Reserve design to optimize the long‐term persistence of multiple species
Bibliographic record
Abstract
Protected areas form the cornerstone of global efforts to conserve biodiversity. Most current methods for designing reserve networks focus on maximizing the representation of species, but with no assurance that those species will persist in the protected landscapes into the future. We present a new strategy for reserve design that combines metapopulation theory with spatial conservation prioritization to estimate conservation solutions that minimize extinction risk across numerous species simultaneously. Our framework optimizes the spatial configuration of reserves to maximize metapopulation persistence for an entire assemblage of species by accounting for both species representation and landscape connectivity. As a case study, we design a reserve network for 114 terrestrial mammal species in Indonesian New Guinea. Our approach builds on Marxan, the flagship representation-based reserve design tool, improving estimated persistence (metapopulation capacity) by an average of 4.6-fold across species, without increasing the socioeconomic cost. We suggest that enhancing species persistence, rather than protecting arbitrary proportions of species' ranges, should be the ultimate objective of conservation planning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".