A light in the dark: using cabled seafloor observatories to study abundance and behaviour of seafloor megafauna in response to environmental change
Bibliographic record
Abstract
Surface primary productivity forms the base of most marine food webs and contributes significantly to global carbon cycling, providing a key link from surface to seafloor. High seasonal primary productivity along temperate latitude coastlines provides crucial nutrients for seafloor communities, driving spatiotemporal patterns in abundance, behaviour, biodiversity, and distribution of benthic megafauna. Many factors, including ocean warming, deoxygenation, and increasing frequency and duration of marine heatwaves (MHW), may alter the dynamics governing primary production, threatening benthic organisms that depend on the seasonal input of phytodetritus for food. The focus of this thesis is to make use of two seafloor observatories, NEPTUNE near Vancouver Island, British Columbia, and a new platform in Conception Bay, NL, to examine variability in abundance, behaviour, and composition of seafloor communities in response to environmental change across temporal scales. First, the response of the deep-sea pink urchin Strongylocentrotus fragilis to a recent MHW on the continental margin off the coast of Vancouver Island was investigated using a combination of benthic trawls (2004-2018) and seafloor observatory data (2013-2020). Sea urchin density declined during the MHW, likely in response to reduced kelp subsidies from coastal waters. Next, the new Holyrood Underwater Observatory in Conception Bay was used to study benthic community response to the spring phytoplankton bloom. High-frequency variability in seafloor environmental dynamics was documented during the winter-spring transition, and the unexpected emergence of >200 sea cucumbers (Psolus sp.) coinciding with the arrival of phytodetritus at the seafloor was observed. These data will provide a baseline against which to monitor changes in phenology as climate change progresses. This work comes at a critical point in ocean observing as we approach global climate tipping points. Now more than ever, it is essential to document the current state of marine communities to understand and predict community responses to changing ocean conditions, and to sustainably manage ocean resources.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".