration occurring from the Black River, which was primarily used by adult fish during the spawning season. Instantaneous fishing mortality rates differed among lakes and ranged from 0.16 to 0.27, with the highest rate coming from one of the smaller and uppermost lakes in the system. The approach developed provides a flexible framework that incorporates seasonal behavioral ecology (i.e., spawning-site fidelity) in estimation of movement for a mobile fish species that will ultimately provide information to aid research and management for spatially structured fish populations.