Image-based ecological assessment of deep-sea sponge, coral and other cnidarian assemblages through a morpho-functional approach

Abstract

AO_SCPLOWBSTRACTC_SCPLOWPorifera and Cnidaria are key phyla of marine ecosystem functioning. Slow-growing and long-lived, they show limited resilience to human disturbances and are therefore commonly used as indicator taxa of Vulnerable Marine Ecosystems. Their diversity and distribution largely rely on seafloor images, but image-based identification (ID) of sponges and cnidarians beyond class/order ranks is hindered by morphological plasticity and convergence, and the need of microscopic diagnostic characters or molecular markers that are not accessible on images. Higher-rank taxonomy also poorly reflects their morphological and functional diversity. To address this issue, we applied an image-based classification relying on multiple macro-morphological traits to define statistically morpho-functional groups (MFGs) based on these traits, then used as response variables in ecological analyses, instead of taxa. We applied this framework to seamounts and island slopes in the Mozambique Channel, a poorly sampled region where taxonomic limitations are acute. We aimed to evaluate the relevance of a sponge/cnidarian morpho-functional approach to characterise their diversity and distribution at multiscales, responses to environmental gradients and structuring roles on the megabenthic community. We identified 37 sponge and 34 cnidarian MFGs with varying distributions among sites. MFGs richness and beta diversity varied among seamounts, and between seamounts and island slopes. Hydrology explained 26% of the sponge MFG spatial structure at the channel scale, while topography and substrate played a main role at a smaller scale in the northern part of the channel (17-23%). Some sponge/cnidarian MFGs co-structured with other megabenthic taxa (e.g., sea urchins, comatulids, ophiuroids). This morpho-functional approach, especially effective for sponges, allowed quantifying morphological variation of habitat-forming taxa using qualitative traits, identifying environmental drivers shaping the spatial distribution of these traits, and characterising potential biogenic habitats they provide for other megabenthic organisms. The framework proposes metrics to detect and quantify the heterogeneity and spatial variability of potentially vulnerable biogenic habitats in imagery-based studies.