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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

From: Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

Variable name Explanation
Hab_auto strength of spatial habitat autocorrelation
Hab_anis anisotropy of spatial habitat autocorrelation
Hab_smooth smoothness of transition between habitats
Hab_type continuous or categorical habitat
σSD strength of randomness in animal movement
σα movement bias towards attraction centre
σω habitat selection strength
σran strength of directional persistence
σran2 reducing directional persistence in preferred habitats
Errorspat strength of spatial measurement error
Meth_SLRM spatial logistic regression model, dummy points are randomly generated inside the minimal convex polygon
Meth_SLRM_s as for SLRM but with spatial 2D smooth aiming to reduce spatial autocorrelation
Meth_SLRM_w as for SLRM but with strong weights assigned to dummy points
Meth_SSM step selection model
Meth_iSSM integrated step selection model including model selection with respect to the autocorrelation terms
Meth_ST-PPM spatio-temporal point process model including model selection with respect to the autocorrelation terms
  1. Predictors related to habitat start with ‘Hab...’, predictors related to animal movement with ‘ σ...’, and predictors related to the statistical method applied to simulated tracking data with ‘Meth_...’