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 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.

Exploring non-stationary and scale-dependent relationships between walleye (Sander vitreus) distribution and habitat variables in Lake Erie

2017 ◽  
Vol 68 (2) ◽  
pp. 270 ◽  
Author(s):  
Changdong Liu ◽  
Rong Wan ◽  
Yan Jiao ◽  
Kevin B. Reid
Keyword(s):  
Species Distribution ◽  
Lake Erie ◽  
Mixed Model ◽  
Model Performance ◽  
Regression Coefficients ◽  
Aquatic Species ◽  
Local Regression ◽  
Explanatory Variables ◽  
Gwr Model ◽  
Habitat Variables

Global regression techniques that assume spatial stationarity are usually used to study the interaction between aquatic species distribution and habitat variables. In the present study, a local regression model, named geographically weighted regression (GWR), was used to question the spatial stationarity assumption in exploring the relationships between walleye (Stizostedion vitreum) distribution and habitat variables in Lake Erie. The GWR model resulted in a significant improvement of model performance over the two global linear and non-linear regression methods (a generalised least-squares (GLS) model and a generalised additive mixed model (GAMM)), accounting for residual spatial autocorrelation using the same response and explanatory variables as in the GWR model. The values of local regression coefficients from the GWR model changed among spatial locations significantly, implying spatially varying and scale-dependent relationships between walleye distribution and habitat variables. The k-means cluster analyses based on the t-values of local regression coefficients of GWR model characterised special zones of species–environment relationships of walleye in Lake Erie. In conclusion, spatial stationarity needs to be questioned in studying the relationships between aquatic species distribution and habitat variables and a non-stationary approach, such as GWR, is recommended as a complementary tool.

Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression

2007 ◽  
Vol 39 (10) ◽  
pp. 2464-2481 ◽  
Author(s):  
David C Wheeler
Keyword(s):  
Ridge Regression ◽  
Geographically Weighted Regression ◽  
Model Building ◽  
Weighted Regression ◽  
Diagnostic Tools ◽  
Local Regression ◽  
Explanatory Variables ◽  
Building Process ◽  
Global Regression ◽  
Spatially Varying

Geographically weighted regression (GWR) is drawing attention as a statistical method to estimate regression models with spatially varying relationships between explanatory variables and a response variable. Local collinearity in weighted explanatory variables leads to GWR coefficient estimates that are correlated locally and across space, have inflated variances, and are at times counterintuitive and contradictory in sign to the global regression estimates. The presence of local collinearity in the absence of global collinearity necessitates the use of diagnostic tools in the local regression model building process to highlight areas in which the results are not reliable for statistical inference. The method of ridge regression can also be integrated into the GWR framework to constrain and stabilize regression coefficients and lower prediction error. This paper presents numerous diagnostic tools and ridge regression in GWR and demonstrates the utility of these techniques with an example using the Columbus crime dataset.

scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

Developing global regression models for metabolite concentration prediction regardless of cell line

2017 ◽  
Vol 114 (11) ◽  
pp. 2550-2559 ◽  
Author(s):  
Silvère André ◽  
Sylvain Lagresle ◽  
Anthony Da Sliva ◽  
Pierre Heimendinger ◽  
Zahia Hannas ◽  
...  
Keyword(s):  
Cell Line ◽  
Regression Models ◽  
Metabolite Concentration ◽  
Global Regression ◽  
Concentration Prediction
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