Hypothesis 4: Species interactions lead to the presence of
non-random combinations of traits, hence defining the properties of the
island trait spaces
We compared the observed metrics for each island’s trait space with the
values obtained from a random sample of the species from the Canary
Islands’ subterranean species pool. We hypothesised finding larger
differences between observed and random values on mature islands, where
community and evolutionary processes have optimised the use of the trait
space via the establishment of stronger species interactions (Wilson
1969; Borregaard 2016; 2017). Therefore, we expected that functional
richness and evenness would resemble the value expected by chance in
young islands, as these offer more ecological opportunities for
speciation and new species will evolve occupying all the vacant niche
space. However, as the island reaches its maximum topological complexity
and the species richness gets to its equilibrium, we expected functional
richness to decrease and functional evenness to increase, reflecting how
the establishment of more interactions among species at the community
level will promote a reorganisation the use of the island’s trait space
in a non-random manner. In senescent islands where extinction dominates
the island dynamics, we expected the strength of species interactions to
relax, leading to values of functional richness and evenness again
similar to those expected by chance alone.
We applied null modelling to test whether the hypervolume metrics for
each island were different than expected from a random sample of species
from the Canarian subterranean species pool. We expressed the observed
value as the actual functional richness and evenness of the hypervolume
for each island. Then, we repeatedly (999 times) randomly subsampledn species from the species pool, where n equalled the total
species richness in each island, and extracted the same two functional
metrics. For each permutation, we estimated standard effect sizes and
associated p-values using the BAT function ses to assess the
significance of the deviation from the null expectations.