Sea change under climate change: case studies in rare plant conservation from the dynamic San Francisco Estuary
Bibliographic record
Abstract
We present case studies supporting management of two rare plant species in tidal wetlands of the San Francisco Estuary. We used empirical demographic analyses to identify factors to enhance population establishment and survival of Chloropyron molle subsp. molle (Orobancaceae), an annual hemiparasite, and to compare reintroduced with natural populations. Twelve years after outplanting, the reintroduced population persists but is in decline; impediments to success include the lack of adaptive management response to weed invasions and muted variance in hydrology. Transplantation of Lilaeopsis masonii (Apiaceae), a rhizomatous perennial herb, failed to meet success criteria for mitigation at local project scale, but dispersal and establishment of metapopulation patches indicated persistence at the landscape scale. This species has been found to be genetically indistinct from a widespread congener, and has few threats to persistence so long as suitable habitat is present. These two examples demonstrate the need for integrated conservation management strategies that prioritize habitat connectivity and maintain physical processes to support dispersal in response to sea level rise. For the hemiparasite, assisted colonization may sustain populations threatened by sea level rise, but only if a strong commitment to effective stewardship is realized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".