Tracking and forecasting community responses to climate perturbations in the California Current Ecosystem
Bibliographic record
Abstract
Ocean ecosystems are vulnerable to climate-driven perturbations, which are increasing in frequency and can have profound effects on marine social-ecological systems. Thus, there is an urgency to develop tools that can detect the response of ecosystem components to these perturbations as early as possible. We used Bayesian Dynamic Factor Analysis (DFA) to develop a community state indicator for the California Current Ecosystem (CCE) to track the system’s response to climate perturbations, and to forecast future changes in community state. Our key objectives were to (1) summarize environmental and biological variability in the southern and central regions of the CCE during a recent and unprecedented marine heatwave in the northeast Pacific Ocean (2014–2016) and compare these patterns to past variability, (2) examine whether there is evidence of a shift in the community to a new state in response to the heatwave, (3) identify relationships between community variability and climate variables; and (4) test our ability to create one-year ahead forecasts of individual species responses and the broader community response based on ocean conditions. Our analysis detected a clear community response to the marine heatwave, although it did not exceed normal variability over the past six decades (1951–2017), and we did not find evidence of a shift to a new community state. We found that nitrate flux through the base of the mixed layer exhibited the strongest relationship with species and community-level responses. Furthermore, we demonstrated skill in creating forecasts of species responses and community state based on estimates of nitrate flux. Our indicator and forecasts of community state show promise as tools for informing ecosystem-based and climate-ready fisheries management in the CCE. Our modeling framework is also widely applicable to other ecosystems where scientists and managers are faced with the challenge of managing and protecting living marine resources in a rapidly changing climate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".