What is hidden in the distribution of sea cucumber faecal casts? Spatial point pattern analysis reveals tracemaker community competition in the Bering Sea abyssal plain
Bibliographic record
Abstract
Sea cucumbers are one of the most abundant deep-sea benthic megafauna, both in terms of abundance and biomass. As efficient bioturbators, they assimilate nutrients from ingested material while excreting sediments (i.e., faecal cast lebensspuren), playing an important role as ecosystem engineers of the deep seafloor. Thus, distribution of the faecal casts produced by sea cucumbers, one of the most common morphotypes of deep-sea lebensspuren assemblages, may reflect the nutrient composition of the seafloor. However, the implications of these lebensspuren for understanding competition among tracemaker communities (i.e., benthic fauna responsible of lebensspuren formation) in the deep-sea have rarely been explored. Here, we apply spatial point pattern analysis (SPPA) based on still images of rounded faecal casts and their producer ( Elpidia minutissima ) in an abyssal transect of the Bering Sea that was studied during the RV Sonne expedition AleutBio (Aleutian Trench Biodiversity Studies) (SO293). Elpidia minutissima is a well-known tracemaker that performs non-random foraging movements, and is able to detect and feed on nutrient-rich patches. We found that 24 rounded faecal cast populations best-fit a Complete Spatial Randomness (CSR) model, and 16 best-fit a Heterogeneous Poisson (HP) model (i.e., aggregated distribution). CSR populations were negatively correlated with tracemaker density and digesting lebensspuren assemblage, suggesting a low nutrient seafloor. HP populations were positively correlated with locomotion lebensspuren assemblage, suggesting a more favorable seafloor. We highlight the utility of SPPA on faecal casts, one of the most common lebensspuren on deep-sea still images, as a proxy for seafloor nutrient conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".