The effectiveness of different meso-scale rugosity metrics for predicting intra-habitat variation in coral-reef fish assemblages

Abstract

Habitat structure is frequently an important variable affecting species' abundances and diversity, and identifying the key aspects and spatial scales of habitat complexity is critical for understanding the ecology and conservation of a range of communities. Many coral-reef fishes are intimately linked with benthic habitat structure, and previous research has demonstrated rugosity as an important predictive variable of assemblage parameters. However, these studies typically consider rugosity at small scales, amalgamate multiple habitat features, or are semi-quantitative. This study considers meso-scale rugosity (within 51 plots of 25 m 2 on a Belizean forereef) generated by varying coral densities, heights, and complexities. Seven rugosity metrics were calculated for each plot, and were regressed against each of 11 fish assemblage parameters. Intra-habitat variability of each fish parameter was significantly positively or curvilinearly correlated to at least one metric of mesoscale rugosity, but the metric generating the strongest correlation varied. The abundance of small fishes, and consequently most of the assemblage statistics (e.g., total fish abundance and diversity) were best predicted by the number of tall (>50 cm) corals. The abundance of damselfishes, parrotfish biomass, the abundances of medium-sized and large fishes, and total fish biomass were curvilinearly related to mean coral height. The abundances of wrasses and surgeonfishes were most strongly correlated with the number of corals within a plot. Because coral-generated mesoscale rugosity is an important factor influencing intrahabitat variation in fishes, it should be explicitly considered when investigating fish-habitat relationships and predicting the impacts of coral mortality on ecosystem processes and services.

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

“…In the absence of having access to an alternative and method proved highly accurate to recreate the three-dimensional structure of the reef, here we decided to use traditional metrics as proxy values for reef complexity (e.g., rugosity, substrate height). Although highly useful to understanding ecological processes and patterns [11,70,71], these metrics also introduce human error and noise [38] and, therefore, this variability will be reflected on the accuracy metrics. Therefore, the accuracy values here reported (79%-82%) for 3D reconstructions at reefscape scales reflects high fidelity of model estimates to traditional and ecologically relevant metrics of reef complexity, while using less detailed imaging techniques than the colony-scale exercise (e.g., downward facing video sequences from scooters or diver collected data from lawn-mowing patterns).…”

“…This model was run on simulated complexity data and could be significantly strengthened by the incorporation of habitat structural complexity metrics such as the ones produced by our framework. Similarly, questions relating to resource availability, diversity, and abundance of important reef species could be investigated by using this framework to improve on the precision and spatial extent of habitat complexity metrics, such as those used by [3,70]. These examples demonstrate how the presented framework could contribute to both an improvement of coral reef monitoring and a better understanding of the drivers behind reef biodiversity, function, and resilience [11,14,81,82].…”

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

“…It has also been demonstrated to be superior to this traditional approach when applied to modeled habitat as its not sensitive to the specific placement in 2D space of the measured transect [20]. However, other approaches, such as variance in height [17], slope, or even fractal dimension [16,42,43] may also prove useful as descriptors of structural complexity.…”

“…At very large spatial extents, variables such as bottom roughness and slope have been derived from airborne Lidar [10,11] and sub-surface acoustic profiling [12][13][14][15]. Over smaller areas the direct measurement of factors such as vertical relief, number of holes, surface roughness, and even fractal dimension [5,[16][17][18] have been commonly estimated by divers. Among these techniques there has always been a tradeoff between area covered and resolution obtained such that very precise and accurate methodologies were often only practical over very small areas.…”

Total volume of 3D small patch reefs reflected in aerial photographs can predict total species richness of coral reef damselfish assemblages on a shallow back reef

“…Belmaker et al (2007) demonstrated clearly that, on a small scale, the observed SAR in coral reef fish can be reproduced using random placement model simulations (RPMS). In contrast, Harborne et al (2012) highlighted the important influence of coral-generated meso-scale rugosity on reef fish species abundance and diversity, where tall reefs (>0.5 m) harbor more species than flat reefs. Accordingly, several small but tall reefs may harbor more species than a single flat (and short) reef of equivalent area.…”