Pathways for improving the consideration of ecological connectivity in environmental assessment: lessons from five case studies
Bibliographic record
Abstract
Case studies can highlight opportunities for mainstreaming connectivity into environmental assessment (EA) and reveal relevant conditions for success or failure. We examined five cases from Canada, Spain, Sweden, and the UK to address three questions: (1) What are major challenges? (2) What are relevant opportunities and lessons learnt? (3) What research directions should be promoted? We identified 15 challenges and 19 lessons that can help improve connectivity consideration. Common challenges include i) late consideration; ii) lack of resources; iii) lack of explicit requirements; iv) lack of guidance; v) limited recognition of the importance of connectivity; and vi) absence of a landscape-scale perspective. Lessons learnt include the need for rooting connectivity assessments in scientific knowledge and for considering multiple scales of analysis. The findings revealed multiple pathways that can lead to inclusion of connectivity, such as the involvement of knowledgeable EA practitioners, and governments providing a supportive framework. The findings can be applied to advance connectivity assessments in EA, emphasizing the need for guidance and the role of cumulative effects assessment and strategic environmental assessment.
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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 | 0.045 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".