Rarest of the rare: advances in combining evolutionary distinctiveness and scarcity to inform conservation at biogeographical scales
Bibliographic record
Abstract
Abstract Aim In an era of global habitat loss and species extinction, conservation biology is increasingly becoming a science of triage. A key approach has been the designation of global biodiversity hotspots – areas of high species richness and endemism – prioritizing regions that are disproportionately valuable. However, traditional hotspot approaches leave absent information on species evolutionary histories. We argue that prioritizing the preservation of evolutionary diversity is one way to maximize genotypic and functional diversity, providing ecosystems with the greatest number of options for dealing with an uncertain future. Location Global. Methods We review methods for encapsulating phylogenetic diversity and distinctiveness and provide an illustration of how phylogenetic metrics can be extended to include data on geographical rarity and inform conservation prioritization at biogeographic scales. Results Abundance‐weighted metrics of evolutionary diversity can be used to simultaneously prioritize populations, species, habitats and biogeographical regions. Main conclusion Policy makers need to know where scarce conservation funds should be focused to maximize gains and minimize the loss of biological diversity. By incorporating these evolutionary diversity metrics into prioritization schemes, managers can better quantify the valuation of different regions based on evolutionary information.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".