Modeling climate change impacts on tidal marsh birds: Restoration and conservation planning in the face of uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
The large uncertainty surrounding the future effects of sea‐level rise and other aspects of climate change on tidal marsh ecosystems exacerbates the difficulty in planning effective conservation and restoration actions. We addressed these difficulties in the context of large‐scale wetland restoration activities underway in the San Francisco Estuary (Suisun, San Pablo and San Francisco Bays). We used a boosted regression tree approach to project the future distribution and abundance of five marsh bird species (through 2110) in response to changes in habitat availability and suitability as a result of projected sea‐level rise, salinity, and sediment availability in the Estuary. To bracket the uncertainty, we considered four future scenarios based on two sediment availability scenarios (high or low), which varied regionally, and two rates of sea‐level rise (0.52 or 1.65 m/100 yr). We evaluated three approaches for using model results to inform the selection of potential restoration projects: (1) Use current conditions only to prioritize restoration. (2) Use a single future scenario (among the four referred to above) in combination with current conditions to select priority restoration projects. (3) Combine current conditions with all four future scenarios, while incorporating uncertainty among future scenarios into the selection of restoration projects. We found that simply using current conditions resulted in the poorest performing restoration projects selected in terms of providing habitat for tidal marsh birds in light of possible future scenarios. The most robust method for selecting restoration projects, the “combined” strategy, used projections from all future scenarios with a discounting of areas with high levels of variability among future scenarios. We show that uncertainty about future conditions can be incorporated in site prioritization algorithms and should motivate the selection of adaptation measures that are robust to uncertain future conditions. These results and data have been made available via an interactive decision support tool at www.prbo.org/sfbayslr .
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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 | 0.000 |
| 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 it