Analysis
Our analysis was developed in R 4.1.3 (R Core Team 2021). We began by
filtering the data, retaining only datasets and patches compatible with
our criteria, resulting in 76 metacommunities containing 1190 patches.
Then, we implemented the bias control procedure (Appendix), which
resulted in 100 unbiased samples for each metacommunity. From these we
generated 7600 SLOSS comparisons. The 100 samples for each dataset
represent uncertainty in the SLOSS outcomes in each metacommunity due to
resampling. We developed an algorithm that automatically classified the
SLOSS comparisons into SS > SL (positive responses of
biodiversity to fragmentation), SS = SL (no evidence of a biodiversity
responses to fragmentation), or SL > SS (negative responses
of biodiversity to fragmentation). The algorithm classifies each SLOSS
comparison based on the difference between the two species accumulation
curves in the interval between the areas of (i) the largest patch on the
large-to-small curve and (ii) all patches except the largest patch on
the small-to-large curve (Fig. 1-d).
Next, we fitted generalized linear mixed effects models to determine
whether the SLOSS outcome depended on the general taxon of the
metacommunity. We assumed a binomial distribution of the response in our
models, predicting the likelihood of a dataset being SS >
SL. Because SL > SS was much rarer than SS >
SL, modelling SL > SS would result in poor model fit. We
therefore only present models predicting SS > SL, but we
note that results for SL > SS are opposite to those for SS
> SL, as expected (Fig. 2). We also included patch size
evenness in the model because previous work suggested that the
likelihood of observing SS > SL strongly increases with
decreasing patch size evenness, i.e., as the difference in size between
small and large patches increases (Riva & Fahrig 2022). The model
included a random effect for metacommunity to account for the dependency
among the 100 SLOSS comparisons generated for each metacommunity.