scholarly journals Peer Review #3 of "Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie (v0.2)"

2019 ◽  
Keyword(s):  
Peer Review ◽  
Environmental Effects ◽  
Yellow Perch ◽  
Lake Erie

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

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7350
Author(s):  
Changdong Liu ◽  
Junchao Liu ◽  
Yan Jiao ◽  
Yanli Tang ◽  
Kevin B. Reid
Keyword(s):  
Environmental Effects ◽  
Geographically Weighted Regression ◽  
Regression Models ◽  
Yellow Perch ◽  
Lake Erie ◽  
Regression Coefficients ◽  
Weighted Regression ◽  
Local Regression ◽  
Spatially Varying ◽  
Spatial Locations

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.

scholarly journals Exploring spatial nonstationary environmental effects on species distribution: a case study of Yellow Perch in Lake Erie

2019 ◽  
Author(s):  
Changdong Liu ◽  
Junchao Liu ◽  
Yan Jiao ◽  
Yanli Tang
Keyword(s):  
Species Distribution ◽  
Environmental Effects ◽  
Regression Models ◽  
Yellow Perch ◽  
Lake Erie ◽  
Management Unit ◽  
Regression Coefficients ◽  
Local Regression ◽  
Global Regression ◽  
Spatially Varying

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: GWR 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 location. Consistent species-environment relationships were found in the east basin for juveniles and in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit implied the variation of relationships at a scale finer than the management unit. Conclusions: This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The superiority of GWR over the GAM highlights the limitations of using one global regression model to explore species-environment relationships at a large spatial scale and provides insights for managing Yellow Perch at finer scales.

scholarly journals Exploring spatial nonstationary environmental effects on species distribution: a case study of Yellow Perch in Lake Erie

2019 ◽  
Author(s):  
Changdong Liu ◽  
Junchao Liu ◽  
Yan Jiao ◽  
Yanli Tang
Keyword(s):  
Species Distribution ◽  
Environmental Effects ◽  
Regression Models ◽  
Yellow Perch ◽  
Lake Erie ◽  
Management Unit ◽  
Regression Coefficients ◽  
Local Regression ◽  
Global Regression ◽  
Spatially Varying

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: GWR 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 location. Consistent species-environment relationships were found in the east basin for juveniles and in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit implied the variation of relationships at a scale finer than the management unit. Conclusions: This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The superiority of GWR over the GAM highlights the limitations of using one global regression model to explore species-environment relationships at a large spatial scale and provides insights for managing Yellow Perch at finer scales.

scholarly journals Relative effects of biotic and abiotic factors during early life history on recruitment dynamics: a case study

2017 ◽  
Vol 74 (7) ◽  
pp. 1125-1134 ◽  
Author(s):  
Fan Zhang ◽  
Kevin B. Reid ◽  
Thomas D. Nudds
Keyword(s):  
Life History ◽  
Yellow Perch ◽  
Lake Erie ◽  
Abiotic Factors ◽  
Zooplankton Abundance ◽  
Biotic Factors ◽  
Large Lakes ◽  
Middle Term ◽  
Biotic And Abiotic Factors ◽  
Juvenile Stages

The relative effects of biotic and abiotic factors, and the life-history stages upon which they act to affect fish recruitment, vary among species and ecosystems. We compared the effects of spawning stock biomass, and factors operating at early-term (encompassing the egg, yolk-sac larval, and first few days of swim-up larval stages), middle-term (including the swim-up larval and pelagic juvenile stages), and late-term (over the benthic juvenile stage) on recruitment by yellow perch (Perca flavescens) in the western basin of Lake Erie between 1999 and 2013. Variation of recruitment was mainly driven by middle-term effects. Then, abiotic factors, such as warming rate and wind speed, more strongly affected recruitment than did biotic factors. Among middle-term biotic factors, the top-down effect of yearling walleye (Sander vitreus) abundance was stronger than the bottom-up effect of zooplankton abundance. Similar to marine species, physical processes appear to strongly affect recruitment dynamics of Lake Erie yellow perch over its pelagic larval and juvenile stages, demonstrating the importance of physical and biological processes in understanding fish population dynamics in large lakes.

Synchronous changes in length at maturity across four species of Lake Erie fish with different harvest histories

2021 ◽  
Author(s):  
Davíð Gíslason ◽  
Robert L. McLaughlin ◽  
Beren W Robinson
Keyword(s):  
Yellow Perch ◽  
Lake Erie ◽  
Large Scale ◽  
White Perch ◽  
Water Levels ◽  
White Bass ◽  
Morone Chrysops ◽  
Harvest Intensity ◽  
Ecosystem Changes ◽  
Post Hoc

Decreases in size at maturation in harvested fish populations can reduce productivity and resilience. Delineating the causes for these changes in maturation is challenging. We assessed harvest and large-scale ecosystem variability as causes for changes in maturation in four Lake Erie fishes. Regulated harvests of Yellow Perch (Perca flavescens) and Walleye (Sander vitreus) are greater than unregulated harvests of White Perch (Morone americana) and White Bass (Morone chrysops). Our assessment considered cohort data from 1991-2012 for each species. We used a conceptual model of harvest-induced plasticity to show that changes in female length at 50% maturity (L50) were unrelated to harvest intensity in all species. We then demonstrated that changes in female L50 among cohorts were synchronous across species. Post-hoc analysis of variables capturing year-to-year variation in climatic and lake conditions suggested L50 was larger when water levels were near the norm for the study period and smaller at low and high levels. We conclude that changes in L50 were most strongly related to ecosystem changes unrelated to harvest intensity.

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