Imaging and 3D model of the substratum
We digitally reproduced ~ 0.25 m2areas of the reef, by extracting images from video recordings and
applying SfM algorithms. We delimited each area with a black PVC quadrat
(50 x 50 cm) leveled on the bottom. In some cases, macroalgae such asDyctiota spp. or Lobophora spp. covered up to 80% of the
substrate and we manually removed the algae. We used a NIKON
COOLPIX-AW100 with illumination from two 2500 lumen lights (Sola Video
2500) (to film high-definition videos (1920 x 1080p / 30fps) of each
quadrat). We used a cross level
attached to the quadrat (4.2 x 1.6 cm) to scale and orient the model
parallel to the seawater surface (Supplementary Methods Figure 1). To
generate accurate 3D reconstructions, we attached 12 ground control
points (GCPs) to the quadrat (Supplementary Methods Figure 1). To
capture each scene in hundreds of pictures with different focal
distances and 70% overlap, a SCUBA diver filmed each area following a
spiral pattern, starting 2 meters over the reef and finishing at
~ 50 cm (Young et al. 2017). Finally, we
extracted three high-definition images per second from each video using
the open-source software FFmpeg (www.ffmpeg.org), which yielded to 500
– 700 images per quadrat.
We built 3D dense point clouds and polygon meshes with Agisoft Metashape
v1.6. The workflow to obtain dense point clouds, meshes and DEMs has
been described in detail in previous studies (Burns et al. 2015;
Bayley & Mogg 2020). Modifications and full details can be found in
Supplementary Methods: Table 1. To obtain the coordinates of
scleractinian and octocoral recruits, we marked and labeled the position
of each recruit in the point cloud. Next, we manually annotated the
point cloud based on substratum type, differentiating recruits
(scleractinians ≤ 4 cm wide, octocorals ≤ 5 cm height) from calcareous
rock, igneous rock sand and benthic invertebrates (scleractinian coral,
octocoral base, and sponge). We excluded the PVC quadrat and the area
beyond each quadrat from the analyses.
Our approach differs from most 2.5D methods, which use digital elevation
models (DEMs) to study topography. Instead, we calculated slope and
roughness directly from the point cloud at scales of 5, 10, 20, 50 and
100 mm. We also developed a new metric, the Topographic Exposure Index
(TEI), which quantifies the degree of exposure to the surrounding
environment. This metric distinguishes between flat, concave and convex
topographic features in the 3D models at mm/cm grains.
TEI is the orthogonal distance
from each point of the cloud to each point of a smoother version of the
same cloud (expressed in m). Positive values are assigned to points
above the smoother surface (more exposed, such as those on a ’hill’),
and negative values to those that fall below it (less exposed, such as
those in a ‘hole’). The TEI is defined relative to the surrounding
landscape, and a location with a positive TEI (a ‘hill’) could
potentially be ‘pointed’ towards the water surface, or horizontally, or
even directly down if it is located on the underside of an overhanging
area. Slope, roughness, and the smooth cloud used to calculate TEI were
calculated with CloudCompare v2.9 (see Supplementary Methods Table 2).
The code for calculating TEI is written in R and available at
github.com/AdamWilsonLab/meshSDM.