A Reexamination of the Relationship between Electrofishing Catch Rate and Age‐0 Walleye Density in Northern Wisconsin Lakes

Hansen, Michael J.; Newman, Steven P.; Edwards, Clayton J.

doi:10.1577/m03-014.1

Cited by 39 publications

(74 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For lakes larger than 25.75 km circumference, we restricted analysis to surveys in which at least 16.1 km of shoreline were sampled (following Ceded Territory age-0 walleye sampling protocols). We excluded surveys conducted in water temperatures less than 10°C and greater than 21°C (Hansen et al 2004) and those in which survey reliability was classified as low because of suboptimal conditions known to influence catchability (e.g., low water clarity) to ensure consistency in vulnerability to sampling. When multiple surveys meeting these criteria in a given lake-year were available, catch rates were averaged for that lake-year.…”

Section: Walleye Recruitmentmentioning

confidence: 99%

Predicting walleye recruitment as a tool for prioritizing management actions

Hansen

Carpenter

Gaeta

et al. 2015

Can. J. Fish. Aquat. Sci.

155

View full text Add to dashboard Cite

Abstract:We classified walleye (Sander vitreus) recruitment with 81% accuracy (recruitment success and failure predicted correctly in 84% and 78% of lake-years, respectively) using a random forest model. Models were constructed using 2779 surveys collected from 541 Wisconsin lakes between 1989 and 2013 and predictor variables related to lake morphometry, thermal habitat, land use, and fishing pressure. We selected predictors to minimize collinearity while maximizing classification accuracy and data availability. The final model classified recruitment success based on lake surface area, water temperature degree-days, shoreline development factor, and conductivity. On average, recruitment was most likely in lakes larger than 225 ha. Low degree-days also increased the probability of successful recruitment, but primarily in lakes smaller than 150 ha. We forecasted the probability of walleye recruitment in 343 lakes considered for walleye stocking; lakes with high probability of natural reproduction but recent history of recruitment failure were prioritized for restoration stocking. Our results highlight the utility of models designed to predict recruitment for guiding management decisions, provided models are validated appropriately.Résumé : Un modèle de forêt aléatoire pour la catégorisation du recrutement de dorés jaunes (Sander vitreus) s'est avéré exact dans 81 % des cas (prédiction correcte du succès ou de l'échec du recrutement pour 84 % et 78 % des années-lac, respectivement). Des modèles ont été élaborés à partir de 2779 évaluations obtenues pour 541 lacs du Wisconsin de 1989 à 2013, et de variables prédictives reliées à la morphométrie des lacs, à l'habitat thermique, à l'utilisation du sol et à la pression de pêche. Nous avons sélectionné les variables prédictives de manière à minimiser la colinéarité tout en maximisant l'exactitude de la catégorisation et la disponibilité des données. Le modèle final catégorisait le succès de recrutement en fonction de la superficie du lac, des degrés-jours de température du lac, d'un facteur d'aménagement des berges et de la conductivité. En moyenne, le recrutement était plus probable dans les lacs de plus de 225 ha. De faibles degrés-jours se traduisaient également par une probabilité accrue de succès du recrutement, mais principalement dans les lacs de moins de 150 ha. Nous avons prédit la probabilité de recrutement de dorés jaunes dans 343 lacs considérés comme candidats pour l'ensemencement de dorés; la priorité en ce qui concerne l'ensemencement aux fins de rétablissement a été donnée aux lacs présentant une forte probabilité de reproduction naturelle, mais un historique récent d'échec du recrutement. Nos résultats soulignent l'utilité de modèles conçus pour prédire le recrutement pour ce qui est d'orienter les décisions de gestion, pourvu que ces modèles soient validés adéquatement. [Traduit par la Rédaction]

show abstract

Section: Walleye Recruitmentmentioning

confidence: 99%

Predicting walleye recruitment as a tool for prioritizing management actions

Hansen

Carpenter

Gaeta

et al. 2015

Can. J. Fish. Aquat. Sci.

