Temperature and food chain length, but not latitude, explain region-specific kelp forest responses to an unprecedented heatwave
Bibliographic record
Abstract
Abstract Kelp forests are among the most abundant coastal marine habitats but are vulnerable to the impacts of climate change. Between 2014 and 2016, an unprecedented heatwave and associated changes in trophic dynamics threatened kelp forests across the Northeast Pacific, with impacts documented from Mexico to Alaska. However, responses have varied substantially and remain poorly characterized across large stretches of coast, especially British Columbia (B.C.), which represents a significant percentage of the range of floating kelp species in the Northeast Pacific. Here, we explore variation in floating kelp ( Macrocystis, Nereocystis ) persistence pre- and post-heatwave across a >675 km latitudinal gradient, asking whether B.C. kelp forests are of conservation concern. We assembled and analyzed available quantitative kelp data, comparing snapshots of kelp extent before (1994 – 2007) and after (2018 – 2021) the heatwave in 11 regions spanning a range of temperature and sea otter-occupancy statuses, and contextualizing these with time series analyses, where available (n = 7 regions). We provide strong evidence that kelp forests have declined in many regions but with evidence of refugia at both local and regional scales. Kelp forest persistence was negatively correlated with summer sea temperatures in southern B.C., where temperatures varied by ~6°C across sites, at times exceeding species’ thermal tolerances. Kelp dynamics in northern regions appeared instead to be modulated by top-down control by urchins and sea otters. Our results demonstrate that B.C.’s kelp forest have been substantially reduced in recent years but that regional and local-scale factors influence the resilience of forests to large-scale perturbations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".