Treatment of ecological connectivity in environmental assessment: A global survey of current practices and common issues
Bibliographic record
Abstract
Ecological connectivity should be an important consideration in environmental assessment (EA). How often and how thoroughly the analysis of ecological connectivity is integrated in the EA process is, however, unknown. We surveyed EA actors and stakeholders regarding their perceptions of, and experiences with, connectivity analysis in the context of EA. 134 practitioners, regulators, consultants, researchers, and interest groups from all inhabited continents participated. Over 72% of respondents stated that ecological connectivity should always be considered; however, it is often considered too late in the EA process, at a scale of analysis often unsuitable for capturing landscape-scale effects, and relying on overly simplistic metrics or qualitative approaches. This disparity between the availability of a range of quantitative tools and the poor consideration of connectivity in EA raises major concerns about current practice and the feasibility of connectivity analysis in project-based EA. Connectivity consideration will need to be required explicitly and supported by best-practice guidance to address the conditions that should trigger a connectivity analysis, the required types of approaches, and the kind of information required to inform decision-making.
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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 | 0.056 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".