Environmental control of community organisation on ocean-exposed sandy beaches
Bibliographic record
Abstract
Models of faunal communities on open-coast beaches emphasise the primacy of environmental conditions in determining species richness and abundance. What remains unresolved under this ‘physical-control paradigm’ includes the following two aspects: (1) how habitat properties relate to structural traits of communities; and (2) how environmental conditions shape communities when habitat properties change over time. Here, we test these by modelling the relationship between a broad range of environmental drivers and assemblage structure. Our models draw on a sizeable dataset (15 600 cores collected over 4 years) of benthic invertebrates from beaches in eastern Australia; we also include a test of whether human disturbance (vehicles) alters the relationships between environmental predictors and faunal communities. A suite of physical factors, comprising habitat features (i.e. moisture level, grain size, beach slope) and wave parameters, explained variation in community structure. Novel aspects are the role of sea-surface temperature (SST) as a driver of biological structure on beaches, and that human impacts can override the sediment–animal relationships that are normally important. More generally, theoretical and empirical models of beach-community organisation should incorporate multiple environmental drivers, include broader structural aspect of assemblages, and recognise the role of human habitat alterations in shaping these fauna–environment links.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".