How much sampling does it take to detect trends in coral‐reef habitat using photoquadrat surveys?
Bibliographic record
Abstract
ABSTRACT Coral‐reef managers must detect and reverse collapses in habitat and evaluate the success of such interventions. Since these responsibilities must be met with limited time and resources, methods used should balance statistical power with practical and logistical constraints. Photoquadrat analysis is a commonly used method to survey coral habitats. This method, which involves photographing substratum along transects and digitally analysing habitat at points on the ‘photoquadrats’, affords efficiency in the field but is costly and requires extensive desk‐based analysis. It remains unclear what is the optimal combination of sampling units (points, photoquadrats and transects) needed to detect important trends in coral habitat. Here, a dataset on Philippine coral‐reef habitats, collected using intensive photoquadrat surveys, was used to explore the reliability of using different numbers of points per photoquadrat, photoquadrats per transect and transects per site to detect spatial differences in habitat. Results of leave‐some‐out analyses were compared with analysis of the complete dataset. Using fewer points per photoquadrat and fewer photoquadrats per transect caused little decline in ability to detect key trends, and lessened desk‐based time; reducing the number of photoquadrats also lessened field time. Using fewer transects reduced time requirements but at the expense of statistical reliability. Prospective power analyses revealed that common rates of coral recovery could not be detected using even the most intensive photoquadrat protocols. This result implies that coral recoveries within protected areas might go undetected using standard surveying techniques. Using fixed rather than randomly placed photoquadrats, or more sensitive indicators of habitat recovery than coral cover (e.g. coral surface area) may improve power to detect coral recoveries. Finally, protocols that minimize desk time rarely also minimize field time and vice versa, which highlights the need to prioritize different logistical constraints when designing methods. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.040 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".