Model systems to elucidate minimum requirements for protected areas networks
Bibliographic record
Abstract
In conservation biology there have been varying answers to the question of "How much land to protect?" Simulation models using decision-support software such as Marxan show that the answer is sensitive to target type and amount, and issues of scale. We used a novel model system for landscape ecology to test empirically whether the minimum conservation requirements to represent all species at least once are consistent across replicate landscapes, and if not, whether these minimum conservation requirements are linked to biodiversity patterns. Our model system of replicated microcosms could be scaled to larger systems once patterns and mechanisms are better understood. We found that the minimum representation requirements for lichen species along the microlandscapes of tree trunks were remarkably consistent (4-6 planning units) across 24 balsam fir trees in a single stand, as well as for 21 more widely dispersed fir and yellow birch trees. Variation in minimum number of planning units required correlated positively with gamma diversity. Our results demonstrate that model landscapes are useful to determine whether minimum representation requirements are consistent across different landscapes, as well as what factors (life history, diversity patterns, dispersal strategies) affect variation in these conservation requirements. This system holds promise for further investigation into factors that should be considered when developing conservation designs, thus yielding scientifically-defensible requirements that can be applied more broadly.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".