The dimensions of evolutionary potential in biological conservation
Bibliographic record
Abstract
Abstract It is now well admitted by ecologists that the conservation of biodiversity should imply preserving the evolutionary processes that will permit its adaptation to ongoing and future environmental changes. This is attested by the ever‐growing reference to the conservation of evolutionary potential in the scientific literature. The impression that one may have when reading papers is that conserving evolutionary potential can only be a good thing, whatever biological system is under scrutiny. However, different objectives, such as maintaining species richness versus ecosystem services, may express different, when not conflicting, underlying values attributed to biodiversity. For instance, biodiversity can be intrinsically valued, as worth it to be conserved per se, or it can be conserved as a means for human flourishing. Consequently, both the concept of evolutionary potential and the prescriptions derived from the commitment to conserve it remain problematic, due to a lack of explicit mention of the norms underlying different conservation visions. Here, we contend that those who advocate for the conservation of evolutionary potential should position their conception along four dimensions: what vehicles instantiate the evolutionary potential relevant to their normative commitment; what temporality is involved; how measurable evolutionary potential is, and what degree of human influence is tolerated. We need to address these dimensions if we are to determine why and when the maintenance of evolutionary potential is an appropriate target for the conservation of biodiversity.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.050 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".