In situ Growth Rate Assessment of the Hexactinellid Sponge Asconema setubalense Using 3D Photogrammetric Reconstruction

Abstract

We describe the first application of a non-invasive and novel approach to estimate the growth rate of Asconema setubalense (Porifera, Hexactinellida) through the use of 3D photogrammetric methodology. Structure-from-Motion techniques (SfM) were applied to videos acquired with the Politolana ROTV in the El Cachucho Marine Protected Area (MPA) (Cantabrian Sea) on three different dates (2014, 2017, and 2019) over six years. With these data, a multi-temporal study was conducted within the framework of MPA monitoring. A complete 3D reconstruction of the deep-sea floor was achieved with Pix4D Mapper Pro software for each date. Having 3D point clouds of the study area enabled a series of measurements that were impossible to obtain in 2D images. In 3D space, the sizes (height, diameter, cup-perimeter, and cup-surface area) of several A. setubalense specimens were measured each year. The annual growth rates recorded ranged from zero (“no growth”) for a large size specimen, to an average of 2.2 cm year–1 in cup-diameter, and 2.5 cm year–1 in height for developing specimens. Von Bertalanffy growth parameters were estimated. Taking into account the size indicators used in this study and based on the von Bertalanffy growth model, this sponge reaches 95% maximum size at 98 years of age. During the MPA monitoring program, a high number of specimens disappeared. This raised suspicions of a phenomenon affecting the survival of this species in the area. This type of image-based methodology does not cause damage or alterations to benthic communities and should be employed in vulnerable ecosystem studies and MPA monitoring.

Environmental Change at Deep-Sea Sponge Habitats Over the Last Half Century: A Model Hindcast Study for the Age of Anthropogenic Climate Change

“…Whilst the magnitude of the fluctuations is relatively small, these results can help inform future studies of sponge sensitivity to environmental change. We assume that because any large/mature specimens collected in these areas in recent times (Roberts et al, 2021b) likely represent individuals of many decades to centuries in age (Prado et al, 2021), they appear to have persisted despite this level of variability.…”

Deep-sea sponges in an Anthropocene ocean

“…This assumption is corroborated by recent hindcast models that describe abrupt permanent shifts in water masses and related changes of seawater temperature, salinity, oxygen-and particulate organic matter-concentration across areas with high abundances of G. barretti (Samuelsen et al, 2022). The described presence of large individuals of slow-growing deep-sea sponges that are at least a few decades old (Prado et al, 2021) within these habitats suggest that G. barretti can withstand fluctuations of environmental parameters and persists to form dense aggregations that resemble hotspots of biodiversity, provide stepping stones of connectivity, and greatly determine nutrient cycling (Hogg et al, 2010). The observed tolerance of G. barretti towards decreased seawater pH might be related to its vast numbers of associated microbes (3.1 × 10 11 microbial symbionts per ml sponge tissue (Leys et al, 2018).…”

Evaluating the 3D Integrity of Underwater Structure from Motion Workflows

“…The line segment measurement approach that was applied to evaluate the 3D accuracy of this studies SfM modelling workflow was designed to align with the linear glass sponge growth estimates documented in the research literature, but also to highlight the limitations associated with linear measurements of a 3D organism such as a glass sponge and the significant opportunity presented by SfM modelling. This choice, however, makes it difficult to draw direct comparisons between the outcomes from this study and those quantifying other morphological characteristics (e.g., surface area and volume) (Olinger et al., 2019; Prado et al., 2021). Furthermore, the 3D accuracy of SfM modelling is influenced by variables such as the chosen equipment, data‐capture strategy, data format, and the variable physical properties of the subject (e.g., sponge) and the context (e.g., location, water properties, and light) in which it is surveyed (Kersten and Lindstaedt, 2012; Thoeni et al., 2014; Capra et al., 2015; Lavy et al., 2015; Lochhead and Hedley 2021), making the performance or suitability of a technique (i.e., SfM) hard to overtly express without careful consideration of context.…”