Data analysis
All analyses were carried out in the R environment version 3.6.1 (R Core
Team 2019) and graphics were obtained through the packagesggplot2 , jtools and interactions (Wickham, 2016;
Long, 2019b, 2019a). Species climatic niches were estimated from the
weighted average values of mean annual temperature (bio 1) and annual
precipitation (bio 12) of all sites where each species was recorded.
Weights were based on the average AGWB of the species in each site
across all census intervals. We opted to weigh the species’ niches with
their AGWB rather than abundance because by better synthesizing the
species’ responses to local conditions. We ran the analysis for each
forest type separately because climate is strongly correlated with
forest type. This approach prevented us from including climate and
forest type in the same models, avoiding high variance inflation factors
(VIF > 4).
Balance between recruitment
and extinction
We tested if the number of extinct and recruited species differed
significantly from each other, and estimated the temporal trend of each
group (i.e., the interaction between extinct/recruited and year). For
this, we used generalized linear mixed effects models (GLMM) using
Poisson family. Because observations were nested within sites and within
census intervals, we accounted for the random effects of site and census
interval. We used the natural logarithm of the multiplication between
site sampled area and census interval length as an offset variable to
control for their effects on the species count.
count of species ~ status*year (eqn 1)
Equation 1 describes the global model of extinction/recruitment balance
with all variables included in the R notation. Interaction effects are
represented by “*”.