Designing Landscapes for Performance Based on Emerging Principles in Landscape Ecology
Bibliographic record
Abstract
We have proposed a framework for transforming landscapes to improve performance by integrating ecological principles into landscape design.This effort would focus on the development of multifunctional landscapes, guided by the rapidly growing knowledge base of ecosystem services provided by landscape features.Although the conventional approach to landscape ecology is based on a model that assumes poor ecological quality in the human-dominated matrix, a review of recent literature reveals important opportunities to improve the quality of the landscape matrix by increasing spatial heterogeneity through the addition of seminatural landscape elements designed to provide multiple ecosystem services.Taken alone, these individual elements might not appear to have a large impact on the environment, but when considered together within the entire landscape, the contribution could be significant, particularly when these elements are intentionally designed to improve landscape performance.Previous attention has focused on the value of large patches of native vegetation for conservation efforts.These efforts have included preserving those areas that still remain, restoring those that once existed, and providing connectivity between them.But great opportunities exist to improve the quality of the matrix by designing multifunctional elements throughout the landscape.Through a synthesis of knowledge in landscape architecture and landscape ecology, we have demonstrated some important applications of the landscape performance framework in urban and agricultural settings.Based on a review of the literature, we have suggested several methods of evaluating and monitoring landscape performance to determine the relative success of a designed landscape.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".