A modified trait‐based framework for assessing the resilience of ecosystem services provided by coral reef communities
Bibliographic record
Abstract
Abstract Climate change and other anthropogenic disturbances are having dramatic impacts on tropical coral reefs and the ecosystem services they provide. Anticipating change in ecosystem services is a pressing challenge that requires understanding how species respond to an environmental change, and how they contribute to ecosystem services. Building on analogous efforts by plant ecologists, we propose a trait‐based framework that aims to predict the resistance and recovery of coral ecosystem services against both pulse and press disturbances. The framework involves identifying effect, resistance, and recovery traits across coral species and using these to ascertain how species might (1) contribute to a service, (2) resist disturbance, and (3) recover after the disturbance. By identifying trait functional overlaps and quantifying correlations among the trait types, the framework can yield a better understanding of the potential mechanistic links between community assembly, ecosystem dynamics, and ecosystem functioning. We demonstrate the utility of the framework by applying it to a virtual scenario, implemented via simulation, in which habitat provisioning is the focal ecosystem service, and cyclones and bleaching events are the disturbances of interest. This demonstration required us to assemble an extensive dataset of relevant traits—a process that revealed key gaps in the coral trait database and in our understanding of coral functional ecology. The framework can provide predictive power for the resilience of ecosystem services and can also help identify crucial knowledge gaps, research objectives, and strategies for applying a trait‐based approach to advancing coral reef science and conservation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".