Identifying the drivers of structural complexity on Hawaiian coral reefs using photogrammetric measures of linear rugosity

Structural complexity on coral reefs is commonly associated with high abundance of reef fish and Scleractinian corals. Thus, methods to efficiently and accurately assess structural complexity are relevant for coral reef monitoring and conservation. Using photogrammetry and Structure from Motion (SfM), it is now possible to quantify the structural complexity of coral reefs using 3D models. This study uses SfM to quantify linear rugosity, the ratio of contour distance to horizontal distance across the reef surface, across spatial scales at reefs in the Main and Northwestern Hawaiian Islands (MHI and NWHI). Cross-scale patterns of rugosity differed among reef types: coral-dominated reefs exhibited the highest structural complexity at small scales (millimeters to centimeters), while reefs with large variations in substrate topography (due to ridges, canyons, boulders, or other geologic features) exhibited more structural complexity at large scales (decimeters to meters). Degraded reefs were characterized by low rugosity across all spatial scales. Using these methods, cross-scale differences in rugosity between the MHI and NWHI were attributed to higher coral cover at sites surveyed in the MHI rather than differences in underlying reef topography between regions. These observations highlight how both biological and geological factors contribute to habitat complexity across a range of scales, supporting a variety of functions (i.e., predator refugia, increased foraging area, shelter from currents, etc.) that enable reefs to sustain diverse communities of reef fish and invertebrates. Overall, the scale of measurement and the rate at which linear rugosity changes across scales are important considerations when assessing reef structure. This study demonstrates how traditional methods of assessing reef condition can be scaled up using SfM to identify aspects of the reef (biotic vs. geologic) that drive structural complexity and contextualize change in complexity over time.