Improving fishery-independent indices of abundance for a migratory walleye population

Berger, Aaron M.; Jones, Michael L.; Zhao, Yingying

doi:10.1016/j.jglr.2012.09.012

Cited by 15 publications

(8 citation statements)

References 52 publications

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“…Gill-net selectivity is influenced by both availability (the likelihood of a fish being in the immediate sampling area and encountering the net) and contact selectivity. Availability can be influenced by environmental factors, net placement, deployment duration and timing, and associations between species caught in the net (Berger et al 2012;Kraus et al 2017). Contact selectivity is influenced by factors such as net material (e.g., visibility, elasticity, etc.)…”

Section: Discussionmentioning

confidence: 99%

Gill Net Selectivity for Fifteen Fish Species of the Upper San Francisco Estuary

Wulff¹,

Feyrer²,

Young³

2022

SFEWS

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Gill-net size selectivity for fifteen fish species occurring in the upper San Francisco Estuary was estimated from a data set compiled from multiple studies which together contained 7,096 individual fish observations from 882 gill net sets. The gill nets considered in this study closely resembled the American Fisheries Society’s recommended standardized experimental gill nets for sampling inland waters. Relationships between gill-net mesh sizes and the sizes for each fish species retained in them were estimated indirectly using generalized linear modeling and maximum likelihood. Selectivity curves are provided for each species to inform researchers about population characteristics of fishes sampled with similar gill nets.

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Section: Discussionmentioning

confidence: 99%

Gill Net Selectivity for Fifteen Fish Species of the Upper San Francisco Estuary

Wulff¹,

Feyrer²,

Young³

2022

SFEWS

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“…The most common response of Walleye biomass or abundance to climate was no effect (39% of tests), while positive and negative results were equally prevalent in the literature (25% of tests, each). Higher summer temperatures were associated with declining Walleye catch rates in some systems (e.g., Robillard and Fox 2006; Haxton 2015; Kraus et al 2017), but in other cases, warmer temperatures led to higher Walleye catch rates (Schupp 2002; Berger et al 2012). Spring warming rates were positively related to the abundance of age‐0 Walleye in western Lake Erie (Roseman et al 1999), while other studies detected no relationship between Walleye abundance and temperature, illustrating the complexity of this relationship (Hansen et al 2019; Pennock and Gido 2021).…”

Section: Resultsmentioning

confidence: 99%

It’s Complicated and It Depends: A Review of the Effects of Ecosystem Changes on Walleye and Yellow Perch Populations in North America

Hansen

Ruzich

Krabbenhoft

et al. 2022

N American J Fish Manag

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Walleye Sander vitreus and Yellow Perch Perca flavescens are culturally, economically, and ecologically significant fish species in North America that are affected by drivers of global change. Here, we review and synthesize the published literature documenting the effects of ecosystem changes on Walleye and Yellow Perch. We focus on four drivers: climate (including temperature and precipitation), aquatic invasive species, land use and nutrient loading, and water clarity. We identified 1,232 tests from 370 papers, split evenly between Walleye (n = 613) and Yellow Perch (n = 619). Climate was the most frequently studied driver (n = 572), and growth or condition was the most frequently studied response (n = 297). The most commonly reported relationship was "no effect" (42% of analyses), usually because multiple variables were tested and only a few were found to be significant. Overall responses varied among studies for most species-response-driver combinations. For example, the influence of invasive species on growth of both Walleye and Yellow Perch was approximately equally likely to be positive, negative, or have no effect. Even when results were variable, important patterns emerged; for example, growth responses of both species to temperature were variable, but very few negative responses were observed. A few relationships were relatively consistent across studies. Invasive species were negatively associated with Walleye recruitment and abundance, and higher water clarity was negatively associated with Walleye abundance, biomass, and production. Some variability in responses may be due to differences in methodology or the range of variables studied; others represent true context dependence, where the effect of a driver depends on the influence of other variables. Using common metrics of impact, publishing negative results, and robust analytical approaches could facilitate comparisons among systems and provide a more comprehensive understanding of the responses of Walleye and Yellow Perch to ecosystem change.

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“…The partnership index survey (PIS) was conducted using the standard gillnets by the Ontario Ministry of Natural Resources and Forestry and the Ontario Commercial Fisheries’ Association in the Canadian waters of Lake Erie in annual late summer and early fall. A depth-based stratified random sampling design was used and the number of sample sites was determined according to the surface area of each depth stratum (see Berger, Jones & Zhao, 2012; Pandit et al, 2013; Liu et al, 2018). The survey gillnet gangs composed of 14 different mesh sizes were set on the bottom and suspended (canned) in the water column with a mean soak time of 20 h.…”

Section: Methodsmentioning

confidence: 99%

Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie

Liu

Jiao

et al. 2019

PeerJ

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Background Global regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales. Method We applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships. Results Geographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU. Conclusions This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.

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Improving fishery-independent indices of abundance for a migratory walleye population

Cited by 15 publications

References 52 publications

Gill Net Selectivity for Fifteen Fish Species of the Upper San Francisco Estuary

Gill Net Selectivity for Fifteen Fish Species of the Upper San Francisco Estuary

It’s Complicated and It Depends: A Review of the Effects of Ecosystem Changes on Walleye and Yellow Perch Populations in North America

Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie

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