Diversity patterns and the design of protected areas in Canada
Bibliographic record
Abstract
Protected areas (i.e., parks and nature reserves) are one of the more commonly applied strategies for conserving biodiversity. However, research shows that many protected areas are located "by default" in areas of little or no economic value. As a result, many habitats and ecosystems are either underrepresented or not represented at all in protected areas. Both the academic literature and recent agency practices focus on the need to establish new protected areas that are representative of species diversity and natural features of ecoregions. The literature describes a variety of techniques for selecting sets of representative protected areas. These techniques are designed to maximize representation and efficiency. Efficiency in terms of the amount of land needed to set aside as protected is desirable, as it minimizes the cost of land acquisition, and maximizes the amount of land left over for other uses. However, very little of the work on representative protected areas design has addressed the issue of species persistence, that is, whether protected areas will contain representative assemblages of species over the long term. In this thesis, I build on past work by developing minimum representative sets of protected areas (that are simultaneously designed to meet minimum criteria to allow for species persistence) for disturbance-sensitive mammals in ecologically-bounded regions in Canada. The number of sites required to represent different regions of Canada varies, and thus I also examine whether diversity patterns (i.e., how heterogeneous a region is in terms of species composition) and the size of the target region are significantly related to the minimum number of protected areas needed. The results suggest that both heterogeneity and size of target regions may influence the number of protected areas needed to represent all species at least once. In addition, I examine how well existing protected areas are representing mammals, and find that, in most parts of the country, existing protected areas are not part of an optimal solution set for representative protected areas. The results of this thesis, then, may help protected areas managers identify priority areas for establishing new protected areas, or enlarging existing ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".