Data analysis
The data was analyzed based on self-regressive linear models with two parameters of interest, namely the permanence probability of the taxon in the occurrence of the disturbance \(\left(\psi\right)\), and the colonization probability in a substrate without previous occurrence of the taxon \(\left(\theta\right)\). The standard model used has the following structure:
\begin{equation} {O_{t}\sim Bern\left(P_{t}\right)\backslash n}{P_{t}=\theta+O_{t-1}\bullet\psi}\nonumber \\ \end{equation}
where Ot was the occurrence of the taxon at timet . This model was modified to fit each stage of the experiment. We used Bayesian inference to adjust the models to the data and to estimate the credibility intervals (CI; 95%) for the parameters of interest. All analyses were conducted in R (R Development Core Team 2020), using the runjags package (Denwood 2016). We calculated the CI using the methodology of re-sampling by relevance, in a Monte Carlo Markov chain iteration (Gelman et al. 2000, Andrade and Kinas 2008). In all analyses we used 5 parallel chains, burning the first 1000 iterations and sampling every 50 iterations, in a total of 5000 chain steps. The priori distributions of all analysis were not informative.
The combinations of the traits of the insects characterize the life history strategies for the organisms (Poff et al. 2006, Verberk et al. 2013, Sarremejane et al. 2020). We elaborated individual hypotheses for each life history strategy, because many groups of insects had similar traits. The hypotheses are related to estimates of the parameters permanence and colonization and are based on information about how the functional traits may be affected these parameters (Table 1).