Beta Diversity of Antarctic and Sub-Antarctic Benthic Communities Reveals a Major Role of Stochastic Assembly Processes
Bibliographic record
Abstract
Community assembly is the result of both, deterministic and stochastic processes. The former encompasses niche-based local-scale mechanisms such as environmental filtering and biotic interactions; the latter includes ecological drift, probabilistic colonisation, and random extinctions. Using standardised sampling protocols, we show that the spatial variation in species composition (beta diversity) of shallow subtidal macrobenthic communities of sub-Antarctic (Strait of Magellan and Yendegaia Fjord [Beagle Channel]) and Antarctic (Fildes Bay [King George Island, West Antarctic Peninsula]) localities reflects a high contribution of stochastic processes to community assembly. Null model analyses indicated that random sampling from species pools of different sizes drove the observed among-locality differences in incidence- and abundance-based beta diversity. We analysed a normalised stochasticity ratio (NST), which delimits between more deterministic (<50%) and more stochastic (>50%) assembly. NST was notably larger than 50%, with mean values of 69.5% (95% CI = 69.2–69.8%), 62.5% (62.1–62.9%), and 72.8% (72.5–73.2%) in Strait of Magellan, Yendegaia Fjord, and Fildes Bay, respectively. Accordingly, environmental factors, such as depth, seawater temperature, salinity, and underwater light penetration, accounted for a small fraction of the spatial variation in community composition across the three localities. In this region, therefore, stochastic processes could have stronger effects on community assembly than deterministic niche-based factors. As anthropogenic biotic homogenisation continues apace, our study can give useful insights into the major ecological processes in Southern Ocean’ coastal marine communities.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".