Figure 3 : At the upper
panel, description of the framework used to calculate plot’sfilling index es. Logic behind the filling index procedure,
which tests whether the observed plot show a polygon-shaped pattern when
compared to simulated line-shaped plots. A) Calculation of the observed
plot filling index . First, the abundance-environment plot is
rasterized, indicating whether there is one or more data points within
the range of each cell (purple cells). Then, the upper boundary of the
point cloud (purple line) is defined by connecting the upper cells of
each raster column (dark purple cells). Finally, the rate of occupation
is calculated below the upper boundary (i.e., sum of purple cells/sum of
all cells bellow the purple line). Values of 1 indicate that the area
bellow the ceiling is fully filled by data points. B) Generation of a
null model based on 999 simulated line-shaped point clouds. The
line-shaped simulations use the same environmental data and sample size
as the observed data, and resemble the general trend of the observed
plot. The filling index is calculated for each simulated
distribution as described in A. C) Statistical significance of the
observed filling index is different to the distribution of the
simulated line-shaped patters. The probability of the observedfilling index (purple line) is greater than the simulated ones
(blue distribution) and is calculated at a statistical significance of
p<0.05. At the lower
panel, exemplification of the
performance of the filling index to identify polygon-shaped
patterns in two simulated plots: polygon-shaped plot (D), and
line-shaped plot (E). For more details on evaluation methods and results
see Appendix S6.
Figure 4