Impact of Multiple Climate Stressors on Early Life Stages of North Pacific Kelp Species
Bibliographic record
Abstract
ABSTRACT This study examines the effects on bull kelp ( Nereocystis luetkeana ) and ribbon kelp ( Alaria marginata ) of combinations of three climate‐related stressors relevant to high‐latitude kelp forests: temperature, salinity, and sediment load. Fertile specimens of both species were collected from Juneau, Alaska. Spores produced were cultivated over 40 days in four ecologically relevant stressor treatments: control (all stressor levels normal; CTRL), increased glacial melt (normal temperature, low salinity, high sediment load; GLAC), increased runoff (high temperature, low salinity, normal sediment load; MELT) and climate change (high temperature, low salinity, high sediment load; CLIM). Gametophyte density in both species was reduced in treatments involving high sediment load. Gametophyte density in bull kelp was also reduced in the increased runoff treatment, while ribbon kelp appeared resilient. Gametophytes of A. marginata grew equally in the increased glacial melt treatment as in the control and exhibited some growth in the increased runoff treatment. Conversely, gametophytes of N. luetkeana exhibited low growth in all treatments other than the control. A large number of gametophytes of both species were unidentifiable as either male or female in high‐temperature treatments. This likely had impacts on reproduction, as neither species was able to produce eggs or sporophytes in these treatments. The results presented here show that both N. luetkeana (a subtidal canopy‐former) and A. marginata (an intertidal subcanopy species) are sensitive to combinations of thermal, hyposaline, and sediment stress. This may have an impact on the development of gametophytes and successful reproduction in these species and may therefore have implications for the ongoing persistence of wild kelp populations in future ocean conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".