Mapping for coral reef conservation: comparing the value of participatory and remote sensing approaches
Bibliographic record
Abstract
Abstract Detailed habitat maps are critical for conservation planning, yet for many coastal habitats only coarse‐resolution maps are available. As the logistic and technological constraints of habitat mapping become increasingly tractable, habitat map comparisons are warranted. Here we compare two mapping approaches: local environmental knowledge ( LEK ) obtained from interviews; and remote sensing analysis ( RS ) of high spatial resolution satellite imagery (2.0 m pixel) using object‐based image analysis. For a coral reef ecosystem, we compare the accuracy of these two approaches for mapping shallow seafloor habitats and contrast their characterization of habitat area and seascape connectivity. We also explore several implications for conservation planning. When evaluated using independent ground verification data, LEK ‐derived maps achieved a lower overall accuracy than RS ‐derived maps ( LEK : 66%; RS : 76%). A comparison of mapped habitats found low overall agreement between LEK and RS maps. The RS map identified 5.4 times more habitat edges (the border between adjacent habitat classes) and 3.7–6.4 times greater seascape connectivity. Since the spatial arrangement of habitats affects many species (e.g., movement, predation risk), such discrepancies in landscape metrics are important to consider in conservation planning. Our results help identify strengths and weakness of both mapping approaches for conservation planning. Because RS provided a more accurate estimate of habitat distributions, it would be better for conservation planning for species sensitive to fine‐spatial scale seascape patterns (e.g., habitat edges), whereas LEK is more cost effective and appropriate for mapping coarse habitat patterns. Goals for maps used in conservation should be identified early in their development.
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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".