Multi-scale variation in salinity: a driver of population size and structure in the muricid gastropod Nucella lamellosa
Bibliographic record
Abstract
The abiotic environment varies continuously at a variety of temporal scales. While this variation is known to be ecologically important, multiple scales of variability are rarely explicitly considered in ecological studies. Here, we combine field observations and laboratory experiments to determine the individual and population level effects of short-term (tidal) and longer-term (seasonal and interannual) salinity variation on the dogwhelkNucella lamellosain the Strait of Georgia, British Columbia, Canada. The Fraser River heavily influences surface salinity in the Strait of Georgia, which varies with season, depth, and distance to the river mouth. At low salinity sites,N. lamellosapopulation size decreased following high outflow years, with fewer juveniles present, as opposed to high salinity sites, which had higher population densities in all years. Sustained salinity exposure in the laboratory caused developmental delay of encapsulated embryos and complete mortality at 9 and 12 psu. Juvenile dogwhelks (<30 mm shell length) and those from a high salinity site experienced higher mortality in low salinity conditions than larger individuals and those from a low salinity site. The inclusion of a 3 h daily exposure to 20 psu, simulating high tides in a stratified water column, enabledN. lamellosato survive otherwise low salinity conditions for considerably longer. Overall, our results suggest that seasonal and interannual variation in salinity have a profound influence onN. lamellosapopulations and that shorter-scale fluctuations can moderate these seasonal and interannual effects. It is likely that similar multi-scale environmental effects will determine survival and population dynamics in many species.
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) | 0.000 | 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".