bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.06.22.660910

Ecology of Echinodermata in the Clarion-Clipperton-Fracture Zone (Central Pacific)

Stratmann, T.·Simon-Lledo, E.·van der Meer, M. T. J.·Christodoulou, M.·Rossel, S.·Colaco, A.
View original source ↗Export metadata ↓

Abstract

Abyssal seascapes between 3,000 and 6000 m water depth represent over 50% of the Planets surface, but the species, functions, and particularly the life history traits that these ecosystems harbour remain poorly understood. Brittle stars (Ophiuroidea) contribute about one third to the invertebrate megabenthos assemblage between 3,800 m and 4,800 m water depth in the Clarion-Clipperton Fracture Zone (CCZ, Northeast Pacific). Starfishes (Asteroidea) are present in lower densities. In the CCZ, Ophiuroidea are often seen near Xenophyophoroidea and attached to glass sponge (Hexactinellida) stalks. We hypothesize that (1) the observed relationship between Ophiuroidea, Xenophyophoroidea, and Hexactinellida is a predator-prey relationship, where Ophiuroidea feed on foraminifera- and sponge-derived organic matter. (2) Ophiuroidea have a reduced dependency on fresh phytodetritus. (3) Brisingida (order of Asteroidea), often clings to stalks to have easier access to particulate organic matter sinking to the seafloor. To test these three hypotheses, we combined bulk and compound-specific stable isotope analyses of fauna (Ophiuroidea, Asteroidea) and sediments with the analyses of seafloor images from the eastern CCZ. Faunal specimens and sediments were collected during three research expeditions between 2019 and 2022, and previously collected seabed images were re-analysed to quantify the major behaviours in which Ophiuroidea and Asteroidea engage. All investigated Echinodermata species had a high trophic level. Phospholipid-derived fatty acids (PLFAs) used as biomarkers suggest that Silax daleus consumes sedimentary detritus that is processed by its gut microbiome. Ophiacantha cosmica is likely a top consumer or scavenger, Ophiosphalma glabrum is an opportunistic omnivore ingesting phytodetritus, bacteria, Crustacea, and Foraminifera, while Ophiuroglypha cf. polyacantha is a more selective omnivore. Freyella benthophila sits mostly on stalks of Hexactinellida and uses this elevated position to catch phytodetritus and zooplankton. Freyastera cf. tuberculata, in comparison, sits mostly on polymetallic nodules from where it preys upon Crustacea moving on the sediment surface. This study confirmed the hypothesis that Ophiuroidea in the CCZ are less dependent on phytodetritus than Holothuroidea in the Peru Basin. It was confirmed that Ophiuroidea consume foraminifera- and sponge-derived organic matter, but Brisingida cling to stalks of Hexactinellida to prey upon Crustacea living in the benthic boundary layer.

Source connections

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Stratmann, T., Simon-Lledo, E., van der Meer, M. T. J., Christodoulou, M., Rossel, S., Colaco, A.. 2025-06-27. Ecology of Echinodermata in the Clarion-Clipperton-Fracture Zone (Central Pacific). https://doi.org/10.1101/2025.06.22.660910

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗
bioRxiv2026

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗
bioRxiv2026

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