Table 1 Possible predictors for statistical power and type I error rates in the context of habitat selection and large-scale attraction based on animal tracking data

strength of spatial habitat autocorrelation

anisotropy of spatial habitat autocorrelation

smoothness of transition between habitats

continuous or categorical habitat

strength of randomness in animal movement

movement bias towards attraction centre

habitat selection strength

strength of directional persistence

reducing directional persistence in preferred habitats

strength of spatial measurement error

spatial logistic regression model, dummy points are randomly generated inside the minimal convex polygon

as for SLRM but with spatial 2D smooth aiming to reduce spatial autocorrelation

as for SLRM but with strong weights assigned to dummy points

step selection model

integrated step selection model including model selection with respect to the autocorrelation terms

spatio-temporal point process model including model selection with respect to the autocorrelation terms