Durability of environment-recruitment relationships in aquatic ecosystems: insights from long-term monitoring in a highly modified estuary and implications for management
Bibliographic record
Abstract
Abstract The environment can strongly influence the survival of aquatic organisms and their resulting dynamics. Our understanding of these relationships, typically based on correlations, underpins many contemporary resource management decisions and conservation actions. However, such relationships can break down over time as ecosystems evolve. Even when durable, they may not be very useful for management if they exhibit high variability, context dependency, or non-stationarity. Here, we systematically review the literature to identify trends across environment-recruitment relationships for aquatic taxa from California’s San Francisco Bay and Sacramento-San Joaquin Delta Estuary. This is one of the most heavily modified aquatic ecosystems in North America, and home to numerous species of concern whose relationships with the environment inform regulatory actions and constraints. We retested 23 of these relationships spanning 9 species using data that have accumulated in the years since they were first published (9-40 additional years) to determine whether they persisted. Most relationships were robust (i.e., same or stronger in magnitude) to the addition of new data, but the ability to predict how a species will respond to environmental change did not generally improve with more data. Instead, prediction error generally increased over time and in some cases very quickly, suggesting a rapid regime shift. Our results suggest that more data alone will not necessarily improve the ability of these relationships to inform decision making. We conclude by synthesizing emerging insights from the literature on best practices for the analysis, use, and refinement of environment-recruitment relationships to inform decision making in dynamic ecosystems.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".