Landscape Analysis in Environmental Impact Assessment: Is there Potential to Improve Biodiversity Conservation through Better-Informed Decisions?
Bibliographic record
Abstract
Biodiversity is in severe decline globally, attributed in a large part to anthropogenic land use change.The conservation literature refers to the landscape scale as important in mediating biodiversity.However, environmental impact assessment (EIA), a prevalent tool to inform decision-making with respect to ecological considerations such as biodiversity impacts, rarely takes a landscape perspective.Decisions are often made for individual projects, at local scales, with little attention paid to landscape contexts.The cumulative impact of project-by-project decision-making all too often results in alteration of ecological networks in the landscape with associated losses in biodiversity.A disconnect is apparent between scales of analysis for biodiversity conservation and those used for impact assessment.Landscape ecology studies landscape patterns and processes at a range of scales and has potential to bridge this disconnect.This thesis examines the potential to improve biodiversity conservation by better incorporating landscape ecology-based analysis into project EIA.The mixed-methods research follows three lines: (1) identifying gaps between the science of landscape ecology and the practice of EIA, (2) examining the challenges faced by EIA practitioners when considering broader-scale analysis in EIA and associated opportunities for overcoming them, and ( 3) testing an accessible approach to landscape analysis that incorporates a scenario-based simulation model of cumulative project decision-making.Research was focused on Ontario, Canada, and its multijurisdictional EIA regime.iii Results revealed gaps in how landscape context was considered in EIA, such as the ability of the whole landscape to support species movement and dispersal, and in comparing project-induced land use change to landscape-based ecological targets and thresholds.Quantitative and spatial analyses were infrequently used to assess landscape composition and configuration.Challenges exacerbating these gaps are both policy-and science-based.Weak policy and guidance for broader-scale analysis and a lack of multilevel policy support undermine practitioners' ability to incorporate landscape analysis into EIA.Better multi-jurisdictional data and data management systems are recommended, as well as increasing knowledge of ecological thresholds within the science-practitioner communities.If these challenges can be overcome, the modelling exercise demonstrated that incorporating even simple landscape considerations in projectbased decision-making can have a positive effect on biodiversity indicators.Firstly, I would like to express my sincere gratitude to my supervisors, Dr. Scott Mitchell and Dr. Mike Brklacich, for their unwavering support of my PhD study, and for guiding me in my research with patience, motivation, knowledge and understanding.They never failed to provide me with opportunities to succeed and to grow
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".