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Table 2 Linear mixed effects models (LMEs) to estimate predictors of time spent within a patch for California horn sharks (Heterodontus francisci)

From: Active acoustic telemetry tracking and tri-axial accelerometers reveal fine-scale movement strategies of a non-obligate ram ventilator

Model Parameters       
ResponsePredictorsRandomLikelihoodAICTotal Deviance from ModelDeviance Explained (%)Chi-SquaredDegrees of freedomp value
Time in Patch1 (null model)(1|Track)ML 141.05    
Time in PatchDistance travelled to get to patch(1|Track)ML145.7135.73.79%0.58410.444
Number of patches per individual per night     5.02810.025
Time in PatchActivity within patch (% of time spent resting)(1|Track)ML146.1136.13.53%0.16510.685
Number of patches per individual per night     5.32410.021
Time in PatchArea of patch(1|Track)ML136.7126.710.18%13.34910.0002
Number of patches per individual per night     7.44610.006
  1. LMEs were done in the lmer framework in R. Time in patch was calculated as the total time (min) spent in a patch and derived from the start and end times of patch use from a First Passage Time (FPT) analysis. Distance travelled to get to patch (m) and area of patch (m2) were measured in ArcGIS. Activity within patch (% of time spent resting) was calculated using the k-means analysis. Number of patches per individual per night was added to each model to account for temporal autocorrelation associated with time spent, and individual per night was included as a random factor. Maximum Likelihood (ML) was used to obtain AIC and deviance values. Percent deviance explained was calculated by subtracting the total deviance from the model from the null deviance and dividing by the null deviance. The AIC values are from the model summary and for each model independently, as no model comparison was made. Chi-squared values, degrees of freedom, and p values were obtained from the Anova function in the car package in R