Habitat connectivity and island biogeography: A call for community-engaged scholarship to address isolated parks and protected areas
Bibliographic record
Abstract
Using the theory of island biogeography as a framework, we seek to determine the potential impact of the lack of connectivity between parks and protected areas on large-scale conservation efforts. We analyze lessons learned from the current Yellowstone to Yukon (Y2Y) initiative and develop recommendations to improve connectivity while incorporating the motivations, needs, and emotions of stakeholder groups. We strongly encourage ecologists, geographers, biologists, and other academics and activists to partake wholly and enthusiastically in community-engaged scholarship through outreach, capacity building, and social capital building through the proven frameworks of consensus-based and structured decisionmaking. Further, we argue that large-scale conservation initiatives may greatly benefit from an approach focused on small, more tangible actions when working toward a larger goal. As human populations and urban–wildland interfaces continue to grow rapidly, former models of park and protected area development become increasingly ineffective. We must adopt new strategies, such as those listed here, in order to increase landscape connectivity and provide effective conservation for all species. [This is a paper from “Systemic Threats to Parks & Protected Areas,” the 2020 George Wright Society Student Summit.]
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".