Life‐history diversity and its importance to population stability and persistence of a migratory fish: steelhead in two large <scp>N</scp>orth <scp>A</scp>merican watersheds
Bibliographic record
Abstract
Life-history strategies can buffer individuals and populations from environmental variability. For instance, it is possible that asynchronous dynamics among different life histories can stabilize populations through portfolio effects. Here, we examine life-history diversity and its importance to stability for an iconic migratory fish species. In particular, we examined steelhead (Oncorhynchus mykiss), an anadromous and iteroparous salmonid, in two large, relatively pristine, watersheds, the Skeena and Nass, in north-western British Columbia, Canada. We synthesized life-history information derived from scales collected from adult steelhead (N = 7227) in these watersheds across a decade. These migratory fishes expressed 36 different manifestations of the anadromous life-history strategy, with 16 different combinations of freshwater and marine ages, 7·6% of fish performing multiple spawning migrations, and up to a maximum of four spawning migrations per lifetime. Furthermore, in the Nass watershed, various life histories were differently prevalent through time - three different life histories were the most prevalent in a given year, and no life history ever represented more than 45% of the population. These asynchronous dynamics among life histories decreased the variability of numerical abundance and biomass of the aggregated population so that it was > 20% more stable than the stability of the weighted average of specific life histories: evidence of a substantial portfolio effect. Year of ocean entry was a key driver of dynamics; the median correlation coefficient of abundance of life histories that entered the ocean the same year was 2·5 times higher than the median pairwise coefficient of life histories that entered the ocean at different times. Simulations illustrated how different elements of life-history diversity contribute to stability and persistence of populations. This study provides evidence that life-history diversity can dampen fluctuations in population abundances and biomass via portfolio effects. Conserving genetic integrity and habitat diversity in these and other large watersheds can enable a diversity of life histories that increases population and biomass stability in the face of environmental variability.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".