Landscape Analysis in Environmental Impact Assessment: Is there Potential to Improve Biodiversity Conservation through Better-Informed Decisions?
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».