Biodiversity conservation requires integration of species-centric and process-based strategies
Bibliographic record
Abstract
Conservation science and policy are geared primarily toward the preservation of species and habitats, with priority often given to the rarest, most vulnerable or most charismatic forms. This pattern-based approach has broad appeal and offers a pragmatic short-cut for targeting conservation action. However, the long-term efficacy of species and landscape conservation programs remains highly uncertain, amid growing evidence that sustainable conservation action requires an increased emphasis on preserving ecological and evolutionary processes. This reframing of conservation goals was first proposed 50 y ago, but the concept has struggled to gain traction, particularly in terms of translation into policy. Nonetheless, recent events have shifted the narrative, with multiple interlinked global challenges-including biological invasions, food security, disease, and climate change-putting ecological processes firmly back on the agenda. Concurrently, conservation finance is changing rapidly, driven in part by the 2022 Kunming-Montreal Global Biodiversity Framework, which prioritized actions to enhance and restore ecosystem stability, connectivity, and resilience. These ecosystem properties are fundamentally process-driven and appear to create an operational gulf between current conservation practice and the targets of international agreements. We describe how new approaches can be used to close this gap by redirecting conservation attention toward processes at the heart of ecosystem function, including adaptation, gene flow, dispersal, and trophic interactions. Wider adoption of these approaches is urgently needed to forge a deeper connection between conservation practice and policy targets, thereby ensuring that ongoing investment in biodiversity conservation goes beyond damage limitation and instead leaves a lasting legacy of resilient ecosystems.
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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 | 0.028 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".