Interpreting multivariate probit models

I've attempted to use some stats which are definitely a bit beyond me as my most advanced stats before was just an ANOVA, but it seemed to fit my data well and am finding it fun to mess around with. My experiment consisted of prey models predated by different predators, with two independent variables: whether the prey was grouped and the location of the prey (forest edge or not). I detected what predated the models, and as such have binary outcomes of 1 if attacked by a certain prey (e.g. bird, mammal etc) and 0 if not.

From my research, a multivariate probit model is used for multiple binary outcomes which may be linked/correlate in some whay (which i suspect), so I used this model.

I used the brms package to model each predator as such:

Birdmodel <- bf(Bird ~ Edge. * Grouped.,
                    family = bernoulli(link = "probit"))

Then combined them into one multivariate model:

model <- brm(
  Antmodel + Birdmodel + Rodentmodel + Paramodel + Othermodel,
  data = datatype,
  chains = 4,
  cores = 4,
  iter = 4000,
  seed = 123,
  family = bernoulli(link = "probit")
)

To analyze this data, I made a table showing estimates and confidence intervals of the model, using the following code:

library(broom.mixed)           
table <- tidy(model)

An example of the resulting table is this:

I am at a loss to determine whether the effect is significant or not, and the strength of the effect.

What parts of this table should I look at to determine the effect strength and significance?