Density of intertidal barnacles along their full elevational range of distribution conforms to the abundant-centre hypothesis
Bibliographic record
Abstract
The abundant-centre hypothesis (ACH) predicts that the density of a species should peak at its distribution centre and decrease similarly towards distribution margins. The ACH has been deduced from a theory that postulates that environmental conditions should be most favourable for a species at the centre of its distribution. This idealised density pattern, however, has been supported by limited field studies, as natural patterns are often more complex. It is thus of interest to examine under what conditions compliance with the ACH could be favoured. Such conditions could be smooth environmental gradients with limited habitat patchiness throughout the distribution range of a species. Thus, we tested the ACH by measuring the density of an intertidal barnacle ( Semibalanus balanoides ) across its full vertical distribution range (from low to high elevations) on a rocky shore with similar substrate properties across elevations. To do a reliable test, we surveyed eight elevation zones applying an equal sampling effort in each zone. Average barnacle density conformed to the ACH, as it peaked at the middle of the vertical distribution range of this species. The same underlying theory predicts a similar unimodal pattern for maximum body size, but this trait was decoupled from density, as maximum barnacle size increased from low to high elevations. Overall, although the ACH is not a universal predictive tool as once envisioned, it may predict some cases well, as shown by this study. Therefore, the ACH should not be discarded completely, and its domain of application should be further evaluated.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".