Spatial niche comparison: Realized niche breadth and overlap
The realized niche breadths for both taxa were quantified using Levins’
B2 metric (Levins 1968), which ranges from 0 to 1, with higher values
indicating broader habitat ranges. The overlap between those ranges was
described with Schoener’s D (Schoener 1968; Warren et al.2008) and Warren’s I (Warren et al. 2008). Both indices
range from 0 to 1, i.e. from complete divergence and no overlap to
identical niche breadth and complete overlap. Species with perfectly
opposite responses to the same environmental gradient may have high
niche overlap (Warren et al. 2019). Thus, we calculated the rank
correlation coefficients rho (Spearman 1904) to compare
correlations to the environmental factors. Both, the realized habitat
breadth for each taxon and their overlap were calculated in geographic
and environmental space using a sample of 100,000 points from the
background, with a tolerance of 0.0001 (Warren et al. 2019).
Definitions and additional information about those terms can be found in
Supplementary Methods Table 3. All metrics were calculated with the R
package ENMTools 1.0 (Warren et al. 2021). The realized niche
breadth for each taxon was visualized by projecting their habitat
suitability scores on the polygon meshes generated from the point
clouds.
We tested whether the scleractinian and octocoral niches were identical
with a niche identity test (Warren et al. 2008, 2010), and also
performed a background test to compare the habitat overlap calculated
with the empirical occurrences to the overlap expected by chance with
random occurrences (Warren et al. 2019). Definitions and
additional information about those terms can be found in Supplementary
Methods Table 3. Identity and background tests were completed in both,
geographic and environmental space with the R package ENMTools 1.0
(Warren et al. 2021).
Lastly, we calculated the distribution of suitability scores between
taxa and their relative abundances and tested whether the amount of
suitable habitat available for each taxon was significantly different
with a Mann-Whitney U test.
RESULTS
The most important variables
included for model fitting were the % coral, % octocoral, % sponge,
% calcareous rock, % igneous rock, % sand, slope, and both, roughness
and TEI calculated at 10-mm and 100-mm scales. Fifty-nine octocoral
recruits and 32 scleractinians were used to test the performance of the
SDMs. Bohl et al. tests (2019)
revealed our models outperformed null models, in both geographic and
environmental space (Supplementary Methods Figure 4), with
AUCTest values in geographic and environmental space of
0.84 and 0.91 for octocorals and 0.77 and 0.89 for scleractinians. In
addition, AUCDIFF was 0.01 in geographic space and 0.00
in environmental space, indicating low-level to no model overfitting.
The importance of each environmental predictor to the realized niche
breadth of recruits and the marginal response curves for each taxon are
shown in Figure 3. The distribution of recruits was not random on the
reef. For octocoral recruits, reef geomorphology was more important than
the presence of benthic invertebrates to characterize suitable
microhabitats. The three most important predictors were roughness at 10
mm scale, the percentage of calcareous rock, and slope (importance
values of 0.14 ± 0.02, 0.05 ± 0.01, and 0.04 ± 0.01, respectively;
Figure 3A). Highly suitable microhabitats for octocoral recruits were
located on fine-scale topographic features of calcareous rock with
slopes between 15 and 80 degrees, with holes and crevices < 10
mm being more suitable than bumps and hammocks of similar dimensions
(Figure 3, B8). At the scale of 100 mm, we found a positive relationship
between habitat suitability and TEI, with higher suitability values on
large convex regions of the reef such as boulders (Figure 3, B5). The
cover of benthic invertebrates, ranked lower in importance, but also
affected the suitability of microhabitats for octocoral recruits.
Recruits were not found on live invertebrates, and the models projected
that microhabitats in direct contact with cnidarians (i.e. areas covered
> 50%) were highly unsuitable for octocorals with
suitability scores ~ 0.2; Figure 2, B4 and B10). In
comparison, microhabitats in direct contact with sponges were more
suitable, with suitability scores ~ 0.5 (Figure 3, B6).
The least important predictor for octocoral recruit distributions was
sand.
As for octocorals, fine-scale roughness (10 mm-scale) was the most
important predictor to characterize the realized spatial niche of
scleractinian recruits (importance of 0.09 ± 0.01; Figure 3C). But in
contrast to octocorals, the % cover of scleractinian corals and sponges
were the most important predictors of suitable habitat (importance of
0.05 ± 0.02, and 0.05 ± 0.02, respectively; Figure 3C). Substrate type
was less important. Microhabitats in direct contact with a scleractinian
coral or a sponge (i.e. areas covered > 50%), were
moderately suitable for scleractinian recruits (suitability scores
~ 0.6 and 0.5, respectively, Figure 3, D2 and D3). Slope
and roughness at 100 mm-scale also affected habitat suitability for
scleractinian recruits, but in contrast to octocorals, flat and exposed
areas of the reef at 100 mm-scales were more suitable (Figure 3, D4).
The estimated probability of suitable habitat for recruits of both taxa
can be seen in Figure 4, and Video 1 shows the rotation of Figure 4C and
4D in 3D (the Supplementary material)