Conservation of Mammals in Eastern North American Wildlife Reserves: How Small Is Too Small?
Bibliographic record
Abstract
Abstract: A common objective of methods of systematic reserve selection has been to maximize conservation benefits—frequently current species richness—while reducing the costs of acquiring and maintaining reserves. But the probability that a reserve will lose species in the future is frequently not known because the minimum area requirements for most species have not been estimated empirically. For reserves within the Alleghenian‐Illinoian mammal province of eastern North America, we empirically estimated the minimum area requirement of terrestrial mammals such that reserves should not lose species because of insularization. We compared this estimate to the actual size of 2355 reserves and reserve assemblages within the mammal province. The estimated minimum area requirement was 5037 km 2 (95% CI: 2700–13,296 km 2 ). Fourteen reserves and reserve assemblages were> 2700 km 2 , 9 were> 5037 km 2 , and 3 were> 13,296 km 2 . These 14 reserves accounted for 73% of the total area of reserves and 10% of the total area of the mammal province. Few reserves appear large enough to avoid loss of some mammal species without the additional cost of active management of habitat or populations. Immigration corridors and buffer zones that combine small reserves into assemblages totaling at least 2700 km 2 may be the most efficient means of conserving mammals in these reserves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".