155

View full text Add to dashboard Cite

show abstract

“…For example, in Wisconsin lakes, measurement error ratios were also greater than 1.0 for electrofishing CPEs of age-0 and adult walleyes Sander vitreus (Rogers et al 2003;Hansen et al 2004;Schoenebeck and Hansen 2005) and largemouth bass Micropterus salmoides, smallmouth bass, northern pike Esox lucius, and muskellunge Esox masquinongy (Schoenebeck and Hansen 2005). Lower measurement errors of abundance estimates than of CPEs indicate that mark-recapture methods provide a more precise index of adult brook trout and brown trout abundance than CPE in Wisconsin streams.…”

Section: Discussionmentioning

confidence: 98%

“…Measurement error ratios (Y/X) between electrofishing CPE and adult population abundance were greater than 1.0 for 10 of the 11 models tested, so CPE was generally measured with greater error than adult population abundance, as is often true when adult population abundance is estimated by markrecapture (Rogers et al 2003;Hansen et al 2004;Schoenebeck and Hansen 2005). For example, in Wisconsin lakes, measurement error ratios were also greater than 1.0 for electrofishing CPEs of age-0 and adult walleyes Sander vitreus (Rogers et al 2003;Hansen et al 2004;Schoenebeck and Hansen 2005) and largemouth bass Micropterus salmoides, smallmouth bass, northern pike Esox lucius, and muskellunge Esox masquinongy (Schoenebeck and Hansen 2005).…”

Section: Discussionmentioning

confidence: 98%

Relationship between Electrofishing Catch Rate and Adult Trout Abundance in Wisconsin Streams

Bergman

Hansen

Nate

2011

N American J Fish Manag

Self Cite

View full text Add to dashboard Cite

The relative quickness and cost effectiveness of single‐pass electrofishing samples (catch per effort [CPE]) argues for their use over multiple‐pass estimates for enumerating stream fish populations. However, CPE can be an imprecise index of fish abundance and therefore may be less useful than more‐precise indices of abundance, such as those obtained from mark–recapture or depletion methods, for assessing trout populations. We evaluated the use of single‐pass electrofishing samples (catch/mi) as abundance (number/mi) predictors of adult brook trout Salvelinus fontinalis and brown trout Salmo trutta in Wisconsin streams by quantifying the relationship between electrofishing CPE and adult trout abundance. First, we tested for linearity of the relationship between CPE and adult trout abundance while accounting for measurement error. Next, we explained the residual variability in the relationship using physical and biological attributes of surveyed streams. We found that the relationship between CPE and adult trout abundance was proportional (linear) for both brook trout and brown trout. Electrofishing catchability did not differ significantly between trout species or among seasons or ecoregions. However, catchability was significantly greater for towed‐barge electrofishing (0.64; 95% confidence interval, 0.55–0.73) than backpack electrofishing (0.23; 0.09–0.59). Adult trout abundance alone explained 82% of the variation in CPE for backpack units and 87% of the variation in CPE for towed‐barge units. Stream habitat features such as stream slope, mean stream width, and pool density failed to explain additional variation in catchability. Our results indicate that CPE can provide a reliable index of adult trout abundance in Wisconsin streams, but mark–recapture methods may still be needed to obtain a more precise estimate of abundance. However, because time and effort can be reduced 50% if single‐pass CPEs are used instead of mark–recapture abundance estimates to predict adult trout abundance, managers may choose to sacrifice higher precision for reduced sampling effort. Received December 20, 2010; accepted June 17, 2011

show abstract

“…In contrast, problems associated with using fisheryindependent data as an abundance index have not received as much attention because the objective of these surveys is usually to representatively sample the target population. Yet, nonlinearity in the proportionality between abundance and CPE can still arise in survey data (e.g., Swain and Sinclair, 1994;Hansen et al, 2004), despite accounting for as many confounding factors as possible given available data. Thus, the assumption of constant catchability may remain invalid and applying several methods to adjust for space or time-varying catchability when using survey abundance indices may be prudent (Wilberg and Bence, 2006;Wilberg et al, 2010).…”

Section: Countrymentioning

confidence: 99%