Monthly macroalgal surveys reveal a diverse and dynamic community in an urban intertidal zone
Bibliographic record
Abstract
Abstract Baseline data are critical to measuring how communities shift in response to climate change and anthropogenic activity. Macroalgae are marine foundation species that are vulnerable to climate change, but baseline macroalgal biodiversity data are lacking for many areas of British Columbia, particularly at a high temporal resolution over years. This presents an obstacle for measuring how communities change in response to shifting average conditions as well as after extreme events, such as the 2021 heat dome. To increase baseline macroalgae biodiversity data in British Columbia, we established a monthly transect-based macroalgal survey in 2021 at an urban intertidal site with a low-cost and easily replicable survey protocol, in addition to publishing a previously unpublished 1983-1984 historical dataset of the same area. Our datasets and our survey protocol are freely available. Over 35 months we have recorded 61 taxa of macroalgae, including the canopy forming kelp Nereocystis luetkeana and the introduced fucoid Sargassum muticum . Surveying throughout the year at regular intervals has revealed large-scale seasonal shifts in macroalgal community composition, the timing of kelp recruitment, and a decrease in abundance of Fucus distichus in the upper intertidal zone over multiple years. Our publicly accessible data constitute the most comprehensive survey of intertidal macroalgal diversity in Burrard Inlet, illustrating how simple surveying methods can provide high resolution records of macroalgae biodiversity, particularly in easily accessible urban 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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".