Implications of spatial contraction for density dependence and conservation in a depressed population of anadromous fish
Bibliographic record
Abstract
Changes in density-independent mortality can alter the spatial extent of populations through patch extinction and colonization, and spatial contraction may alter population productivity and compensatory capacity. Here, we analyze a time series of steelhead (Oncorhynchus mykiss) abundance and examine the hypothesis that spatial contraction can decrease compensatory capacity. Over the last 20 years, steelhead in the Keogh River have declined by an order of magnitude because of a period of poor smolt-to-adult survival. Low abundance has been associated with more depressed production of out-migrating smolts than would be expected based on traditional models of compensatory dynamics. Patterns of juvenile density over time show changes in the spatial distribution of the population. We developed a spatially explicit population model to explore spatial structure and juvenile recruitment under varying marine survival. Results suggest that spatial contraction during a period of poor marine survival can strengthen density-dependent population regulation, reducing smolt production at the watershed scale. Our results highlight that spatial contraction can alter the fundamental density-dependent relationships that define population dynamics, recovery trajectories, and sustainable harvest levels of spatially structured populations.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".