Integrating functional and phylogenetic dimensions of biodiversity into ecological restoration
Bibliographic record
Abstract
Functional and phylogenetic diversity are central to most ecological theories applied to ecological restoration. However, because functional and phylogenetic diversity are rarely considered together, the field lacks a universal lens through which to improve restoration science and practice. Here, we demonstrate how simultaneously maximizing functional and phylogenetic diversity in restoration can lead to rapid generation of knowledge about community assembly, the safeguarding of unique evolutionary lineages and functionally distinct species, and the reestablishment of stable and resilient ecosystems. To demonstrate the utility of this approach, we present a species selection framework showing how thoughtful selection of even a limited number of species can result in high levels of functional and phylogenetic diversity in restoration projects. Increasing availability of data on functional traits, phylogenies, and restoration outcomes now enables the explicit incorporation of multiple dimensions of diversity into restoration science and offers new opportunities for integrating ecological theory and restoration practice.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".