Trophic control of cryptic coralline algal diversity
Bibliographic record
Abstract
Understanding how trophic dynamics drive variation in biodiversity is essential for predicting the outcomes of trophic downgrading across the world's ecosystems. However, assessing the biodiversity of morphologically cryptic lineages can be problematic, yet may be crucial to understanding ecological patterns. Shifts in keystone predation that favor increases in herbivore abundance tend to have negative consequences for the biodiversity of primary producers. However, in nearshore ecosystems, coralline algal cover increases when herbivory is intense, suggesting that corallines may uniquely benefit from trophic downgrading. Because many coralline algal species are morphologically cryptic and their diversity has been globally underestimated, increasing the resolution at which we distinguish species could dramatically alter our conclusions about the consequences of trophic dynamics for this group. In this study, we used DNA barcoding to compare the diversity and composition of cryptic coralline algal assemblages at sites that differ in urchin biomass and keystone predation by sea otters. We show that while coralline cover is greater in urchin-dominated sites (or "barrens"), which are subject to intense grazing, coralline assemblages in these urchin barrens are significantly less diverse than in kelp forests and are dominated by only 1 or 2 species. These findings clarify how food web structure relates to coralline community composition and reconcile patterns of total coralline cover with the widely documented pattern that keystone predation promotes biodiversity. Shifts in coralline diversity and distribution associated with transitions from kelp forests to urchin barrens could have ecosystem-level effects that would be missed by ignoring cryptic species' identities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".