Diagnosing restoration trajectories using demographic modeling and modern coexistence theory
Bibliographic record
Abstract
Restoration success is often measured by comparing target species abundance between restored and reference populations. Abundance may poorly predict long-term success, however, because seed addition may initially inflate restored population abundances, and reference population abundances may fluctuate with environmental variation. A demographic approach, informed by modern coexistence theory, may allow for more accurate diagnosis of restoration trajectories. We modeled population dynamics of an endangered plant ( Lasthenia conjugens ) in restored vernal pools and compared them to reference populations over 18 years (2000-2017). Model estimates of L. conjugens growth rates were better predictors of long-term trends than observed abundances. Although populations fluctuated in reference pools, annual rainfall variability acted as a stabilizing factor for L. conjugens . In restored pools however, invasive grasses and associated litter accumulation overrode the benefits of environmental variability. Our approach improves assessment of restoration outcomes and indicates when management actions, such as grass removal, will improve future trajectories.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".