Rarest of the rare: advances in combining evolutionary distinctiveness and scarcity to inform conservation at biogeographical scales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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