Microbial bioenergetics of coral-algal interactions
Bibliographic record
Abstract
Human impacts are causing ecosystem phase shifts from coral- to algal-dominated reef systems on a global scale. As these ecosystems undergo transition, there is an increased incidence of coral-macroalgal interactions. Mounting evidence indicates that the outcome of these interaction events is, in part, governed by microbially mediated dynamics. The allocation of available energy through different trophic levels, including the microbial food web, determines the outcome of these interactions and ultimately shapes the benthic community structure. However, little is known about the underlying thermodynamic mechanisms involved in these trophic energy transfers. This study utilizes a novel combination of methods including calorimetry, flow cytometry, and optical oxygen measurements, to provide a bioenergetic analysis of coral-macroalgal interactions in a controlled aquarium setting. We demonstrate that the energetic demands of microbial communities at the coral-algal interaction interface are higher than in the communities associated with either of the macroorganisms alone. This was evident through higher microbial power output (energy use per unit time) and lower oxygen concentrations at interaction zones compared to areas distal from the interface. Increases in microbial power output and lower oxygen concentrations were significantly correlated with the ratio of heterotrophic to autotrophic microbes but not the total microbial abundance. These results suggest that coral-algal interfaces harbor higher proportions of heterotrophic microbes that are optimizing maximal power output, as opposed to yield. This yield to power shift offers a possible thermodynamic mechanism underlying the transition from coral- to algal-dominated reef ecosystems currently being observed worldwide. As changes in the power output of an ecosystem are a significant indicator of the current state of the system, this analysis provides a novel and insightful means to quantify microbial impacts on reef health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".