Severe coral loss shifts energetic dynamics on a coral reef
Bibliographic record
Abstract
Abstract Declining coral cover and loss of structural complexity are widely reported on today's coral reefs. While coral loss frequently triggers changes in coral reef fish assemblage structure, the ecosystem‐scale consequences of these changes are poorly known. Here we evaluate how four metrics of energy flow and storage that underscore a critical coral reef function, consumer biomass production, respond to severe coral loss on a coral reef in the northern Great Barrier Reef, Australia. We compared fish and benthic surveys at Lizard Island from 2003 to 2004 with surveys in 2018 using an individual‐level modelling approach that integrates growth and mortality coefficients to estimate community‐level standing biomass, productivity, consumed biomass and turnover. In the study period, coral cover declined by 72%–83% in forereef zones while turf cover increased by 18%–100% across all zones. Reef fish assemblages, in turn, responded with a 71% increase in standing biomass, 41% in productivity and 37% in consumed biomass, mainly driven by nominally‐herbivorous fishes (Labridae—Scarini, Acanthuridae and Siganidae). By contrast, biomass turnover rates declined by 19%. Our findings suggest that coral loss can drive energetic shifts on coral reefs, leading to more productive, but slower paced reef fish assemblages. Although the observed build‐up of biomass may appear positive, the decreased turnover rates indicate that the system is unable to maintain biomass replacement levels. This suggests that the enhanced productivity that accompanied coral loss may be driven by storage effects from the somatic growth of individuals already present, questioning the temporal stability of these changes to coral reef ecosystem functioning. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.001 | 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".