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Advantages of PIT tags are their small size, longevity, and low cost compared to other tags. They are often used in fisheries to study movement patterns and survival or to estimate population size. However, PIT tags are limited by their short detection distance. Mobile PIT antennas may increase the utility of PIT tags in fisheries. In this study, we synthesized current detection efficiency literature for mobile PIT antennas, determined physical factors that decreased PIT tag detection probabilities for our antenna, determined factors that influenced the proportion of PIT‐tagged suckers detected by our mobile antenna, and summarized techniques used to increase detections of PIT‐tagged suckers using mobile antennas in a wadable stream. Our literature review indicated that tag size and orientation were the most important factors affecting detection probabilities. However, our manual testing suggested that the detection probability for our antenna was primarily influenced by water depth of the tag and distance from the antenna. Our sucker detection data showed that detection efficiency in our stream was most influenced by discharge, turbidity, and sample date. Tracking methods that include targeting key habitats (e.g., rootwads) and using natural features to congregate tagged fishes (e.g., riffles or pinch points) may increase detection efficiency in wadable streams. This is the first formal review of factors affecting mobile PIT antenna detection efficiency. The published literature, combined with our study results, indicates that several factors need to be considered prior to mobile PIT antenna tracking.

Population parameter estimates from mark-recapture studies are dependent on individuals retaining marks or tags. Therefore, tag retention estimates are needed for different tag types and anatomical tagging locations. Few studies have empirically quantified the bias from tag retention on fish population parameters that are derived from mark-recapture studies. We examined differences in retention between T-bar anchor tags and PIT tags as well as among four anatomical locations for PIT tags in Brown Trout Salmo trutta in a tailwater fishery in Arkansas, USA. We also estimated the relative bias of tag type and PIT tag location on apparent survival estimates from Cormack-Jolly--Seber models. Tag retention for the anchor tags was 15.1% lower than that for the PIT tags after 1 year and 46.1% lower after 4 years. Greater PIT tag retention resulted in less biased estimates of apparent survival for PIT tags (average −7.1%) than for anchor tags (average −37.8%). However, PIT tags that were placed in different anatomical locations had varying retention rates, so the degree of relative bias that was associated with their apparent survival estimates also varied. Inserting the PIT tags in the cheek or dorsal musculature provided the greatest retention for Brown Trout and may provide the least biased apparent survival estimates from future mark-recapture studies.

Scale and hierarchy have received less attention in aquatic systems compared to terrestrial. Walleye Sander vitreus spawning habitat offers an opportunity to investigate scale’s importance. We estimated lake-, transect-, and quadrat-scale influences on nearshore walleye egg deposition in 28 Minnesota lakes from 2016-2018. Random forest models (RFM) estimated importance of predictive variables to walleye egg deposition. Predictive accuracies of a multi-scale classification tree (CT) and a quadrat-scale CT were compared. RFM results suggested that five of our variables were unimportant when predicting egg deposition. The multi-scale CT was more accurate than the quadrat-scale CT when predicting egg deposition. Both model results suggest that in-lake egg deposition by walleye is regulated by hierarchical abiotic processes and that silt/clay abundance at the transect-scale (reef-scale) is more important than abundance at the quadrat-scale (within-reef). Our results show machine learning can be used for scale-optimization and potentially to determine cross-scale interactions. Further incorporation of scale and hierarchy into studies of aquatic systems will increase our understanding of species-habitat relationships, especially in lentic systems where multi-scale approaches are rarely used.

Spawning habitat assessments often focus on substrate composition, but few studies have predicted shoal substrates by using environmental factors. We developed a model for predicting shoal substrates in Belle Lake, Minnesota, using wind fetch and shoreline relief characteristics. Percent composition of four substrate classes (silt, sand, gravel, and rock), water depth estimated at 1 m from shore (shoal slope), effective wind fetch measured using a GIS model, and riparian bank height derived from LIDAR imaging were determined at 50 transects. Classification and regression tree (CART) analysis grouped substrates into categories, and general additive modeling described the effects of three predictor variables on the percent composition of substrate classes. The CART analysis correctly grouped 39 of 50 transects into four categories, and misclassifications primarily resulted from the movement of sand. Effective fetch most influenced silt (low fetch) and rock (high fetch) substrate classes, shoal slope was predictive of rock, and riparian height was useful in distinguishing sand from gravel. These results demonstrate the utility of a single empirical model for determining shoal substrate composition. Fisheries managers can use this technique to determine potential fish spawning locations and identify potential areas for habitat restoration or protection projects. Received December 5, 2016; accepted May 5, 2017 Published online July 19, 2017