Evolution in biodiversity policy – current gaps and future needs
Bibliographic record
Abstract
The intensity and speed of human alterations to the planet's ecosystems are yielding our static, ahistorical view of biodiversity obsolete. Human actions frequently trigger fast evolutionary responses, affect extant genetic variation and result in the establishment of new communities and co-evolutionary networks for which we lack past analogues. Contemporary evolution interplays with ecological changes to determine the response of organisms and ecosystems to anthropogenic pressures. Examples on wild species include responses to harvest (e.g. fisheries, hunting, angling), habitat loss and fragmentation (e.g. genetic effects of isolation), biotic exchange (e.g. evolutionary responses to control measures), climate change (e.g. local adaptation and its interplay with dispersal processes) and the responses of endangered species to conservation measures. A review of international and EU biodiversity policies showed numerous opportunities for the integration of evolutionary knowledge, with the realistic prospect of improving their efficacy. Such opportunities should be extended to other sectoral policies of direct relevance for biodiversity - notably nature conservation, fisheries, agriculture, water resources, spatial planning and climate change. These avenues for improvement are, however, challenged by the low level of enforcement of biodiversity policies, linked to the nonbinding nature of most biodiversity-policy documents, and the decreasing representation of biodiversity in EU's research policy.
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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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".