Uncovering intra-daily intertidal biofilm dynamics in a shorebird foraging hotspot with hourly UAV multispectral imagery acquisitions
Bibliographic record
Abstract
Estuarine mudflats are colonized by biofilm-forming microphytobenthos (MPB), which support primary production, stabilize sediment, and provide critical food for benthic invertebrates and shorebirds. MPB biomass fluctuates intra-daily, peaking post-emersion and declining before tidal immersion. Understanding these dynamics requires high-resolution monitoring, yet traditional methods, including sediment sampling and satellite imagery, lack the necessary spatial and temporal precision. We used unoccupied aerial vehicles (UAVs) to map MPB distribution, biomass, and mudflat morphology at shorebird-relevant scales. Hourly multispectral surveys over two 12-hour tidal emersion cycles at the Fraser River Estuary, British Columbia—an internationally significant shorebird stopover—captured diel MPB dynamics. Optical imagery was processed using a photogrammetric co-alignment approach to generate continuous chl- a maps (via the normalized vegetation index), digital surface models, and topographic position index layers. UAV-derived data were integrated with climate variables to model MPB variability and quantify diel biofilm patch dynamics. A modified Z -score normalization of pseudo-invariant features stabilized reflectance data, allowing fine-scale analysis of MPB distribution. Our diel model explained 31.6 % of MPB variation, with biomass peaking seven hours post-emersion, consistent with vertical migration of microalgae. Mudflat morphology significantly influenced MPB biomass, and spatial metrics revealed interactions between MPB dynamics, microtopography and shorebird foraging ecology. The study demonstrates the efficacy of high-temporal-resolution UAV imagery for monitoring MPB and mudflat morphology, enabling detailed examination of MPB diel vertical migration in response to emersion timing and light availability. Such new insights into estuarine ecology provide a framework for advancing conservation strategies and habitat management in intertidal environments.
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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 | 0.001 |
| 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".