Table 1. Posterior inferences of the coefficients (on the logit-scale) in the best Bayesian zero-one-inflated beta distribution model on blue sharks’ proportion of time spent at the surface (0–1 m).

The first model component estimates the mean (linear predictor) in the model, and the second and third component the probability of zero and one, respectively. The factor levels ‘off shelf’ and ‘time bin’ 2 (06:00–12:00) are the baseline values in the model and are included in the intercept. Time bin 3 is the time period 12:00–18:00.

Model component	Effect	Estimate	SE	2.5% quantile	97.5% quantile
logit(mean)	Intercept	-1.110	0.005	-1.570	-0.686*
	as.factor(Shelf)—on	1.889	0.006	1.386	2.407*
	as.factor(Time bin) - 3	-0.236	0.003	-0.464	-0.013*
logit(Pr(y = 0))	Intercept	-0.364	0.007	-0.939	0.208
	as.factor(Shelf)—on	-5.757	0.031	-8.799	-3.828*
	as.factor(Time bin) - 3	0.643	0.009	-0.118	1.419
logit(Pr(y = 1))	Intercept	-71.576	0.863	-160.022	-17.622*
	as.factor(Shelf)—on	34.001	0.662	-0.438	102.495
	as.factor(Time bin) - 3	32.529	0.648	0.714	103.489*
	d	1.828	0.004	1.509	2.120*
	σ	1.273	0.006	0.817	1.815*

d—Regression coefficient in the linear predictor for the sum of the two shape parameters in the beta distribution

σ—Posterior mean of the variance of the random effect

* indicates a significant difference when the quantile range does not overlap zero