The relative performance of umbrella species for biodiversity conservation in island archipelagos of the Great Lakes, North America
Bibliographic record
Abstract
Managers often face the dilemma of planning reserve networks with limited data on species' distributions; “umbrella species,” as surrogates for other co-occurring taxa, were thus proposed. Here, the relative efficiencies of “target species” representation in reserves selected using “single-species umbrellas” and “umbrella species groups” are compared, both relative to each other and to target species representation in randomly selected reserve areas. Distribution data for vertebrates and plants on islands of six Great Lakes basin archipelagos were analyzed. Reserves selected using “umbrella groups” contained more species than did those selected using “single-species umbrellas.” Random selection constrained to the same total area occupied by umbrellas typically performed as well as umbrellas of any type. Reserve systems selected at random but constrained to the same number of islands occupied by umbrellas, however, contained lower proportions of target species than did reserve systems selected using umbrellas. Where data are limited, managers may be consoled by the result that random reserve selection appears to perform at least as well as any of the traditional applications of “umbrella species.”
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".