Interpreting Coefficients of brms Bernoulli family model

I am struggling with interpreting model results from a brm() model. The first result uses scaled and centered data with command scale(df$column, scale = TRUE, center = TRUE). > summary(global.use.initial) Family: bernoulli Links: mu = logit Formula: use ~ 1 + iogs_std + ipred_std + idepth_std + idry_std + iopenwater_std + ibroodaltered_std + icc_std + ivosum_std + ic + entrancedir + boxage_std + pguardtype + neighbor_std + wetland.type + bpp + (1 | year) + (1 | site) Data: Masterintialusewatt.initalonly.naomit (Number of observations: 825) Draws: 4 chains, each with iter = 16000; warmup = 8000; thin = 1; total post-warmup draws = 32000 Group-Level Effects: ~site (Number of levels: 3) Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sd(Intercept) 1.04 1.06 0.03 3.90 1.00 9107 10772 ~year (Number of levels: 4) Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sd(Intercept) 0.61 0.47 0.15 1.87 1.00 9120 13385 Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept -0.25 0.99 -2.47 1.79 1.00 11429 8999 iogs_std 0.65 0.18 0.31 1.01 1.00 26064 24495 ipred_std -0.31 0.20 -0.70 0.09 1.00 21572 23319 idepth_std 0.55 0.15 0.27 0.84 1.00 32939 23456 idry_std 0.20 0.11 -0.02 0.42 1.00 34767 23558 iopenwater_std -0.18 0.10 -0.40 0.00 1.00 35240 22896 ibroodaltered_std 0.12 0.12 -0.11 0.34 1.00 34813 20159 icc_std -0.42 0.17 -0.75 -0.10 1.00 34900 23951 ivosum_std -0.22 1.13 -2.49 1.98 1.00 41638 20542 icInland 0.66 1.71 -3.07 4…