Factors related to growth and survival of larval walleyes: implications for recruitment in a southern Great Plains reservoir

Quist, Michael C.; Guy, Christopher S.; Bernot, Randall J.; Stephen, James L.

doi:10.1016/j.fishres.2003.09.042

Cited by 30 publications

(39 citation statements)

References 35 publications

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“…Previous analyses have identified biotic predictors of walleye recruitment, including adult stock size (Chevalier 1977;Hansen et al 1998;Beard et al 2003) as well as predation and competition by other species (e.g., Forney 1977;Madenjian et al 1996;Fielder et al 2007). Abiotic factors affecting walleye recruitment have also been identified; these include lake size (Nate et al 2000), spring water temperatures (e.g., Serns 1982;Hansen et al 1998;Quist et al 2003), a surrogate for regional climate variability (Beard et al 2003), and (or) water levels (Chevalier 1977;Quist et al 2004). However, the utility of these prior analyses for forecasting recruitment has not been evaluated in most cases.…”

Section: Introductionmentioning

confidence: 99%

Predicting walleye recruitment as a tool for prioritizing management actions

Hansen

Carpenter

Gaeta

et al. 2015

Can. J. Fish. Aquat. Sci.

155

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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]

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Section: Introductionmentioning

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

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“…Similar results were observed by Hoxmeier et al (2006) in Illinois reservoirs and by Hansen et al (1998) in Escanaba Lake, Wisconsin. Low water spring temperatures have also been associated with poor growth of walleye in a southern Great Plains reservoir (Quist et al 2004). Predation appears to be another factor affecting larval walleye survival (Roseman et al 1996;Hansen et al 1998;Hoxmeier et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Egg thiamine concentration affects embryo survival in Lake Erie walleye

et al. 2010

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We quantified thiamine in eggs of Lake Erie walleye to determine if differences exist between spawning stocks within the Maumee and Sandusky rivers, both of which drain into the Western Basin. In spring 2004, eggs of walleye were collected in the Maumee River at three occasions (early, peak and late of the spawning run) and in the Sandusky River during the spawning run. After collection, eggs were fertilized with a known amount of milt, incubated and embryo survival was determined at the pigmented eyed stage. Thiamine and its derivatives were analyzed using high performance liquid chromatography. Thiamine pyrophosphate (TPP) was the most abundant form of vitamin B 1 present in eggs (60-95% of the total vitamin B 1 concentration). Total thiamine concentrations in walleye eggs from the Maumee and Sandusky rivers at the peak of the spawning run averaged 6.1±1.6 nmolg −1 and 5.0±2.9 nmolg −1 , respectively. Our results also indicated that Maumee River stock survival to the eyed stage embryo declined as the spawning season progressed (72%, 59% and 37% in the early, middle and late of spawning run, respectively) as well as the total thiamine and TPP concentrations. At the peak of the spawning run, survival to the eyed stage embryo did not differ significantly between stocks 59% versus 65% in the Maumee and Sandusky rivers, respectively, and thiamine concentrations were not significantly different between sites.

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“…There are several explanations for this decline: First, fry migration to the inshore area (see above). Second, the larval period is considered to be critical in the life history of many fish species (Quist et al, 2004) with predation and starvation as the major causes of mortality (Urpanen et al, 2005). Natural mortality could therefore be the main reason for the density decline.…”

Section: Comparisons Of Fry Density and Species Composition Between Bmentioning

confidence: 99%

“…The size of the trawl nets's mouth can also influence the size of fish caught (Mous et al, 2002), because larger fish have greater swimming capability (Godø et al, 1990) and are able to escape the opening of a trawl net with a small mouth. In many studies, different types of ichthyoplankton nets and samplers have been used for catching fish larvae and to estimate the abundance of early juveniles (Cada and Loar, 1982;Mooij, 1996;Quist et al, 2004;Urpanen et al, 2005;Scharf et al, 2009), but a common feature of most of these studies is the relatively small mouth opening of the sampling tools (usually less than 1 m 2 ).…”

Section: Comparison Of Densities and Length Distributions Reached By mentioning

confidence: 99%

“…The usual methods used in ichthyoplankton studies consist of various types of nets, which are either towed behind the boat (Cada and Loar, 1982;Anderson et al, 1998;Čech et al, 2005;Kratochvíl et al, 2008) or fixed on the sides or forward end of the boat (Frankiewicz et al, 1996(Frankiewicz et al, , 1997Quist et al, 2004). Because of the selectivity of these types of nets (Treasurer, 1978), both the estimated densities and size distributions determined by the different methods vary (Wanzenböck et al, 1997;Tischler et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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