Beta diversity and nature reserve system design a case study from the Yukon, Canada
Bibliographic record
Abstract
Design of protected areas has focused on setting targets for representation of biodiversity, but \noften these targets do not include prescriptions as to how large protected areas should be or where they \nshould be located. Principles of island biogeography theory have been applied with some success, but they \nhave limitations. The so-called SLOSS (single large or several small reserves) debate hinged on applications \nof island biogeography theory to protected areas but was resolved only to the point that parties agreed there \nmight be different approaches in different situations. Although proponents on both sides of the SLOSS debate \ngenerally agree that replicating protected areas is desirable, it is difficult to determine how to replicate reserves \nin terms of number and spatial arrangement. More important, many targets for representation often do not \naddress issues of species persistence. Here, we used a geographic information system in a study of disturbancesensitive \nmammals of the Yukon Territory, Canada, to design a protected-areas network that maintains a \nhistorical assemblage of species goals for component ecoregions. We simultaneously determined patterns of \ndiversity as Whittaker’s beta and compositional turnover and examined how these two measures can give \nfurther insights into reserve location and spatial arrangement. Both regional heterogeneity and compositional \nturnover between nonadjacent sites were significant predictors of the number of protected areas necessary \nto represent mammals within each ecoregion. Thus, protected-area planners can use diversity measures to \nidentify number and spacing of protected areas within ecologically bounded regions.
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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 | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".