Abstract
BackgroundGlobal 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.MethodWe 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.ResultsGeographically 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.ConclusionsThis 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.
Highlights
Estimating the key relationships between species distribution and environmental variables is essential for natural resource conservation and ecosystem-based fishery management (Grüss et al, 2017)
Developing a global regression model by pooling all the survey data in the large region may mask the local variability in the processes being studied such an approach is more convenient to conduct
We applied the geographically weighted regression (GWR) to test the assumption of spatial stationarity in estimating the relationships between Yellow Perch distribution and environmental variables in Lake Erie
Summary
Estimating the key relationships between species distribution and environmental variables is essential for natural resource conservation and ecosystem-based fishery management (Grüss et al, 2017). Accounting for spatial nonstationarity can improve our understanding of the interactive process between species distribution and environmental variables at various spatial scales (Windle et al, 2010, 2012; Sadorus et al, 2014; Liu et al, 2017; Li et al, 2018; Bi et al, 2019). 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.
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