Co‐creating solutions to the hidden impacts of climate change on Canada's Pacific kelp forests
Bibliographic record
Abstract
Abstract Co‐designed research driven by the information needs of coastal communities can enhance social‐ecological resilience to climate change. On the Central Coast of British Columbia, Canada, the 2014–2015 marine heatwave triggered an outbreak of an encrusting bryozoan ( Membranipora spp.) on giant kelp ( Macrocystis tenuifolia ), an ecologically, culturally and economically important seaweed to coastal Indigenous Peoples that is also under direct threat from ocean warming and extreme heat events. In response, Indigenous stewardship departments and academic researchers co‐designed field surveys examining potential impacts of bryozoans on kelp at 10 sites over 2 years, encompassing spatial and interannual variation in ocean temperatures. The susceptibility of kelp beds to bryozoan outbreaks increased with higher kelp canopy cover and warmer sea surface temperatures yet decreased with greater wave exposure. During 2021, maximum bryozoan cover on kelp occurred ≈2.5 months earlier than in 2020, which correlated with the timing of anomalously early warming of seawater. This result illustrates how ocean warming can indirectly impact kelp. Adaptive management strategies include selection of cooler and more wave‐exposed sites for kelp harvests, reduced harvests in warmer years and seasonally earlier harvests during years with anomalously early warming. Our study demonstrates how engagement with local knowledge holders and collaborative monitoring can inform adaptive management strategies aimed at increasing social‐ecological resilience at local scales relevant to Indigenous governance and coastal economies. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".