Climate Change Implications for Tidal Marshes and Food Web Linkages to Estuarine and Coastal Nekton
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change is altering naturally fluctuating environmental conditions in coastal and estuarine ecosystems across the globe. Departures from long-term averages and ranges of environmental variables are increasingly being observed as directional changes [e.g., rising sea levels, sea surface temperatures (SST)] and less predictable periodic cycles (e.g., Atlantic or Pacific decadal oscillations) and extremes (e.g., coastal flooding, marine heatwaves). Quantifying the short- and long-term impacts of climate change on tidal marsh seascape structure and function for nekton is a critical step toward fisheries conservation and management. The multiple stressor framework provides a promising approach for advancing integrative, cross-disciplinary research on tidal marshes and food web dynamics. It can be used to quantify climate change effects on and interactions between coastal oceans (e.g., SST, ocean currents, waves) and watersheds (e.g., precipitation, river flows), tidal marsh geomorphology (e.g., vegetation structure, elevation capital, sedimentation), and estuarine and coastal nekton (e.g., species distributions, life history adaptations, predator-prey dynamics). However, disentangling the cumulative impacts of multiple interacting stressors on tidal marshes, whether the effects are additive, synergistic, or antagonistic, and the time scales at which they occur, poses a significant research challenge. This perspective highlights the key physical and ecological processes affecting tidal marshes, with an emphasis on the trophic linkages between marsh production and estuarine and coastal nekton, recommended for consideration in future climate change studies. Such studies are urgently needed to understand climate change effects on tidal marshes now and into the future.
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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 | 0.001 |
| 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 it