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

ABSTRACT:

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

SUBMITTER: Liu C 

PROVIDER: S-EPMC6661154 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature