Weakened portfolio effects constrain management effectiveness for population aggregates
Bibliographic record
Abstract
Population diversity can reduce temporal variability in aggregate population abundances in a process known as the portfolio effect. Portfolio effects may weaken, however, due to greater synchrony among component populations. While weakened portfolio effects have been previously documented, the consequences of reduced stability on meeting conservation goals for population aggregates that are harvested (e.g., stock aggregates in fisheries) are rarely quantified. Here, we demonstrate how changes in variability within components, synchrony among components, and population productivity interact to influence the probability of achieving an array of management objectives for Fraser River sockeye salmon: a stock aggregate of high economic, ecological, and cultural value. We first present evidence that component variability and synchrony have increased over the last two decades, consistent with a weakening portfolio effect. We then parameterize a stochastic, closed-loop model that simulates the population dynamics of each stock, the fishery that harvests the stock aggregate, and the management framework used to establish mixed-stock exploitation rates. We find that while median aggregate abundance and catch through time were relatively insensitive to greater aggregate variability, catch stability and performance metrics associated with achieving management targets generally declined as component variability and synchrony increased. A notable exception we observed is that harvest control means that scale exploitation rates based on aggregate abundance may be more effective as synchrony increases. Reductions in productivity led to broad declines in performance, but also moderated the impacts of component variability and synchrony on the proportion of component stocks above management targets and catch stability. Our results suggest that even precautionary management strategies that account for declines in productivity may underestimate risk, particularly to socioeconomic objectives, if they fail to consider changes in aggregate variability. Adequately incorporating changes in portfolio effect strength may be particularly relevant when developing recovery strategies that are robust to climate change, which is likely to increase synchrony and component variability.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".