Hello all,

I am working with seabird count data, producing models in brms using the mgcv smooths available:

```
m1 <- brms::brm(CountIndivs ~ s(Date, k = 6) +
s(DayofYear, bs = 'cc', k = 6) +
s(daily_wind_speed, k = 4) +
s(wind_30dav, k = 4) +
s(upwelling_30dav, k = 4) +
s(SOI, k = 4) +
s(SAM, k = 4) +
(1|Port),
data = speciesdf,
family = zero_inflated_negbinomial(link = "log"),
cores = 4,
seed = 17,
iter = 4000,
warmup = 1000,
thin = 10,
refresh = 1000,
control = list(adapt_delta = 0.99))
```

I am running into issues when trying to interpret the output of the summary function:

```
summary(m1)
Family: zero_inflated_negbinomial
Links: mu = log; shape = identity; zi = identity
Data: speciesdat (Number of observations: 397)
Samples: 4 chains, each with iter = 4000; warmup = 1000; thin = 10;
total post-warmup samples = 1200
Smooth Terms:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sds(sDate_1) 1.83 1.78 0.06 6.60 1.00 1164 1171
sds(sDayofYear_1) 2.37 1.19 0.84 5.14 1.00 1192 1215
sds(sdaily_wind_speed_1) 2.55 2.43 0.09 8.96 1.00 1089 1162
sds(swind_30dav_1) 2.84 2.35 0.11 8.74 1.00 1239 1135
sds(supwelling_30dav_1) 1.87 1.69 0.06 6.56 1.00 1206 1202
sds(sSOI_1) 4.27 4.55 0.13 16.13 1.00 1098 884
sds(sSAM_1) 2.81 2.24 0.15 8.41 1.00 1185 1101
Group-Level Effects:
~Port (Number of levels: 4)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.66 0.65 0.02 2.33 1.00 1233 1210
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept -2.40 0.71 -3.91 -1.12 1.00 832 1163
sDate_1 -4.02 2.98 -10.51 -1.61 1.00 1195 1178
sdaily_wind_speed_1 -0.52 5.93 -14.26 9.32 1.00 978 1030
swind_30dav_1 -0.97 4.79 -8.98 10.03 1.00 1211 1160
supwelling_30dav_1 -1.74 4.84 -11.48 7.29 1.00 1308 1067
sSOI_1 2.39 8.98 -11.28 27.01 1.00 1141 1156
sSAM_1 4.54 4.01 -3.85 12.20 1.00 1116 1174
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 0.36 0.20 0.15 0.87 1.00 1166 1032
zi 0.29 0.17 0.02 0.58 1.00 1060 1238
Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
```

As I understand it, the ‘Smooth Terms’ (e.g. sds(sDate_1) represent the wiggliness of each term, and where the CI cross zero, there is little usefulness in including a smooth term on the given variable.

My question concerns how I interpret the ‘Population-Level Effects’ section of the summary output. Would terms with Population-Level Effects CI’s crossing zero be of little significance in explaining variability in the data used (i.e. the term is not a good predictor of the response variable)? In this example, ‘sDate_1’ is the only term where the CI’s do not cross zero.

I ask as I am running models for each species in my dataset (67 species) with each one containing the same predictors. This is producing a significant number of results and smooth plots, and I am trying to determine which terms should be included for each species in the publication, and which are of little significance and resigned to supplementary.

I realise this is quite a basic question but I have yet to be able to find a definitive answer.

Thank you very much.