Management thresholds shift under the influence of multiple stressors: Eelgrass meadows as a case study
Bibliographic record
Abstract
Abstract As human activities increase in intensity and extent, ecosystems face growing threats from multiple stressors. Successful management requires identifying measurable targets, which is challenging because of data limitations, nonlinear ecosystem responses, and potentially shifting targets under multiple stressors. To identify critical management values and determine whether these values shift in the presence of multiple stressors, we use eelgrass ( Zostera marina ) meadows as a model system. We reviewed 20 studies that measured the effects of light and temperature on eelgrass performance, providing 109 unique study–site–treatment combinations. We modeled the interactive effect of temperature and light on eelgrass population growth rate (i.e., lateral shoot production rates) using a hierarchical generalized additive model and predicted population growth rates across a range of light levels and temperatures. We found that two critical performance metrics of population growth, zero‐growth and maximum growth rates, shifted across a gradient of light and temperature, suggesting that fixed management targets linked to population growth rates might be unsuitable for managing meadows under multiple stressors. Our approach bridges the gap between data from laboratory and field studies and could be developed into an interactive management tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".