Linking microphytobenthos distribution and mudflat geomorphology under varying sedimentary regimes using unoccupied aerial vehicle (UAV)-acquired multispectral reflectance and photogrammetry
Bibliographic record
Abstract
Microphytobenthic (MPB) biofilms play significant roles in the ecology of coastal mudflats, including provision of essential food resources to shorebird species. In these ecosystems, water-divergence structures like jetties and causeways can drastically alter sedimentation patterns and mudflat topography, yet their effects on MPB biofilm biomass and distribution are poorly understood. Here, we used a combination of unoccupied aerial vehicle (UAV) technologies, photogrammetric processing, and sediment field samples to compare biofilm and mudflat characteristics between areas of the Fraser River Estuary with varying sedimentary regimes and shorebird use. Our aims were to: (1) demonstrate the use of fine spatial resolution UAV-acquired multispectral imagery (cm2) with extensive spatial coverage (>km2) and a co-alignment photogrammetric processing techniques to survey MPB biofilm and mudflat topography at spatial scales and detail relevant to foraging shorebirds; and, (2) investigate the effects of water-divergence structures on mudflat elevation and microtopography, as well as MPB biofilm biomass, distribution, and spatial patterning. From a technical perspective, co-alignment allowed us to analyze aligned and continuous fine-resolution elevation models and orthomosaics for large areas of the estuary, while the normalized difference vegetation index was a good predictor of sediment chlorophyll-a (R2 = 0.9). Using these data products, we found that mudflats in close proximity to water-divergence structures have cross-shore profiles characteristic of low sediment supply as well as decreased microtopographic variability. At disturbed sites, elevation and microtopography had a weaker influence on biofilm biomass compared to intact estuarine ecosystem sites. Analysis of biofilm patch that sites either had a smaller number of large, contiguous patches, or a large number of smaller, isolated patches; however, less disturbed sites did not necessarily have larger biofilm patches than more disturbed sites. We conclude that UAV-acquired multispectral imagery and co-alignment workflow offer promising new tools for ecologists to map, monitor, and understand MPB biofilm dynamics in ecologically sensitive estuaries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".