Drivers of Coral and Sponge Community Composition and Size Structure Revealed With Cumulative Abundance Profiles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Deep‐sea corals and sponges form ecologically significant habitats that support biodiversity hotspots and contribute to important ecosystem functions such as carbon and nutrient cycling as well as benthic‐pelagic coupling. However, quantifying their contributions to ecosystem functioning requires examination not only of the fine spatial distribution of community composition but also community size structure, because larger individuals are expected to contribute more to ecosystem functions than smaller ones. Here we create novel cumulative abundance profiles (CAPs) by combining body size structure with species abundance data to identify ecological drivers of sponge and coral community composition and size structure. Data were collected from 226 drop camera images captured near Saglek Bank, on the northern Labrador shelf and upper slope in the northwest Atlantic. The density of four coral and 17 sponge morphospecies were recorded from each image. The surface area covered by coral and sponge specimens was measured (1458 measurements in total) and converted to size estimates using data from live specimens collected with a rock dredge. Cumulative abundance profiles were then constructed and combined with cluster analysis to identify distinct community assemblages. In addition, distance‐based redundancy analysis was used to identify environmental drivers influencing cluster community composition and/or size structure. Finally, organic carbon turnover was calculated for each cluster using published respiration data. Three assemblages were identified with differing composition and size structures. One of these was characterized by large coral and sponge morphospecies and individuals. The spatial distribution of this cluster was controlled by interactions between substrate type, terrain position index (TPI) and orientation of the slope (eastness). When analysing composition or size structure separately, dissolved oxygen and current speed respectively were also identified as key parameters. This finding indicates that substrate type and TPI influence the presence of coral and sponges in the study area, while dissolved oxygen may constrain which morphospecies are present and bottom currents restrict the size of individuals. As predicted, high levels of carbon turnover were driven by large sponge and coral individuals, likely maintained in part by the sponge loop in which sponges recycle dissolved organic matter into particulate organic matter. This study gives the first demonstration of how CAPs can be used to analyse spatial variation in deep‐sea benthic community composition and size structure and appropriately quantify contribution to ecosystem functions such as carbon turnover.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it